Increasing fall risk awareness using wearables: A fall risk awareness protocol

Enhancing Free-Living Fall Risk Assessment: Contextualizing Mobility Based IMU Data

Fall risk assessment needs contemporary approaches based on habitual data. Currently, inertial measurement unit (IMU)-based wearables are used to inform free-living spatio-temporal gait characteristics to inform mobility assessment. Typically, a fluctuation of those characteristics will infer an increased fall risk. However, current approaches with IMUs alone remain limited, as there are no contextual data to comprehensively determine if underlying mechanistic (intrinsic) or environmental (extrinsic) factors impact mobility and, therefore, fall risk. Here, a case study is used to explore and discuss how contemporary video-based wearables could be used to supplement arising mobility-based IMU gait data to better inform habitual fall risk assessment. A single stroke survivor was recruited, and he conducted a series of mobility tasks in a lab and beyond while wearing video-based glasses and a single IMU. The latter generated topical gait characteristics that were discussed according to current research practices. Although current IMU-based approaches are beginning to provide habitual data, they remain limited. Given the plethora of extrinsic factors that may influence mobility-based gait, there is a need to corroborate IMUs with video data to comprehensively inform fall risk assessment. Use of artificial intelligence (AI)-based computer vision approaches could drastically aid the processing of video data in a timely and ethical manner. Many off-the-shelf AI tools exist to aid this current need and provide a means to automate contextual analysis to better inform mobility from IMU gait data for an individualized and contemporary approach to habitual fall risk assessment.

Effects of Healthcare Technologies on the Promotion of Physical Activities in Older Persons: A Systematic Review

This study aimed to explore the effects of health technologies on the promotion of health through physical activities of older persons. Following PRISMA guidelines, a systematic review of relevant articles published prior to 2020 was conducted from selected indices such as COCHRANE, PubMed, Science Direct, Proquest, including the use of hand search procedure. Twenty-seven articles were analyzed with significant findings influential to older people nursing: types of health technologies used for promoting physical activity; effects of technology use in promoting physical activity of older person care; and aspects that need to be considered in technology use among older persons. Characteristics of technologies were accuracy, usefulness, reliability, comfort, safety, and relevancy. Most technologies promoting physical activities for older people were wearable technologies that use artificial intelligence. Altogether, these technologies influenced overall healthcare behaviors of older persons. With healthcare technology efficiencies, proficiencies, and dependencies, technology-based healthcare have served older people well. Most technologies for older people care, such as wearables, reliably produce characteristics enhancing dependency and accuracy of bio-behavioral information influencing physical activities of older persons. Health technologies foster the values of physical activities among older persons thereby promoting healthy living.

Inertial Data-Based AI Approaches for ADL and Fall Recognition

The recognition of Activities of Daily Living (ADL) has been a widely debated topic, with applications in a vast range of fields. ADL recognition can be accomplished by processing data from wearable sensors, specially located at the lower trunk, which appears to be a suitable option in uncontrolled environments. Several authors have addressed ADL recognition using Artificial Intelligence (AI)-based algorithms, obtaining encouraging results. However, the number of ADL recognized by these algorithms is still limited, rarely focusing on transitional activities, and without addressing falls. Furthermore, the small amount of data used and the lack of information regarding validation processes are other drawbacks found in the literature. To overcome these drawbacks, a total of nine public and private datasets were merged in order to gather a large amount of data to improve the robustness of several ADL recognition algorithms. Furthermore, an AI-based framework was developed in this manuscript to perform a comparative analysis of several ADL Machine Learning (ML)-based classifiers. Feature selection algorithms were used to extract only the relevant features from the dataset’s lower trunk inertial data. For the recognition of 20 different ADL and falls, results have shown that the best performance was obtained with the K-NN classifier with the first 85 features ranked by Relief-F (98.22% accuracy). However, Ensemble Learning classifier with the first 65 features ranked by Principal Component Analysis (PCA) presented 96.53% overall accuracy while maintaining a lower classification time per window (0.039 ms), showing a higher potential for its usage in real-time scenarios in the future. Deep Learning algorithms were also tested. Despite its outcomes not being as good as in the prior procedure, their potential was also demonstrated (overall accuracy of 92.55% for Bidirectional Long Short-Term Memory (LSTM) Neural Network), indicating that they could be a valid option in the future.

