Fall detection devices and their use with older adults: a systematic review

Association between the use of orexin receptor antagonists and falls or fractures: A meta-analysis

Evidence indicates that the use of sedative-hypnotics, including benzodiazepines and z-drugs, is linked to an increased risk of falls and fractures. Nonetheless, the potential exacerbation of this risk by orexin receptor antagonists, which are novel therapeutic agents for treating insomnia, remains uncertain despite their escalating prevalence in clinical practice. We systematically searched four electronic databases from inception to April 17, 2024. In addition, we performed a quality assessment; calculated pooled odds ratios (ORs) to assess the relationship between the use of orexin receptor antagonists and the occurrence of falls or fractures; evaluated heterogeneity across the included studies; and conducted sensitivity analyses. The meta-analysis encompassed eight papers, comprising a total of 46,636 subjects. These papers included 5 case-control studies and 3 randomized controlled trials (RCTs), collectively encompassing ten studies. Analysis of the included case-control studies (pooled adjusted OR = 0.75, 95% confidence interval [CI] = 0.00-1.50, I2 = 66.2%, k = 3) and RCTs (OR = 0.68, 95% CI = 0.31-1.50, I2 = 45.9%, k = 5) indicated that the use of orexin receptor antagonists did not elevate the risk of falls. Similarly, analysis of the included case-control studies revealed no significant increase in the risk of fractures associated with the use of orexin receptor antagonists (pooled adjusted OR = 1.01, 95% CI = 0.82-1.20, I2 = 40.1%, k = 2). This meta-analysis suggests that the use of orexin receptor antagonists for treating insomnia does not escalate the risk of falls or fractures, although the data for lemborexant and daridorexant are limited.

Effects of community-based fall prevention interventions for older adults using information and communication technology: A systematic review and meta-analysis

Objectives: This systematic review and meta-analysis aimed to investigate the effect of fall prevention interventions using information and communication technology (ICT). Methods: A comprehensive search across four databases was performed. The inclusion criteria were fall prevention interventions including telehealth, computerized balance training, exergaming, mobile application education, virtual reality exercise, and cognitive-behavioral training for community-dwelling adults aged ≥60 years. Results: Thirty-four studies were selected. Telehealth, smart home systems, and exergames reduced the risk of falls (RR = 0.63, 95% CI [0.54, 0.75]). Telehealth and exergame improved balance (MD = 3.30, 95% CI [1.91, 4.68]; MD = 4.40, 95% CI [3.09, 5.71]). Telehealth improved physical function (SMD = 0.69, 95% CI [0.23, 1.16]). Overall, ICT fall interventions improved fall efficacy but not cognitive function. For quality of life (QOL), mixed results were found depending on the assessment tools. Conclusion: Future investigations on telehealth, smart home systems, or exergames are needed to motivate older adults to exercise and prevent falls.

Wrist-Based Fall Detection: Towards Generalization across Datasets

Increasing age is related to a decrease in independence of movement and with this decrease comes falls, millions of falls occur every year and the most affected people are the older adults. These falls usually have a big impact on health and independence of the older adults, as well as financial impact on the health systems. Thus, many studies have developed fall detectors from several types of sensors. Previous studies related to the creation of fall detection systems models use only one dataset that usually has a small number of samples. Training and testing machine learning models in this small scope: (i) yield overoptimistic classification rates, (ii) do not generalize to real-life situations and (iii) have very high rate of false positives. Given this, the proposal of this research work is the creation of a new dataset that encompasses data from three different datasets, with more than 1300 fall samples and 28 K negative samples. Our new dataset includes a standard way of adding samples, which allow the future addition of other data sources. We evaluate our dataset by using classic cost-sensitive Machine Leaning methods that deal with class imbalance. For the training and validation of this model, a set of temporal and frequency features were extracted from the raw data of an accelerometer and a gyroscope using a sliding window of 2 s with an overlap of 50%. We study the generalization properties of each dataset, by testing on the other datasets and also the performance of our new dataset. The model showed a good ability to distinguish between activities of daily living and falls, achieving a recall of 90.57%, a specificity of 96.91% and an Area Under the Receiver Operating Characteristic curve (AUC-ROC) value of 98.85% against the combination of three datasets.

