Telemedicine Chapter 10: Telemedicine and High-Risk Falls
Baig, MM et al (2020) [Systematic Review] A Systematic Review of Wearable Sensors and IoT-Based Monitoring Applications for Older Adults -A Focus on Ageing Population and Independent Living1
This review aims to present current advancements in wearable technologies and IoT-based applications to support independent living. The secondary aim was to investigate the barriers and challenges of wearable sensors and Internet-of-Things (IoT) monitoring solutions for older adults. For this work, we considered falls and activity of daily life (ADLs)for the ageing population (older adults). A total of 327 articles were screened, and 14 articles were selected for this review. This review considered recent studies published between 2015 and 2019. The research articles were selected based on the inclusion and exclusion criteria, and studies that support or present a vision to provide advancement to the current space of ADLs, independent living and supporting the ageing population. Most studies focused on the system aspects of wearable sensors and IoT monitoring solutions including advanced sensors, wireless data collection, communication platform and usability. Moderate to low usability/ user-friendly approach is reported in most of the studies. Other issues found were inaccurate sensors, battery/ power issues, restricting the users within the monitoring area/ space and lack of interoperability. The advancement of wearable technology and the possibilities of using advanced IoT technology to assist older adults with their ADLs and independent living is the subject of many recent research and investigation.
Zhong, R, Rau, PP (2020) [Systematic Review] Are Cost-Effective Technologies Feasible to Measure Gait in Older Adults? A Systematic Review of Evidence-Based Literature2
Background:Unrestricted by time and place, innovative technologies seem to provide cost-effective solutions for gait assessment in older adults. Objective: The objective of this study is to provide an overview of gait assessment for older adults by investigating critical gait characteristics of older adults, discussing advantages and disadvantages of the current gait assessment technologies, as well as device applicability. Methods:The Preferred Reporting Item for Systematic Reviews and Meta Analysis (PRISMA) guidelines were followed during the review. Inclusion criteria were: 1. sample consisting of adults older than 60 years; 2. qualitative, quantitative, or mixed-method researches using one or more specific gait assessment technologies; and 3. publication in English between 2000 and 2018. Results: In total, twenty-one studies were included. Gait speed, stride length, frequency, acceleration root mean square, step-to-step consistency, auto-correlation, harmonic ratio were reported in the existing literatures to be associated with falls. The enrolled studies address the use of pedometer, wearable accelerometer-based devices, Kinect, Nintendo Wii Balance Board as cost-effective gait assessment technologies. Conclusions:Gait parameters and assessment approaches for older adults are diverse. Cost-effective technologies such as a wearable accelerometer-based device, Kinect, and the Nintendo Wii Balance Board provide potential alternatives for gait assessment with acceptable validity and reliability compared with sophisticated devices. The popularity and development of cost-effective devices have made large-scale data collection for gait assessment possible in the daily environment. Further study could involve older adults and their family members/caregivers in use of these technologies to design elderly-friendly products.
Bet, P et al (2019) [Systematic Review] Fall Detection and Fall Risk Assessment in Older Person Using Wearable Sensors: A Systematic Review3
Background: wearable sensors are often used to acquire data for gait analysis as a strategy to study fall events, due to greater availability of acquisition platforms, and advances in computational intelligence. However, there are no review papers addressing the three most common types of applications related to fall using sensors, namely: fall detection, fallers classification and fall risk screening. Objective: To identify the state of art of fall-related events detection in older person using wearable sensors, as well as the main characteristics of the studies in the literature, pointing gaps for future studies.
Methods:A systematic review design was used to search peer-reviewed literature on fall detection and risk in elderly through inertial sensors, published in English, Portuguese, Spanish or French between August 2002 and June 2019. The following questions are investigated: the type of sensors and their sampling rate, the type of signal and data processing employed, the scales and tests used in the study and the type of application.
Results:We identified 608 studies, from which 29 were included. The accelerometer, with sampling rate 50 or 100 Hz, allocated in the waist or lumbar was the most used sensor setting. Methods comparing features or variables extracted from the accelerometry signal are the most common, and fall risk screening the most observed application.
Conclusion:This review identifies the main elements to be addressed in studies on the detection of events related to falls in the elderly and may help in future studies on the subject. However, some aspects are still no reach consensus in the literature such as the size of the sample to be studied, the population under study and how to acquire data for each application.
Pang, I et al (2019) [Systematic Review] Detection of Near Falls Using Wearable Devices: A Systematic Review4
Background and Purpose: Falls among older people are a serious health issue. Remote detection of near falls may provide a new way to identify older people at high risk of falling. This could enable exercise and fall prevention programs to target the types of near falls experienced and the situations that cause near falls before fall-related injuries occur. The purpose of this systematic review was to summarize and critically examine the evidence regarding the detection of near falls slips, trips, stumbles, missteps, incorrect weight transfer, or temporary loss of balance using wearable devices.
Methods: CINAHL, EMBASE, MEDLINE, Compendex, and Inspec were searched to obtain studies that used a wearable device to detect near falls in young and older people with or without a chronic disease and were published in English.
Results: Nine studies met the final inclusion criteria. Wearable sensors used included accelerometers, gyroscopes, and insole force inducers. The waist was the most common location to place a single device. Both high sensitivity (≥85.7%) and specificity (≥90.0%) were reported for near-fall detection during various clinical simulations and improved when multiple devices were worn. Several methodological issues that increased the risk of bias were revealed. Most studies analyzed a single or few near-fall types by younger adults in controlled laboratory environments and did not attempt to distinguish naturally occurring near falls from actual falls or other activities of daily living in older people.
Conclusions: The use of a single lightweight sensor to distinguish between different types of near falls, actual falls, and activities of daily living is a promising low-cost technology and clinical tool for long-term continuous monitoring of older people and clinical populations at risk of falls. However, currently the evidence is limited because studies have largely involved simulated laboratory events in young adults. Future studies should focus on validating near-fall detection in larger cohorts and include data from 1. people at high risk of falling; 2. activities of daily living; 3. both near falls and actual falls; and 4. naturally occurring near falls.
Sun, R, Sosnoff, JJ (2018) [Systematic Review] Novel Sensing Technology in Fall Risk Assessment in Older Adults: A Systematic Review5
Background: Falls are a major health problem for older adults with significant physical and psychological consequences. A first step of successful fall prevention is to identify those at risk of falling. Recent advancement in sensing technology offers the possibility of objective, low-cost and easy-to-implement fall risk assessment. The objective of this systematic review is to assess the current state of sensing technology on providing objective fall risk assessment in older adults.
Methods:A systematic review was conducted in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement (PRISMA).