A modified digital functional reach test device using an ultrasonic sensor for balance assessment: A test of validity and reliability

Study aim: Evaluation of dynamic balance is inferred to be compulsory for fall prevention in the elderly. Therefore, this study aimed to develop a modified digital functional reach test device using an ultrasonic sensor for balance assessment and to test validity and reliability of the newly developed tool to qualify psychometric properties.

Material and methods: This study was a cross-sectional study of a convenient sample including 50 participants both males and females. Mean age of the participants was 51.20 ± 19.30 years. Reliability of the newly developed device was analysed using the intraclass correlation coefficient (ICC) and standard error of measurement (SEM). The criterion validity was also investigated using a yardstick mounted on the wall at a level of shoulder together with the MaxTraq® 2D motion analysis software. The modified digital functional reach test device using an ultrasonic sensor was correlated with the conventional FRT and the MaxTraq® 2D motion analysis.

Results: The results presented that test-retest reliability of the modified digital functional reach test device was good reliability (ICC = 0.76) and low standard error of measurement (1.41) was found for test-retest reliability. The degree of agreement between the modified device, the conventional FRT, and the MaxTraq® 2D motion analysis was high (r = 0.71 and 0.77 respectively).

Conclusions: The findings suggested that the modified digital functional reach test device using an ultrasonic sensor was a valid and reliable instrument for fall risk screening towards functional reach distance.

The Context of Caring and Concern for Falling Differentiate Which Mobile Fall Technology Features Chinese Family Caregivers Find Most Important

Falls are not only a leading cause of death and disability, but also a strain on the capacity for caregivers to provide care. This study examined how the context of caregiving relates to the importance of caregiver-defined mobile fall prevention feature sets. A sample of 266 family caregivers, recruited from a Chinese social media platform, reported care for an older adult and interest in mobile fall prevention technology features. Factor analysis identified three caregiver-defined feature sets: automatic fall response, digitized fall prevention tools, and social features. Multiple regression showed caregivers' concern about falling was the most robust predictor of a feature set's importance. Poisson regression revealed that caregiver concern and assistance with instrumental activities of daily living were associated with rating more features as important. Our findings suggest that caregivers are interested in mobile fall prevention technologies that support older adults' independence while also alleviating concerns about falling.

State-of-the-Art Wearable Sensors and Possibilities for Radar in Fall Prevention

Radar technology is constantly evolving, and new applications are arising, particularly for the millimeter wave bands. A novel application for radar is gait monitoring for fall prevention, which may play a key role in maintaining the quality of life of people as they age. Alarming statistics indicate that one in three adults aged 65 years or older will experience a fall every year. A review of the sensors used for gait analysis and their applications to technology-based fall prevention interventions was conducted, focusing on wearable devices and radar technology. Knowledge gaps were identified, such as wearable radar development, application specific signal processing and the use of machine learning algorithms for classification and risk assessment. Fall prevention through gait monitoring in the natural environment presents significant opportunities for further research. Wearable radar could be useful for measuring gait parameters and performing fall risk-assessment using statistical methods, and could also be used to monitor obstacles in real-time.

Basic gait pattern and impact of fall risk factors on gait among older adults in India

BACKGROUND

An unstable gait pattern is an indicator of an increased risk of falls among older adults. Data on basic gait parameters is useful in the early identification of gait impairment. However, reference gait measurements are not available in low- and middle-income countries.

RESEARCH QUESTION

What are the normative reference values of gait parameters and do fall risk factors such as impaired balance, functional difficulty, and multimorbidity affect the gait patterns of older adults in India?

METHODS

A cross-sectional data of 659 older adults were collected using a semi-structured schedule. Gait parameters were measured using wearable sensors. Descriptive statistics, independent t-test, and one-way ANCOVA were used to determine the significant difference (p < 0.05) in gait parameters across the risk factors.

RESULTS

A mean stride length of 123.00 ± 15.19 cm, stride velocity of 110.57 ± 17.57 cm/s, and a cadence of 106.14 ± 11.44 steps/minute were reported in the study. Functional difficulties and balance impairment were the two major risk factors that affected stride velocity, stride length, and cadence after adjusting for age and height. No difference in gait parameters was observed among participants with and without multimorbidity.

SIGNIFICANCE

This study provides a baseline or reference values of various gait parameters measured on a large sample of population aged 60 and above from India. Assessment of gait patterns and associated risk factors in a clinical setup will help identify the older adults at risk of falls and reduce the enormous burden of fall injuries. Since gait parameters show a large variation across geographical regions, it is important to have region-specific reference values.

Reducing Fall Risks Among Visually Impaired Older Adults

Falls are the leading cause of death due to unintentional injuries in the older adult population, and fall-related death rates among older adults are escalating annually. Visual deficits are underrecognized and underdiagnosed, which increases fall risk. The purpose of this article is to provide a review of the common types of visual impairment, their etiology, and treatment and to present strategies to reduce falls among older adults with visual impairments. Both traditional home safety interventions and emerging technology-based interventions to reduce falls are described. Appropriate use of both traditional and emerging fall prevention interventions may reduce fall risk and falls among older adult home healthcare patients.

Foreseeing future falls with accelerometer features in active community-dwelling older persons with no recent history of falls

BACKGROUND

Acceleration sensors are a viable option for monitoring gait patterns and its application on monitoring falls and risk of falling. However the literature still lacks prospective studies to investigate such risk before the occurrence of falls.

OBJECTIVE

To investigate features extracted from accelerometer signals with the purpose of predicting future falls in individuals with no recent history of falls.

METHODS

In this study we investigate the risk of fall in active and healthy community-dwelling living older persons with no recent history of falls, using a single accelerometer and variants of the Timed Up and Go (TUG) test. A prospective study was conducted with 74 healthy non-fallers older persons. After collecting acceleration data from the participants at the baseline, the occurrence of falls (outcome) was monitored quarterly during one year. A set of frequency features were extracted from the signal and their ability to predict falls was evaluated.

RESULTS

The best individual feature result shows an accuracy of 0.75, sensitivity of 0.71 and specificity of 0.76. A fusion of the three best features increases the sensitivity to 0.86. On the other hand, the cut-off points of the TUG seconds, often used to assess fall risk, did not demonstrate adequate sensitivity.

CONCLUSION

The results confirms previous evidence that accelerometer features can better estimate fall risk, and support potential applications that try to infer falls risk in less restricted scenarios, even in a sample stratified by age and gender composed of active and healthy community-dwelling living older persons.

Using consumer-wearable technology for remote assessment of physiological response to stress in the naturalistic environment

Psychosocial stress is a major risk factor for morbidity and mortality related to a wide range of health conditions and has a significant negative impact on public health. Quantifying exposure to stress in the naturalistic environment can help to better understand its health effects and identify strategies for timely intervention. The objective of the current project was to develop and test the infrastructure and methods necessary for using wearable technology to quantify individual response to stressful situations and to determine if popular and accessible fitness trackers such as Fitbit® equipped with an optical heart rate (HR) monitor could be used to detect physiological response to psychosocial stress in everyday life. The participants in this study were University of Minnesota students (n = 18) that owned a Fitbit® tracker and had at least one upcoming examination. Continuous HR and activity measurements were obtained during a 7-day observation period containing examinations self-reported by the participants. Participants responded to six ecological momentary assessment surveys per day (~ 2 hour intervals) to indicate occurrence of stressful events. We compared HR during stressful events (e.g., exams) to baseline HR during periods indicated as non-stressful using mixed effects modeling. Our results show that HR was elevated by 8.9 beats per minute during exams and by 3.2 beats per minute during non-exam stressors. These results are consistent with prior laboratory findings and indicate that consumer wearable fitness trackers could serve as a valuable source of information on exposure to psychosocial stressors encountered in the naturalistic environment.

Pre-Impact Detection Algorithm to Identify Tripping Events Using Wearable Sensors

This study aimed to investigate the performance of an updated version of our pre-impact detection algorithm parsing out the output of a set of Inertial Measurement Units (IMUs) placed on lower limbs and designed to recognize signs of lack of balance due to tripping. Eight young subjects were asked to manage tripping events while walking on a treadmill. An adaptive threshold-based algorithm, relying on a pool of adaptive oscillators, was tuned to identify abrupt kinematics modifications during tripping. Inputs of the algorithm were the elevation angles of lower limb segments, as estimated by IMUs located on thighs, shanks and feet. The results showed that the proposed algorithm can identify a lack of balance in about 0.37 ± 0.11 s after the onset of the perturbation, with a low percentage of false alarms (<10%), by using only data related to the perturbed shank. The proposed algorithm can hence be considered a multi-purpose tool to identify different perturbations (i.e., slippage and tripping). In this respect, it can be implemented for different wearable applications (e.g., smart garments or wearable robots) and adopted during daily life activities to enable on-demand injury prevention systems prior to fall impacts.

Covariance matrix based fall detection from multiple wearable sensors

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, e.g. 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.