Results: Twenty-two studies out of 855 articles were systematically identified and included in this review. Pertinent methodological features sensing technique, assessment activities, outcome variables, and fall discrimination/prediction models were extracted from each article. Four major sensing technologies inertial sensors, video/depth camera, pressure sensing platform and laser sensing were reported to provide accurate fall risk diagnostic in older adults. Steady state walking, static/dynamic balance, and functional mobility were used as the assessment activity. A diverse range of diagnostic accuracy across studies (47.9% – 100%) were reported, due to variation in measured kinematic/kinetic parameters and modelling techniques.
Conclusions:A wide range of sensor technologies have been utilized in fall risk assessment in older adults. Overall, these devices have the potential to provide an accurate, inexpensive, and easy-to-implement fall risk assessment. However, the variation in measured parameters, assessment tools, sensor sites, movement tasks, and modelling techniques, precludes a firm conclusion on their ability to predict future falls. Future work is needed to determine a clinical meaningful and easy to interpret fall risk diagnosis utilizing sensing technology. Additionally, the gap between functional evaluation and user experience to technology should be addressed.
Ma, CZ et al (2016) [Systematic Review] Balance Improvement Effects of Biofeedback Systems With State-of-the-Art Wearable Sensors: A Systematic Review6
Falls and fall-induced injuries are major global public health problems. Balance and gait disorders have been the second leading cause of falls. Inertial motion sensors and force sensors have been widely used to monitor both static and dynamic balance performance. Based on the detected performance, instant visual, auditory, electrotactile and vibrotactile biofeedback could be provided to augment the somatosensory input and enhance balance control. This review aims to synthesize the research examining the effect of biofeedback systems, with wearable inertial motion sensors and force sensors, on balance performance. Randomized and non-randomized clinical trials were included in this review. All studies were evaluated based on the methodological quality. Sample characteristics, device design and study characteristics were summarized. Most previous studies suggested that biofeedback devices were effective in enhancing static and dynamic balance in healthy young and older adults, and patients with balance and gait disorders. Attention should be paid to the choice of appropriate types of sensors and biofeedback for different intended purposes. Maximizing the computing capacity of the micro-processer, while minimizing the size of the electronic components, appears to be the future direction of optimizing the devices. Wearable balance-improving devices have their potential of serving as balance aids in daily life, which can be used indoors and outdoors.
Hu, X, Qu, X (2016) [Systematic Review] Pre-impact Fall Detection7
Pre-impact fall detection has been proposed to be an effective fall prevention strategy. In particular, it can help activate on-demand fall injury prevention systems (eg inflatable hip protectors) prior to fall impacts, and thus directly prevent the fall-related physical injuries. This paper gave a systematic review on pre-impact fall detection, and focused on the following aspects of the existing pre-impact fall detection research: fall detection apparatus, fall detection indicators, fall detection algorithms, and types of falls for fall detection evaluation. In addition, the performance of the existing pre-impact fall detection solutions were also reviewed and reported in terms of their sensitivity, specificity, and detection/lead time. This review also summarized the limitations in the existing pre-impact fall detection research, and proposed future research directions in this field.
Randomised Controlled Trials
Barker, A et al (2019) [Randomised Controlled Trial] Evaluation of RESPOND, a Patient-Centred Program to Prevent Falls in Older People Presenting to the Emergency Department With a Fall: A Randomised Controlled Trial8
Background: Falls are a leading reason for older people presenting to the emergency department (ED), and many experience further falls. Little evidence exists to guide secondary prevention in this population. This randomised controlled trial (RCT)investigated whether a 6-month telephone-based patient-centred program-RESPOND-had an effect on falls and fall injuries in older people presenting to the ED after a fall.
Methods and Findings: Community-dwelling people aged 60-90 years presenting to the ED with a fall and planned for discharge home within 72 hours were recruited from two EDs in Australia. Participants were enrolled if they could walk without hands-on assistance, use a telephone, and were free of cognitive impairment (Mini-Mental State Examination > 23). Recruitment occurred between 1 April 2014 and 29 June 2015. Participants were randomised to receive either RESPOND (intervention) or usual care (control). RESPOND comprised 1. home-based risk assessment; 2. 6 months telephone-based education, coaching, goal setting, and support for evidence-based risk factor management; and 3.linkages to existing services. Primary outcomes were falls and fall injuries in the 12-month follow-up. Secondary outcomes included ED presentations, hospital admissions, fractures, death, falls risk, falls efficacy, and quality of life. Assessors blind to group allocation collected outcome data via postal calendars, telephone follow-up, and hospital records. There were 430 people in the primary outcome analysis-217 randomised to RESPOND and 213 to control. The mean age of participants was 73 years; 55% were female. Falls per person-year were 1.15 in the RESPOND group and 1.83 in the control (incidence rate ratio [IRR]0.65 [95% CI 0.43-0.99]; P = 0.042). There was no significant difference in fall injuries (IRR 0.81 [0.51-1.29]; P = 0.374). The rate of fractures was significantly lower in the RESPOND group compared with the control (0.05versus 0.12; IRR 0.37 [95% CI 0.15-0.91]; P = 0.03), but there were no significant differences in other secondary outcomes between groups: ED presentations, hospitalisations or falls risk, falls efficacy, and quality of life. There were two deaths in the RESPOND group and one in the control group. No adverse events or unintended harm were reported. Limitations of this study were the high number of dropouts (n = 93); possible under-reporting of falls, fall injuries, and hospitalisations across both groups; and the relatively small number of fracture events.
Conclusions: In this study, providing a telephone-based, patient-centred falls prevention program reduced falls but not fall injuries, in older people presenting to the ED with a fall. Among secondary outcomes, only fractures reduced. Adopting patient-centred strategies into routine clinical practice for falls prevention could offer an opportunity to improve outcomes and reduce falls in patients attending the ED.
Antos, SA et al (2020) Smartwatches Can Detect Walker and Cane Use in Older Adults9
Background and Objectives: Clinicians commonly prescribe assistive devices such as walkers or canes to reduce older adults’ fall risk. However, older adults may not consistently use their assistive device, and measuring adherence can be challenging due to self-report bias or cognitive deficits. Because walking patterns can change while using an assistive device, we hypothesized that smartphones and smartwatches, combined with machine-learning algorithms, could detect whether an older adult was walking with an assistive device. Research Design and Methods:Older adults at an Adult Day Center (n = 14) wore an Android smartphone and Actigraph smartwatch while completing the six-minute walk, 10-meter walk, and Timed Up and Go tests with and without their assistive device on five separate days. We used accelerometer data from the devices to build machine-learning algorithms to detect whether the participant was walking with or without their assistive device. We tested our algorithms using crossvalidation.
Results: Smartwatch classifiers could accurately detect assistive device use, but smartphone classifiers performed poorly. Customized smartwatch classifiers, which were created specifically for one participant, had greater than 95% classification accuracy for all participants. Non-customized smartwatch classifiers i.e. an off-the-shelf system had greater than 90% accuracy for 10 of the 14 participants. A non-customized system performed better for walker users than cane users. Discussion and Implications:Our approach can leverage data from existing commercial devices to provide a deeper understanding of walker or cane use. This work can inform scalable public health monitoring tools to quantify assistive device adherence and enable proactive fall interventions.
Clemente, J et al (2020) Smart Seismic Sensing for Indoor Fall Detection, Location, and Notification10
This paper presents a novel real-time smart system performing fall detection, location, and notification based on floor vibration data produced by fall downs. Only using floor vibration as the recognition source, the system incorporates a person identification through vibration produced by footsteps to inform who is the fallen person. Our approach operates in a real-time style, which means the system recognizes a fall immediately and can identify a person with only one or two footsteps. A collaborative in network location method is used in which sensors collaborate with each other to recognize the person walking, and more importantly, detect if the person falls down at any moment. We also introduce a voting system among sensor nodes to improve person identification accuracy. Our system is robust to identify fall downs from other possible similar events, such as jumps, door close, and objects fall down. Such a smart system can also be connected to smart commercial devices for emergency notifications. Our approach represents an advance in smart technology for elder people who live alone. Evaluation of the system shows that it is able to detect fall downs with an acceptance rate of 95.14%, distinguishing from other possible events, and it identifies people with one or two steps in a 97.22% [a higher accuracy than other methods that use more footsteps]. The fall down location error is smaller than 0.27 m, which is acceptable compared with the height of a person.
Background:Wearable devices are valuable assessment tools for patient outcomes in contexts such as clinical trials. To be successfully deployed, however, participants must be willing to wear them. Another concern is that usability studies are rarely published, often fail to test devices beyond 24 hours, and need to be repeated frequently to ensure that contemporary devices are assessed.
Objective: This study aimed to compare multiple wearable sensors in a realworld context to establish their usability within an older adult (>50 years) population. Methods: Eight older adults wore seven devices for a minimum of 1 week each: Actigraph GT9x, Actibelt, Actiwatch, Biovotion, Hexoskin, Mc10 Biostamp_RC, and Wavelet. Usability was established through mixed methods using semistructured interviews and three questionnaires, namely, the Intrinsic Motivation Inventory (IMI), the System Usability Scale (SUS), and an acceptability questionnaire. Quantitative data were reported descriptively and qualitative data were analyzed using deductive content analysis. Data were then integrated using triangulation.
Results:Results demonstrated that no device was considered optimal as all scored below average in the SUS (median, IQR; min-max=57.5, 12.5; 47.5- 63.8). Hexoskin was the lowest scored device based on the IMI (3.6; 3.4-4.5), while Biovotion, Actibelt, and Mc10 Biostamp_RC achieved the highest median results on the acceptability questionnaire (3.6 on a 6-point Likert scale). Qualitatively, participants were willing to accept less comfort, less device discretion, and high charging burdens if the devices were perceived as useful, namely through the provision of feedback for the user. Participants agreed that the purpose of use is a key enabler for long-term compliance. These views were particularly noted by those not currently wearing an activity-tracking device. Participants believed that wrist-worn sensors were the most versatile and easy to use, and therefore, the most suitable for longterm use. In particular, Actiwatch and Wavelet stood out for their comfort. The convergence of quantitative and qualitative data was demonstrated in the study.
Conclusions: Based on the results, the following context-specific recommendations can be made: 1. researchers should consider their device selection in relation to both individual and environmental factors, and not simply the primary outcome of the research study; 2. if researchers do not wish their participants to have access to feedback from the devices, then a simple, wrist-worn device that acts as a watch is preferable; 3. if feedback is allowed, then it should be made available to help participants remain engaged; this is likely to apply only to people without cognitive impairments; 4. battery life of 1 week should be considered as a necessary feature to enhance data capture; 5. researchers should consider providing additional information about the purpose of devices to participants to support their continued use.
Tahir, A et al (2020) Hardware/Software Co-design of Fractal Features Based Fall Detection System12
Falls are a leading cause of death in older adults and result in high levels of mortality, morbidity and immobility. Fall Detection Systems (FDS) are imperative for timely medical aid and have been known to reduce death rate by 80%. We propose a novel wearable sensor FDS which exploits fractal dynamics of fall accelerometer signals. Fractal dynamics can be used as an irregularity measure of signals and our work shows that it is a key discriminant for classification of falls from other activities of life. We design, implement and evaluate a hardware feature accelerator for computation of fractal features through multi-level wavelet transform on a reconfigurable embedded System on Chip, Zynq device for evaluating wearable accelerometer sensors. The proposed FDS utilises a hardware/software codesign approach with hardware accelerator for fractal features and software implementation of Linear Discriminant Analysis on an embedded ARM core for high accuracy and energy efficiency. The proposed system achieves 99.38% fall detection accuracy, 7.3× speed-up and 6.53× improvements in power consumption, compared to the software only execution with an overall performance per Watt advantage of 47.6×, while consuming low reconfigurable resources at 28.67%.
Boutellaa, E et al (2019) Covariance Matrix Based Fall Detection From Multiple Wearable Sensors13
Falls are among the critical accidents experienced by elderly people and patients carrying some diseases. Subsequently, the detection and prevention of falls have become a hot research and industrial topic. This is due to the fact that falls are behind numerous irreversible injuries, or even death, and are weighting on the budgets of the health services. Automatic fall detection is one of the proposed solutions which aim at monitoring people who are likely to fall. Such solutions mitigate the fall impact by taking a quick action, eg in case of a fall occurrence, an alert is sent to the hospital. In this paper, we propose a new fall detection system relying on different signals acquired with multiple wearable sensors. Our system makes use of the covariance of the raw signals and the nearest neighbor classifier. Besides feature extraction, we also employ the covariance matrix as a straightforward mean for fusing signals from multiple sensors, to enhance the classification performance. Evaluation on two publicly available fall datasets, namely CogentLabs and DLR, demonstrates that the proposed approach is efficient when exploiting a single sensor as well as when fusing data from multiple sensors. Geodesic metrics are found to provide a higher fall detection accuracy than the Euclidean metric. The best obtained classification accuracies are 92.51% and 98.31% for CogentLabs and DLR datasets, respectively.