Enabling older adults to carry out paperless falls-risk self-assessments using guidetomeasure-3D: A mixed methods study

BACKGROUND

The home environment falls-risk assessment process (HEFAP) is a widely used falls prevention intervention strategy which involves a clinician using paper-based measurement guidance to ensure that appropriate information and measurements are taken and recorded accurately. Despite the current use of paper-based guidance, over 30% of all assistive devices installed within the home are abandoned by patients. This is in part due to poor fit between the device, the patient, and the environment in which it is installed. Currently HEFAP is a clinician-led process, however, older adult patients are increasingly being expected to collect HEFAP measurements themselves as part of the personalisation agenda. Without appropriate patient-centred guidance, levels of device abandonment to are likely to rise to unprecedented levels. This study presents guidetomeasure-3D, a mobile 3D measurement guidance application designed to support patients in carrying out HEFAP self-assessments.

AIM

The aim of this study is to present guidetomeasure-3D, a web-enabled 3D mobile application that enables older-adult patients to carry out self-assessment measurement tasks, and to carry out a mixed-methods evaluation of its performance, and associated user perceptions of the application, compared with a 2D paper-based equivalent.

METHODS

Thirty-four older adult participants took part in a mixed-methods within-subjects repeated measures study set within a living lab. A series of HEFAP self-assessment tasks were carried out according to two treatment conditions: (1) using the 3D guidetomeasure-3D application; (2) using a 2D paper-based guide. SUS questionnaires and semi-structured interviews were completed at the end of the task. A comparative statistical analysis explored performance with regards to measurement accuracy, accuracy consistency, task efficiency, and system usability. Interview transcripts were analysed using inductive and deductive thematic analysis (informed by UTAUT).

RESULTS

The guidetomeasure-3D application outperformed the 2D paper-based guidance in terms of accuracy (smaller mean error difference in 11 out of 12 items), accuracy consistency (p < 0.05, for 6 out of 12 items), task efficiency (p = 0.003), system usability (p < 0.00625, for two out of 10 SUS items), and clarity of guidance (p < 0.0125, for three out of four items). Three high-level themes emerged from interviews: Performance Expectancy, Effort Expectancy, and Social Influence. Participants reported that guidetomeasure-3D provided improved visual quality, clarity, and more precise guidance overall. Real-time audio instruction was reported as being particularly useful, as was the use of the object rotation and zoom functions which were associated with improving user confidence particularly when carrying out more challenging tasks.

CONCLUSIONS

This study reveals that older adults using guidetomeasure-3D achieved improved levels of accuracy and efficiency along with improved satisfaction and increased levels of confidence compared with the 2D paper-based equivalent. These results are significant and promising for overcoming HEFAP equipment abandonment issue. Furthermore they constitute an important step towards overcoming challenges associated with older adult patients, the digitisation of healthcare, and realising the enablement of patient self-care and management via the innovative use of mobile technologies. Numerous opportunities for the generalisability and transferability of the findings of this research are also proposed. Future research will explore the extent to which mobile 3D visualisation technologies may be utilised to optimise the clinical utility of HEFAP when deployed by clinicians.

The Multidisciplinary Approach to Alzheimer's Disease and Dementia. A Narrative Review of Non-Pharmacological Treatment

Background: Alzheimer's disease (AD) and dementia are chronic diseases with progressive deterioration of cognition, function, and behavior leading to severe disability and death. The prevalence of AD and dementia is constantly increasing because of the progressive aging of the population. These conditions represent a considerable challenge to patients, their family and caregivers, and the health system, because of the considerable need for resources allocation. There is no disease modifying intervention for AD and dementia, and the symptomatic pharmacological treatments has limited efficacy and considerable side effects. Non-pharmacological treatment (NPT), which includes a wide range of approaches and techniques, may play a role in the treatment of AD and dementia. Aim: To review, with a narrative approach, current evidence on main NPTs for AD and dementia. Methods: PubMed and the Cochrane database of systematic reviews were searched for studies written in English and published from 2000 to 2018. The bibliography of the main articles was checked to detect other relevant papers. Results: The role of NPT has been largely explored in AD and dementia. The main NPT types, which were reviewed here, include exercise and motor rehabilitation, cognitive rehabilitation, NPT for behavioral and psychological symptoms of dementia, occupational therapy, psychological therapy, complementary and alternative medicine, and new technologies, including information and communication technologies, assistive technology and domotics, virtual reality, gaming, and telemedicine. We also summarized the role of NPT to address caregivers' burden. Conclusions: Although NPT is often applied in the multidisciplinary approach to AD and dementia, supporting evidence for their use is still preliminary. Some studies showed statistically significant effect of NPT on some outcomes, but their clinical significance is uncertain. Well-designed randomized controlled trials with innovative designs are needed to explore the efficacy of NPT in AD and dementia. Further studies are required to offer robust neurobiological grounds for the effect of NPT, and to examine its cost-efficacy profile in patients with dementia.