Luan-Perejon, F et al (2019) Wearable Fall Detector Using Recurrent Neural Networks14
Falls have become a relevant public health issue due to their high prevalence and negative effects in elderly people. Wearable fall detector devices allow the implementation of continuous and ubiquitous monitoring systems. The effectiveness for analyzing temporal signals with low energy consumption is one of the most relevant characteristics of these devices. Recurrent neural networks (RNNs) have demonstrated a great accuracy in some problems that require analyzing sequential inputs. However, getting appropriate response times in low power microcontrollers remains a difficult task due to their limited hardware resources. This work shows a feasibility study about using RNN-based deep learning models to detect both falls and falls’ risks in real time using accelerometer signals. The effectiveness of four different architectures was analyzed using the SisFall dataset at different frequencies. The resulting models were integrated into two different embedded systems to analyze the execution times and changes in the model effectiveness. Finally, a study of power consumption was carried out. A sensitivity of 88.2% and a specificity of 96.4% was obtained. The simplest models reached inference times lower than 34 ms, which implies the capability to detect fall events in real-time with high energy efficiency. This suggests that RNN models provide an effective method that can be implemented in low power microcontrollers for the creation of autonomous wearable fall detection systems in real-time.
Falls are the primary contributors of accidents in elderly people. An important factor of fall severity is the amount of time that people lie on the ground. To minimize consequences through a short reaction time, the motion sensor AIDE-MOI was developed. AIDE-MOI senses acceleration data and analyzes if an event is a fall. The threshold-based fall detection algorithm was developed using motion data of young subjects collected in a lab setup. The aim of this study was to improve and validate the existing fall detection algorithm. In the two-phase study, twenty subjects (age 86.25 ± 6.66 years) with a high risk of fall (Morse > 65 points) were recruited to record motion data in real-time using the AIDE-MOI sensor. The data collected in the first phase (59 days) was used to optimize the existing algorithm. The optimized second-generation algorithm was evaluated in a second phase (66 days). The data collected in the two phases, which recorded 31 real falls, was splitup into one-minute chunks for labelling as fall or non-fall. The sensitivity and specificity of the threshold-based algorithm improved significantly from 27.3% to 80.0% and 99.9957% (0.43)to 99.9978% (0.17 false alarms per week and subject), respectively.
Avatar-based three-dimensional technology is a new approach to improve physical function in older adults. The aim of this study was to use three-dimensional video gaming technology in virtual reality training to improve postural balance and lower extremity strength in a population of community-dwelling older adults. The experimental group participated in the virtual reality training program for 60 min, twice a week, for 6 weeks. Both experimental and control groups were given three times for falls prevention education at the first, third, and fifth weeks. The experimental group showed significant improvements not only in static and dynamic postural balance but also lower extremity strength (p < .05). Furthermore, the experimental group was improved to overall parameters compared with the control group (p < .05). Therefore, three-dimensional video gaming technology might be beneficial for improving postural balance and lower extremity strength in community-dwelling older adults.
Unintentional falls are a major public health concern for many communities, especially with aging populations. There are various approaches used to classify human activities for fall detection. Related studies have employed wearable, non-invasive sensors, video cameras and depth sensor-based approaches to develop such monitoring systems. The proposed approach in this study uses a depth sensor and employs a unique procedure which identifies the fall risk levels to adapt the algorithm for different people with their physical strength to withstand falls. The inclusion of the fall risk level identification, further enhanced and improved the accuracy of the fall detection. The experimental results showed promising performance in adapting the algorithm for people with different fall risk levels for fall detection.
Reyes, A et al (2018) A Standardized Review of Smartphone Applications to Promote Balance for Older Adults18
Background: Balance is one of the risk factors for falls in older adults. The use of smartphone applications related to health is increasing and, while there is potential for apps to be used as a self-managed balance intervention, many healthcare providers are concerned about the content and credibility of mHealth apps overall.
Purpose:This study evaluates the quality of balance promoting apps and identifies strengths and areas of concern to assist healthcare providers in recommending these resources.
Materials and Methods: Balance apps for the general public, offered on the iPhone Operating System (iOS) and Android platforms, were evaluated using the Mobile Application Rating Scale (MARS).
Results: Five iOS apps met the inclusion criteria. The mean scores for each of the domains in MARS were: Engagement (3.32), Information (3.7), Functionality (3.8), and Esthetics (3.8). Overall, one app (UStabilize) received a rating of 4.43 in MARS five-point scale, which was considered good. Other apps in the review demonstrated acceptable quality.
Conclusions: The reviewed balance apps targeted to improve or maintain physical balance were of acceptable quality. Apps address many current issues older adults have to accessing rehabilitation services and, as such, may be particularly useful for this group. Future research should focus on assessing and comparing app efficacy. Development of balance apps for the Android platform is also necessary. Implications for Rehabilitation Given the availability and accessibility of various mHealth apps and the increasing mobile device usage among older adults, mobile apps are a promising avenue for delivering rehabilitation interventions, such as balance training, to older adults. Smartphone apps exist for balance training but overall confidence in health apps within the healthcare community is low and rigorous evaluation is required. A range of apps exist that demonstrate acceptable to good quality and stakeholders should work towards having these apps listed in credible mHealth clearinghouses.
In recent years, the meaning of successful living has moved from extending lifetime to improving the quality of aging, mainly in terms of high cognitive and physical functioning together with avoiding diseases. In healthy elderly, falls represent an alarming accident both in terms of number of events and the consequent decrease in the quality of life. Stability control is a key approach for studying the genesis of falls, for detecting the event and trying to develop methodologies to prevent it. Wearable sensors have proved to be very useful in monitoring and analyzing the stability of subjects. Within this manuscript, a review of the approaches proposed in the literature for fall risk assessment, fall prevention and fall detection in healthy elderly is provided. The review has been carried out by using the most adopted publication databases and by defining a search strategy based on keywords and boolean algebra constructs. The analysis aims at evaluating the state of the art of such kind of monitoring, both in terms of most adopted sensor technologies and of their location on the human body. The review has been extended to both dynamic and static analyses. In order to provide a useful tool for researchers involved in this field, the manuscript also focuses on the tests conducted in the analyzed studies, mainly in terms of characteristics of the population involved and of the tasks used. Finally, the main trends related to sensor typology, sensor location and tasks have been identified.
Sucerquia, A et al (2018) Real-Life/Real-Time Elderly Fall Detection With a Triaxial Accelerometer20
The consequences of a fall on an elderly person can be reduced if the accident is attended by medical personnel within the first hour. Independent elderly people often stay alone for long periods of time, being in more risk if they suffer a fall. The literature offers several approaches for detecting falls with embedded devices or smartphones using a triaxial accelerometer. Most of these approaches have not been tested with the target population or cannot be feasibly implemented in real-life conditions. In this work, we propose a fall detection methodology based on a non-linear classification feature and a Kalman filter with a periodicity detector to reduce the false positive rate. This methodology requires a sampling rate of only 25 Hz; it does not require large computations or memory and it is robust among devices. We tested our approach with the SisFall dataset achieving 99.4% of accuracy. We then validated it with a new round of simulated activities with young adults and an elderly person. Finally, we give the devices to three elderly persons for full-day validations. They continued with their normal life and the devices behaved as expected.
Wang, C et al (2018) A Low-Power Fall Detector Balancing Sensitivity and False Alarm Rate21
Falls in older people are a major challenge to public health. A wearable fall detector can detect falls automatically based on kinematic information of the human body, allowing help to arrive sooner. To date, most studies have focused on the accuracy of an offline algorithm to distinguish real-world or simulated falls from activities of daily living, while neglecting the false alarm rate and battery life of a real device. To address these two important metrics, which significantly influence user compliance, this paper proposes a lowpower fall detector using triaxial accelerometry and barometric pressure sensing. This fall detector minimizes power consumption using both hardware- and firmware-based techniques. Additionally, the fall detection algorithm used in this device is optimized to achieve a balance between sensitivity and false alarm rate, while minimizing the power consumption due to algorithm execution. The fall detector achieved a high sensitivity (91%) with a low false alarm rate (0.1149 alarms per hour), and a commercially-viable battery life.
Falls are a major threat for senior citizens’ independent living. Motion sensor technologies and automatic fall detection systems have emerged as a reliable low-cost solution to this challenge. We develop a hidden Markov model (HMM) based fall detection system to detect falls automatically using a single motion sensor for real-life home monitoring scenarios. We propose a new representation for acceleration signals in HMMs to avoid feature engineering and developed a sensor orientation calibration algorithm to resolve sensor misplacement issues [misplaced sensor location and misaligned sensor orientation]in real-world scenarios. HMM classifiers are trained to detect falls based on acceleration signal data collected from motion sensors. We collect a dataset from experiments of simulated falls and normal activities and acquired a dataset from a real-world fall repository (FARSEEING)to evaluate our system. Our system achieves positive predictive value of 0.981 and sensitivity of 0.992 on the experiment dataset with 200 fall events and 385 normal activities, and positive predictive value of 0.786 and sensitivity of 1.000 on the real-world fall dataset with 22 fall events and 2618 normal activities. Our system’s results significantly outperform benchmark systems, which shows the advantage of our HMM-based fall detection system with sensor orientation calibration. Our fall detection system is able to precisely detect falls in real-life home scenarios with a reasonably low false alarm rate.
Falls are a major cause of injuries and deaths in older adults. Even when no injury occurs, about half of all older adults who fall are unable to get up without assistance. The extended period of lying on the floor often leads to medical complications, including muscle damage, dehydration, anxiety and fear of falling. Wearable sensor systems incorporating accelerometers and/or gyroscopes are designed to prevent long lies by automatically detecting and alerting care providers to the occurrence of a fall. Research groups have reported up to 100% accuracy in detecting falls in experimental settings. However, there is a lack of studies examining accuracy in the real-world setting. In this study, we examined the accuracy of a fall detection system based on real-world fall and non-fall data sets. Five young adults and 19 older adults went about their daily activities while wearing tri-axial accelerometers. Older adults experienced 10 unanticipated falls during the data collection. Approximately 400 hours of activities of daily living were recorded. We employed a machine learning algorithm, Support Vector Machine (SVM) classifier, to identify falls and non-fall events. We found that our system was able to detect 8 out of the 10 falls in older adults using signals from a single accelerometer [waist or sternum]. Furthermore, our system did not report any false alarm during approximately 28.5 hours of recorded data from young adults. However, with older adults, the false positive rate among individuals ranged from 0 to 0.3 false alarms per hour. While our system showed higher fall detection and substantially lower false positive rate than the existing fall detection systems, there is a need for continuous efforts to collect real-world data within the target population to perform fall validation studies for fall detection systems on bigger real world fall and non-fall datasets.
Background: Falls of individuals with dementia are frequent, dangerous, and costly. Early detection and access to the history of a fall is crucial for efficient care and secondary prevention in cognitively impaired individuals. However, most falls remain unwitnessed events. Furthermore, understanding why and how a fall occurred is a challenge. Video capture and secure transmission of real-world falls thus stands as a promising assistive tool. Objective: The objective of this study was to analyze how continuous video monitoring and review of falls of individuals with dementia can support better quality of care.
Methods: A pilot observational study (July-September 2016) was carried out in a Californian memory care facility. Falls were video captured (24×7), thanks to 43 wall-mounted cameras (deployed in all common areas and in 10 out of 40 private bedrooms of consenting residents and families). Video review was provided to facility staff, thanks to a customized mobile device app. The outcome measures were the count of residents’ falls happening in the video-covered areas, the acceptability of video recording, the analysis of video review, and video replay possibilities for care practice.
Results: Over 3 months, 16 falls were video-captured. A drop in fall rate was observed in the last month of the study. Acceptability was good. Video review enabled screening for the severity of falls and fall related injuries. Video replay enabled identifying cognitive-behavioral deficiencies and environmental circumstances contributing to the fall. This allowed for secondary prevention in high-risk multi-faller individuals and for updated facility care policies regarding a safer living environment for all residents.Conclusions: Video monitoring offers high potential to support conventional care in memory care facilities.
Falls and physical deconditioning are two major health problems for older people. Recent advances in remote physiological monitoring provide new opportunities to investigate why walking exercise, with its many health benefits, can both increase and decrease fall rates in older people. In this paper we combine remote wearable device monitoring of daily gait with non-linear multi-dimensional pattern recognition analysis; to disentangle the complex associations between walking, health and fall rates. One week of activities of daily living (ADL) were recorded with a wearable device in 96 independent living older people prior to completing 6 months of exergaming interventions. Using the wearable device data; the quantity, intensity, variability and distribution of daily walking patterns were assessed. At baseline, clinical assessments of health, falls, sensorimotor and physiological fall risks were completed. At 6 months, fall rates, sensorimotor and physiological fall risks were re-assessed. A non-linear multidimensional analysis was conducted to identify risk-groups according to their daily walking patterns. Four distinct risk-groups were identified: The Impaired (93% fallers), Restrained (8% fallers), Active (50% fallers) and Athletic (4% fallers). Walking was strongly associated with multiple health benefits and protective of falls for the top performing Athletic risk-group. However, in the middle of the spectrum, the Active risk-group, who were more active, younger and healthier were 6.25 times more likely to be fallers than their Restrained counterparts. Remote monitoring of daily walking patterns may provide a new way to distinguish Impaired people at risk of falling because of frailty from Active people at risk of falling from greater exposure to situations were falls could occur, but further validation is required. Wearable device risk-profiling could help in developing more personalised interventions for older people seeking the health benefits of walking without increasing their risk of falls.
De Miguel, K et al (2017) Home Camera-Based Fall Detection System for the Elderly26
Falls are the leading cause of injury and death in elderly individuals. Unfortunately, fall detectors are typically based on wearable devices, and the elderly often forget to wear them. In addition, fall detectors based on artificial vision are not yet available on the market. In this paper, we present a new low-cost fall detector for smart homes based on artificial vision algorithms. Our detector combines several algorithms background subtraction, Kalman filtering and optical flow as input to a machine learning algorithm with high detection accuracy. Tests conducted on over 50 different fall videos have shown a detection ratio of greater than 96%.