Falls management framework for supporting an independent lifestyle for older adults: a systematic review

Falls are one of the common health and well-being issues among the older adults. Internet of things (IoT)-based health monitoring systems have been developed over the past two decades for improving healthcare services for older adults to support an independent lifestyle. This research systematically reviews technological applications related to falls detection and falls management. The systematic review was conducted in accordance to the preferred reporting items for systematic reviews and meta-analysis statement (PRISMA). Twenty-four studies out of 806 articles published between 2015 and 2017 were identified and included in this review. Selected studies were related to pre-fall and post-fall applications using motion sensors (10; 41.67%), environment sensors (10; 41.67%) and few studies used the combination of these types of sensors (4; 16.67%). As an outcome of this review, we postulated a falls management framework (FMF). FMF considered pre- and post-fall strategies to support older adults live independently. A part of this approach involved active analysis of sensor data with the aim of helping the older adults manage their risk of fall and stay safe in their home. FMF aimed to serve the researchers, developers, clinicians and policy makers with pre- and post-falls management strategies to enhance the older adults' independent living and well-being.

Diet and Activity Assessments and Interventions Using Technology in Older Adults

This paper reports on the findings and recommendations specific to older adults from the "Tech Summit: Innovative Tools for Assessing Diet and Physical Activity for Health Promotion" forum organized by the North American branch of the International Life Sciences Institute. The summit aimed to investigate current and emerging challenges related to improving energy balance behavior assessment and intervention via technology. The current manuscript focuses on how novel technologies are applied in older adult populations and enumerated the barriers and facilitators to using technology within this population. Given the multiple applications for technology in this population, including the ability to monitor health events and behaviors in real time, technology presents an innovative method to aid with the changes associated with aging. Although older adults are often perceived as lacking interest in and ability to adopt technologies, recent studies show they are comfortable adopting technology and user uptake is high with proper training and guided facilitation. Finally, the conclusions suggest recommendations for future research, including the need for larger trials with clinical outcomes and more research using end-user design that includes older adults as technology partners who are part of the design process.

THEME INFORMATION

This article is part of a theme issue entitled Innovative Tools for Assessing Diet and Physical Activity for Health Promotion, which is sponsored by the North American branch of the International Life Sciences Institute.

Does functional capacity, fall risk awareness and physical activity level predict falls in older adults in different age groups?

The aims of this study were to examine whether: i) functional capacity and physical activity level differ between fallers and non-fallers older adults, by controlling for fall risk awareness; ii) functional capacity, fall risk awareness and physical activity differ between fallers and non-fallers older adults, by controlling for age; iii) variables and which may predict falls in different age groups. 1826 older adults performed a series of functional tests and reported their fall episodes, fall risk awareness and physical activity level. The overall incidence of falls was high (40.2%), and falls risk awareness scores reduced with age. The older adults with greater falls risk awareness and non-fallers presented better scores in all functional tests and physical activity level (P < .05). Functional tests and falls risk awareness differed among age groups and differed between fallers and non-fallers, irrespective of age group (P < .05). Falls risk awareness predicted falls in all age groups (odds ranging: 1.05-1.09). Handgrip strength and balance scores predicted falls until 79 years (OR = 1.04, 95%CI = 1.01-1.06). The physical activity level predicted falls up to 70 years (OR = 1.09, 95%CI = 1.06-1.12). Functional mobility was able to predict falls up to 80 years (OR = 1.06, 95%CI = 1.01-1.08). Therefore, according to age, functional capacity, physical activity level and falls risk awareness can be a predictor of falls in older adults.