Jatesiktat, P, Wei Tech, A (2017) An Elderly Fall Detection Using a Wrist-Worn Accelerometer and Barometer27
As the world population is growing toward an aging society, elderly fall becomes a serious problem. Automatic fall detection and alert systems could shorten their waiting time after a fall and mitigate its physical and mental negative consequences. This work proposes a method that integrates a 3-axis accelerometer and a barometer on a wrist-worn device for the fall detection task. The method focuses on the use of noisy signals from a barometer in both pre-processing steps and feature extractions. A use of free falling events to address the lack of training data in a learning process is also explored. An evaluation using simulated falls and various activities shows a high classification performance except for a few false alarms occurring when sitting on the floor from a standing pose.
Medrano, C et al (2017) Combining Novelty Detectors to Improve Accelerometer-Based Fall Detection28
Research on body-worn sensors has shown how they can be used for the detection of falls in the elderly, which is a relevant health problem. However, most systems are trained with simulated falls, which differ from those of the target population. In this paper, we tackle the problem of fall detection using a combination of novelty detectors. A novelty detector can be trained only with activities of daily life (ADL), which are true movements recorded in real life. In addition, they allow adapting the system to new users, by recording new movements and retraining the system. The combination of several detectors and features enhances performance. The proposed approach has been compared with a traditional supervised algorithm, a support vector machine, which is trained with both falls and ADL. The combination of novelty detectors shows better performance in a typical cross-validation test and in an experiment that mimics the effect of personalizing the classifiers. The results indicate that it is possible to build a reliable fall detector based only on ADL.
Minvielle, L et al (2017) Fall Detection Using Smart Floor Sensor and Supervised Learning29
Falls are a major risk for elderly people’s health and independence. Fast and reliable fall detection systems can improve chances of surviving the accident and coping with its physical and psychological consequences. Recent research has come up with various solutions, all suffering from significant drawbacks, one of them being the intrusiveness into patient’s life. This paper proposes a novel fall detection monitoring system based on a sensitive floor sensor made out of a piezoelectric material and a machine learning approach. The detection is done by a combination between a supervised Random Forest and an aggregation of its output over time. The database was made using acquisitions from 28 volunteers simulating falls and other behaviours. Our solution offers the advantages of having a passive sensor (no power supply is needed) and being completely unobtrusive since the sensor comes with the floor. Results are compared with state-of-the-art classification algorithms. On our database, good performance of fall detection was obtained with a True Positive Rate of 94.4% and a False Positive Rate of 2.4%.
Rajagopalan, R et al (2017) Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions30
Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in Internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the stateof-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems.
Demiris, G et al (2016) Older Adults’ Experience With a Novel Fall Detection Device31
Background: Falls are a significant concern for the older adult (OA) population, many of whom are unable to get up following a fall.
Introduction:While many devices exist designed to detect a fall, little work has been conducted to evaluate the usability of such devices. We present a longitudinal usability study of a fall detection (FD) device tested with OAs in real-world settings.
Materials and Methods: OAs were recruited and asked to use a wearable FD device for up to 4 months. Participants were interviewed at baseline and 2 and 4 months and encouraged to provide direct feedback on their experience. Results: In total, 18 OAs participated in the study. Eight completed the 4-month trial. We conducted a total of 38 interviews (16 baseline, 7 midpoint, and 15 final) and logged a total of 78 comments. While participants enjoyed the GPS and automatic detection features of the device, they were unhappy with the volume of false alarms and obtrusiveness of the device. Many also did not see a great need for having the device or were embarrassed by the device.
Discussion: Engineers must work to better develop this technology so that it is accessible to people with hearing loss, limited dexterity, and low vision. Utilizing age-appropriate design techniques will help make such informatics tools more user friendly. Conclusion:We explored the usability of a particular FD device with OAs and provide design recommendations to help future device manufacturers create more age-appropriate devices.
Casilari et al (2015) Analysis of Android Device-Based Solutions for Fall Detection32
Falls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Thus, the investigation on automatic Fall Detection Systems (FDSs) has received special attention from the research community during the last decade. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones) have fostered the adoption of this technology to deploy wearable and inexpensive architectures for fall detection. This paper presents a critical and thorough analysis of those existing fall detection systems that are based on Android devices. The review systematically classifies and compares the proposals of the literature taking into account different criteria such as the system architecture, the employed sensors, the detection algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the evaluation methods that are employed to assess the effectiveness of the detection process. The review reveals the complete lack of a reference framework to validate and compare the proposals. In addition, the study also shows that most research works do not evaluate the actual applicability of the Android devices with limited battery and computing resources to fall detection solutions.
Casilari, E et al (2015) Automatic Fall Detection System Based on the Combined Use of a Smartphone and a Smartwatch33
Due to their widespread popularity, decreasing costs, built-in sensors, computing power and communication capabilities, Android-based personal devices are being seen as an appealing technology for the deployment of wearable fall detection systems. In contrast with previous solutions in the existing literature, which are based on the performance of a single element a smartphone this paper proposes and evaluates a fall detection system that benefits from the detection performed by two popular personal devices: a smartphone and a smartwatch, both provided with an embedded accelerometer and a gyroscope. In the proposed architecture, a specific application in each component permanently tracks and analyses the patient’s movements. Diverse fall detection algorithms were implemented in the developed Android apps to discriminate falls from the conventional activities of daily living of the patient. As a novelty, a fall is only assumed to have occurred if it is simultaneously and independently detected by the two Android devices which can interact via Bluetooth communication. The system was systematically evaluated in an experimental testbed with actual test subjects simulating a set of falls and conventional movements associated with activities of daily living. The tests were repeated by varying the detection algorithm as well as the pre-defined mobility patterns executed by the subjects: ie the typology of the falls and non-fall movements.
The proposed system was compared with the cases where only one device is considered to recognize and discriminate the falls. The obtained results show that the joint use of the two detection devices clearly increases the system’s capability to avoid false alarms or false positives while maintaining the effectiveness of the detection decisions,that is to say, without increasing the ratio of false negatives or actual falls that remain undetected.
Garripoli, C et al (2015) Embedded DSP-based Telehealth Radar System for Remote In-Door Fall Detection34
Telehealth systems and applications are extensively investigated nowadays to enhance the quality-of-care and, in particular, to detect emergency situations and to monitor the well-being of elderly people, allowing them to stay at home independently as long as possible. In this paper, an embedded telehealth system for continuous, automatic, and remote monitoring of real-time fall emergencies is presented and discussed. The system, consisting of a radar sensor and base station, represents a cost-effective and efficient healthcare solution. The implementation of the fall detection data processing technique, based on the least-square support vector machines, through a digital signal processor and the management of the communication between radar sensor and base station are detailed. Experimental tests, for a total of 65 mimicked fall incidents, recorded with 16 human subjects (14 men and two women)that have been monitored for 320 min, have been used to validate the proposed system under real circumstances. The subjects’ weight is between 55 and 90 kg with heights between 1.65 and 1.82 m, while their age is between 25 and 39 years. The experimental results have shown a sensitivity to detect the fall events in real time of 100% without reporting false positives. The tests have been performed in an area where the radar’s operation was not limited by practical situations, namely, signal power, coverage of the antennas, and presence of obstacles between the subject and the antennas.
Rantz, M et al (2015Automated In-Home Fall Risk Assessment and Detection Sensor System for Elders35
Purpose of the Study: Falls are a major problem for the elderly people leading to injury, disability, and even death. An unobtrusive, in-home sensor system that continuously monitors older adults for fall risk and detects falls could revolutionize fall prevention and care. Design and
Methods: A fall risk and detection system was developed and installed in the apartments of 19 older adults at a senior living facility. The system includes pulse-Doppler radar, a Microsoft Kinect, and 2 web cameras. To collect data for comparison with sensor data and for algorithm development, stunt actors performed falls in participants’ apartments each month for 2 years and participants completed fall risk assessments (FRAs) using clinically valid, standardized instruments. The FRAs were scored by clinicians and recorded by the sensing modalities. Participants’ gait parameters were measured as they walked on a GAITRite mat. These data were used as ground truth, objective data to use in algorithm development and to compare with radar and Kinect generated variables. Results:All FRAs are highly correlated (p < .01) with the Kinect gait velocity and Kinect stride length. Radar velocity is correlated (p < .05)to all the FRAs and highly correlated (p < .01)to most. Real-time alerts of actual falls are being sent to clinicians providing faster responses to urgent situations.
Implications: The in-home FRA and detection system has the potential to help older adults remain independent, maintain functional ability, and live at home longer.
Falls are the most-common causes of unintentional injury and death in older adults. Many clinics, hospitals, and health-care providers are urgently seeking accurate, low-cost, and easy-to-use technology to predict falls before they happen: eg by monitoring the human walking pattern, or gait. Despite the wide popularity of Microsoft’s Kinect and the plethora of solutions for gait monitoring, no strategy has been proposed to date to allow non-expert users to calibrate the cameras, which is essential to accurately fuse the body motion observed by each camera in a single frame of reference. In this paper, we present a novel multi-Kinect calibration algorithm that has advanced features when compared to existing methods: 1.is easy to use; 2. it can be used in any generic Kinect arrangement; and 3. it provides accurate calibration. Extensive real-world experiments have been conducted to validate our algorithm and to compare its performance against other multi-Kinect calibration approaches, especially to show the improved estimate of gait parameters. Finally, a MATLAB Toolbox has been made publicly available for the entire research community.
-  Baig MM, Afifi S, GholamHosseini H, Mirza F. A Systematic Review of Wearable Sensors and IoT-Based Monitoring Applications for Older Adults -a Focus on Ageing Population and Independent Living.J Med Syst. 2019;438.:233. Published 2019 Jun 15. doi:10.1007/s10916-019-1365-7 [Accessed 11th May 2020]
-  Zhong R, Rau PP. Are cost-effective technologies feasible to measure gait in older adults? A systematic review of evidencebased literature.Arch Gerontol Geriatr. 2020;87:103970. doi:10.1016/j.archger.2019.103970 [Accessed 11th May 2020]
-  Bet P, Castro PC, Ponti MA. Fall detection and fall risk assessment in older person using wearable sensors: A systematic review.Int J Med Inform. 2019;130:103946. doi:10.1016/j.ijmedinf.2019.08.006 [Accessed 11 May 2020]
-  Pang I, Okubo Y, Sturnieks D, Lord SR, Brodie MA. Detection of Near Falls Using Wearable Devices: A Systematic Review.J Geriatr Phys Ther. 2019;421.:48‐56. doi:10.1519/JPT.0000000000000181 [Accessed 11th May 2020]
-  Sun R, Sosnoff JJ. Novel sensing technology in fall risk assessment in older adults: a systematic review.BMC Geriatr. 2018;181.:14. Published 2018 Jan 16. doi:10.1186/s12877-018-0706-6 [Accessed 11th May 2020]
-  Ma CZ, Wong DW, Lam WK, Wan AH, Lee WC. Balance Improvement Effects of Biofeedback Systems with State-of-the-Art Wearable Sensors: A Systematic Review.Sensors (Basel). 2016;164.:434. Published 2016 Mar 25. doi:10.3390/s16040434 [Accessed 11th May 2020]
-  Hu X, Qu X. Pre-impact fall detection.Biomed Eng Online. 2016;151.:61. Published 2016 Jun 1. doi:10.1186/s12938-016-0194-x [Accessed 11th May 2020]
-  Barker A, Cameron P, Flicker L et al Evaluation of RESPOND, a patient-centred program to prevent falls in older people presenting to the emergency department with a fall: A randomised controlled trial.PLoS Med. 2019;165.:e1002807. Published 2019 May 24. doi:10.1371/journal.pmed.1002807 [Accessed 5 May 2020]
-  Antos SA, Danilovich MK, Eisenstein AR, Gordon KE, Kording KP. Smartwatches Can Detect Walker and Cane Use in Older Adults. Innov Aging. 2019;31.:igz008. Published 2019 Apr 22. doi:10.1093/geroni/igz008 [Accessed 11th May 2020]