Telehealth in older adults with cancer in the United States: The emerging use of wearable sensors

As the aging and cancer populations in the world continue to increase, the need for complements to traditional geriatric assessments and the logical incorporation of fast and reliable telehealth tools have become interlinked. In the United States, studies examining the use of telehealth for chronic disease management have shown promising results in small groups. The implementation of health technology on a broader scale requires older adults to both accept and adapt such innovation into routine medical care. Though the commercial and recreational use of new technology has increased in older individuals, the transition into creating a smart and connected home that can interface with both patients and healthcare professionals is in its early phases. Current limitations include an inherent digital divide, as well as concerns regarding privacy, data volume, rapid change, cost and reimbursement. The emergence of low-cost, high-fidelity wearable sensors with a spectrum of clinical utility may be the key to increased use and adaptation by older adults. An opportunity to utilize wearable sensors for objective and real-time assessment of older patients with cancer for baseline functional status and treatment toxicity may be on the horizon.

Recognizing Bedside Events Using Thermal and Ultrasonic Readings

Falls in homes of the elderly, in residential care facilities and in hospitals commonly occur in close proximity to the bed. Most approaches for recognizing falls use cameras, which challenge privacy, or sensor devices attached to the bed or the body to recognize bedside events and bedside falls. We use data collected from a ceiling mounted 80 × 60 thermal array combined with an ultrasonic sensor device. This approach makes it possible to monitor activity while preserving privacy in a non-intrusive manner. We evaluate three different approaches towards recognizing location and posture of an individual. Bedside events are recognized using a 10-second floating image rule/filter-based approach, recognizing bedside falls with 98.62% accuracy. Bed-entry and exit events are recognized with 98.66% and 96.73% accuracy, respectively.

Concurrent validation of an index to estimate fall risk in community dwelling seniors through a wireless sensor insole system: A pilot study

Falls are a major health problem for older adults with immediate effects, such as fractures and head injuries, and longer term effects including fear of falling, loss of independence, and disability. The goals of the WIISEL project were to develop an unobtrusive, self-learning and wearable system aimed at assessing gait impairments and fall risk of older adults in the home setting; assessing activity and mobility in daily living conditions; identifying decline in mobility performance and detecting falls in the home setting. The WIISEL system was based on a pair of electronic insoles, able to transfer data to a commercially available smartphone, which was used to wirelessly collect data in real time from the insoles and transfer it to a backend computer server via mobile internet connection and then onwards to a gait analysis tool. Risk of falls was calculated by the system using a novel Fall Risk Index (FRI) based on multiple gait parameters and gait pattern recognition. The system was tested by twenty-nine older users and data collected by the insoles were compared with standardized functional tests with a concurrent validity approach. The results showed that the FRI captures the risk of falls with accuracy that is similar to that of conventional performance-based tests of fall risk. These preliminary findings support the idea that theWIISEL system can be a useful research tool and may have clinical utility for long-term monitoring of fall risk at home and in the community setting.

Wearables for independent living in older adults: Gait and falls

Solutions are needed to satisfy care demands of older adults to live independently. Wearable technology (wearables) is one approach that offers a viable means for ubiquitous, sustainable and scalable monitoring of the health of older adults in habitual free-living environments. Gait has been presented as a relevant (bio)marker in ageing and pathological studies, with objective assessment achievable by inertial-based wearables. Commercial wearables have struggled to provide accurate analytics and have been limited by non-clinically oriented gait outcomes. Moreover, some research-grade wearables also fail to provide transparent functionality due to limitations in proprietary software. Innovation within this field is often sporadic, with large heterogeneity of wearable types and algorithms for gait outcomes leading to a lack of pragmatic use. This review provides a summary of the recent literature on gait assessment through the use of wearables, focusing on the need for an algorithm fusion approach to measurement, culminating in the ability to better detect and classify falls. A brief presentation of wearables in one pathological group is presented, identifying appropriate work for researchers in other cohorts to utilise. Suggestions for how this domain needs to progress are also summarised.

Real-Time Fall Risk Assessment Using Functional Reach Test

Falls are common and dangerous for survivors of stroke at all stages of recovery. The widespread need to assess fall risk in real time for individuals after stroke has generated emerging requests for a reliable, inexpensive, quantifiable, and remote clinical measure/tool. In order to meet these requests, we explore the Functional Reach Test (FRT) for real-time fall risk assessment and implement the FRT function in mStroke, a real-time and automatic mobile health system for poststroke recovery and rehabilitation. mStroke is designed, developed, and delivered as an Application (App) running on a hardware platform consisting of an iPad and one or two wireless body motion sensors based on different mobile health functions. The FRT function in mStroke is extensively tested on healthy human subjects to verify its concept and feasibility. Preliminary performance will be presented to justify the further exploration of the FRT function in mStroke through clinical trials on individuals after stroke, which may guide its ubiquitous exploitation in the near future.