-  Clemente J, Li F, Valero M, Song W. Smart Seismic Sensing for Indoor Fall Detection, Location, and Notification.IEEE J Biomed Health Inform. 2020;242.:524‐532. doi:10.1109/JBHI.2019.2907498 [Accessed 11th May 2020]
- 11] Keogh A, Dorn JF, Walsh L, Calvo F, Caulfield B. Comparing the Usability and Acceptability of Wearable Sensors Among Older Irish Adults in a Real-World Context: Observational Study.JMIR Mhealth Uhealth. 2020;84.:e15704. Published 2020 Apr 20. doi:10.2196/15704 [Accessed 11th May 2020]
-  Tahir A, Morison G, Skelton DA, Gibson RM. Hardware/Software Co-design of Fractal Features based Fall Detection System.Sensors (Basel). 2020;208.:E2322. Published 2020 Apr 18. doi:10.3390/s20082322 [Accessed 11th May 2020]
-  Boutellaa E, Kerdjidj O, Ghanem K. Covariance matrix based fall detection from multiple wearable sensors.J Biomed Inform. 2019;94:103189. doi:10.1016/j.jbi.2019.103189 [Accessed 11th May 2020]
-  Luna-Perejón F, Domínguez-Morales MJ, Civit-Balcells A. Wearable Fall Detector Using Recurrent Neural Networks.Sensors (Basel). 2019;19(22):4885. Published 2019 Nov 8.doi:10.3390/s19224885 [Accessed 11th May 2020]
-  Scheurer S, Koch J, Kucera M et al Optimization and Technical Validation of the AIDE-MOI Fall Detection Algorithm in a RealLife Setting with Older Adults.Sensors (Basel). 2019;196.:1357. Published 2019 Mar 18. doi:10.3390/s19061357 [Accessed 11th May 2020]
-  Lee Y, Choi W, Lee K, Song C, Lee S. Virtual Reality Training With Three-Dimensional Video Games Improves Postural Balance and Lower Extremity Strength in Community-Dwelling Older Adults.J Aging Phys Act. 2017;254.:621‐627. doi:10.1123/japa.2015-0271 [Accessed 11th May 2020]
-  Nizam Y, Mohd MNH, Jamil MMA. Development of a User-Adaptable Human Fall Detection Based on Fall Risk Levels Using Depth Sensor.Sensors (Basel). 2018;187.:2260. Published 2018 Jul 13. doi:10.3390/s18072260 [Accessed 11th May 2020]
-  Reyes A, Qin P, Brown CA. A standardized review of smartphone applications to promote balance for older adults.Disabil Rehabil. 2018;406.:690‐696. doi:10.1080/09638288.2016.1250124 [Accessed 5 May 2020]
-  Rucco R, Sorriso A, Liparoti M et al Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review [published correction appears in Sensors (Basel). 2018 Jul 30;188.:].Sensors (Basel). 2018;185.:1613. Published 2018 May 18. doi:10.3390/s18051613 [Accessed 11th May 2020]
-  Sucerquia A, López JD, Vargas-Bonilla JF. Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer. Sensors (Basel). 2018;184.:1101. Published 2018 Apr 5. doi:10.3390/s18041101 [Accessed 11th May 2020]
-  Wang C, Lu W, Redmond SJ, Stevens MC, Lord SR, Lovell NH. A Low-Power Fall Detector Balancing Sensitivity and False Alarm Rate.IEEE J Biomed Health Inform. 2018;226.:1929‐1937. doi:10.1109/JBHI.2017.2778271[Accessed 11th May 2020]
-  Yu S, Chen H, Brown RA. Hidden Markov Model-Based Fall Detection With Motion Sensor Orientation Calibration: A Case for Real-Life Home Monitoring.IEEE J Biomed Health Inform. 2018;226.:1847‐1853. doi:10.1109/JBHI.2017.2782079 [Accessed 11th May 2020]
-  Aziz O, Klenk J, Schwickert L et al Validation of accuracy of SVM-based fall detection system using real-world fall and nonfall datasets.PLoS One. 2017;127.:e0180318. Published 2017 Jul 5. doi:10.1371/journal.pone.0180318 [Accessed 11th May 2020]
-  Bayen E, Jacquemot J, Netscher G, Agrawal P, Tabb Noyce L, Bayen A. Reduction in Fall Rate in Dementia Managed Care Through Video Incident Review: Pilot Study.J Med Internet Res. 2017;19(10):e339. Published 2017 Oct 17. doi:10.2196/jmir.8095 [Accessed 11th May 2020]
-  Brodie MA, Okubo Y, Annegarn J, Wieching R, Lord SR, Delbaere K. Disentangling the health benefits of walking from increased exposure to falls in older people using remote gait monitoring and multi-dimensional analysis.Physiol Meas. 2017;381.:45‐62. doi:10.1088/1361-6579/38/1/45 [Accessed 11th May 2020]
-  de Miguel K, Brunete A, Hernando M, Gambao E. Home Camera-Based Fall Detection System for the Elderly.Sensors (Basel). 2017;17(12):2864. Published 2017 Dec 9. doi:10.3390/s17122864 [Accessed 11th May 2020]
-  Jatesiktat P, Wei Tech Ang. An elderly fall detection using a wrist-worn accelerometer and barometer.Conf Proc IEEE Eng Med Biol Soc. 2017;2017:125‐130. doi:10.1109/EMBC.2017.8036778 [Accessed 11th May 2020]
-  Medrano C, Igual R, García-Magariño I, Plaza I, Azuara G. Combining novelty detectors to improve accelerometer-based fall detection.Med Biol Eng Comput. 2017;55(10):1849‐1858. doi:10.1007/s11517-017-1632-z [Accessed 11th May 2020]
-  Minvielle L, Atiq M, Serra R, Mougeot M, Vayatis N. Fall detection using smart floor sensor and supervised learning.Conf Proc IEEE Eng Med Biol Soc. 2017;2017:3445‐3448. doi:10.1109/EMBC.2017.8037597 [Accessed 11th May 2020]
-  Rajagopalan R, Litvan I, Jung TP. Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.Sensors (Basel). 2017;17(11):2509. Published 2017 Nov 1. doi:10.3390/s17112509 [Accessed 11th May 2020]
-  Demiris G, Chaudhuri S, Thompson HJ. Older Adults’ Experience with a Novel Fall Detection Device.Telemed J E Health. 2016;22(9):726‐732. doi:10.1089/tmj.2015.0218 [Accessed 11th May 2020]
-  Casilari E, Luque R, Morón MJ. Analysis of Android Device-Based Solutions for Fall Detection.Sensors (Basel). 2015;158.:17827‐17894. Published 2015 Jul 23. doi:10.3390/s150817827 [Accessed 11th May 2020]
-  Casilari E, Oviedo-Jiménez MA. Automatic Fall Detection System Based on the Combined Use of a Smartphone and a Smartwatch.PLoS One. 2015;10(11):e0140929. Published 2015 Nov 11. doi:10.1371/journal.pone.0140929 [Accessed 11th May 2020]
-  Garripoli C, Mercuri M, Karsmakers P et al Embedded DSP-based telehealth radar system for remote in-door fall detection.IEEE J Biomed Health Inform. 2015;191.:92‐101. doi:10.1109/JBHI.2014.2361252 [Accessed 11th May 2020]
-  Rantz M, Skubic M, Abbott C et al Automated In-Home Fall Risk Assessment and Detection Sensor System for Elders.Gerontologist. 2015;55 Suppl 1(Suppl 1):S78‐S87. doi:10.1093/geront/gnv044 [Accessed 11th May 2020]
-  Staranowicz AN, Ray C, Mariottini GL. Easy-to-use, general, and accurate multi-Kinect calibration and its application to gait monitoring for fall prediction.Conf Proc IEEE Eng Med Biol Soc. 2015;2015:4994‐4998. doi:10.1109/EMBC.2015.7319513 [Accessed 11th May 2020]