Characteristics and Range of Reviews About Technologies for Aging in Place: Scoping Review of Reviews

BACKGROUND

It is a contemporary and global challenge that the increasing number of older people requiring care will surpass the available caregivers. Solutions are needed to help older people maintain their health, prevent disability, and delay or avoid dependency on others. Technology can enable older people to age in place while maintaining their dignity and quality of life. Literature reviews on this topic have become important tools for researchers, practitioners, policy makers, and decision makers who need to navigate and access the extensive available evidence. Due to the large number and diversity of existing reviews, there is a need for a review of reviews that provides an overview of the range and characteristics of the evidence on technology for aging in place.

OBJECTIVE

This study aimed to explore the characteristics and the range of evidence on technologies for aging in place by conducting a scoping review of reviews and presenting an evidence map that researchers, policy makers, and practitioners may use to identify gaps and reviews of interest.

METHODS

The review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Literature searches were conducted in Web of Science, PubMed, and Scopus using a search string that consisted of the terms "older people" and "technology for ageing in place," with alternate terms using Boolean operators and truncation, adapted to the rules for each database.

RESULTS

A total of 5447 studies were screened, with 344 studies included after full-text screening. The number of reviews on this topic has increased dramatically over time, and the literature is scattered across a variety of journals. Vocabularies and approaches used to describe technology, populations, and problems are highly heterogeneous. We have identified 3 principal ways that reviews have dealt with populations, 5 strategies that the reviews draw on to conceptualize technology, and 4 principal types of problems that they have dealt with. These may be understood as methods that can inform future reviews on this topic. The relationships among populations, technologies, and problems studied in the reviews are presented in an evidence map that includes pertinent gaps.

CONCLUSIONS

Redundancies and unexploited synergies between bodies of evidence on technology for aging in place are highly likely. These results can be used to decrease this risk if they are used to inform the design of future reviews on this topic. There is a need for an examination of the current state of the art in knowledge on technology for aging in place in low- and middle-income countries, especially in Africa.

The Application of Internet of Things for the Elderly Health Safety: A Systematic Review

The elderly population is projected to increase from 8.5% in 2015 to 12% in 2030 and 16% in 2050. This growing demographic is chronically vulnerable to various age-related diseases and injuries like falling, leading to long-term pain, disability, or death. Thus, there is a need to use the potential of novel technologies to support the elderly regarding patient safety matters in particular. Internet of Things (IoT) has recently been introduced to improve the lifestyle of the elderly. This study aimed to evaluate the studies that have researched the use of the IoT for elderly patients' safety through performance metrics, accuracy, sensitivity, and specificity. We conducted a systematic review on the research question. To do this, we searched PubMed, EMBASE, Web of Science, Scopus, Google Scholar, and Science Direct databases by combining the related keywords. A data extraction form was used for data gathering through which English, full-text articles on the use of the IoT for the safety of elderly patients were included. The support vector machine technique has the most frequency of use compared to other techniques. Motion sensors were the most widely used type. The United States with four studies had the highest frequencies. The performance of IoT to ensure the elderly's safety was relatively good. It, however, needs to reach a stage of maturity for universal use.

Evidence and User Considerations of Home Health Monitoring for Older Adults: Scoping Review

BACKGROUND

Home health monitoring shows promise in improving health outcomes; however, navigating the literature remains challenging given the breadth of evidence. There is a need to summarize the effectiveness of monitoring across health domains and identify gaps in the literature. In addition, ethical and user-centered frameworks are important to maximize the acceptability of health monitoring technologies.

OBJECTIVE

This review aimed to summarize the clinical evidence on home-based health monitoring through a scoping review and outline ethical and user concerns and discuss the challenges of the current user-oriented conceptual frameworks.

METHODS

A total of 2 literature reviews were conducted. We conducted a scoping review of systematic reviews in Scopus, MEDLINE, Embase, and CINAHL in July 2021. We included reviews examining the effectiveness of home-based health monitoring in older adults. The exclusion criteria included reviews with no clinical outcomes and lack of monitoring interventions (mobile health, telephone, video interventions, virtual reality, and robots). We conducted a quality assessment using the Assessment of Multiple Systematic Reviews (AMSTAR-2). We organized the outcomes by disease and summarized the type of outcomes as positive, inconclusive, or negative. Second, we conducted a literature review including both systematic reviews and original articles to identify ethical concerns and user-centered frameworks for smart home technology. The search was halted after saturation of the basic themes presented.

RESULTS

The scoping review found 822 systematic reviews, of which 94 (11%) were included and of those, 23 (24%) were of medium or high quality. Of these 23 studies, monitoring for heart failure or chronic obstructive pulmonary disease reduced exacerbations (4/7, 57%) and hospitalizations (5/6, 83%); improved hemoglobin A1c (1/2, 50%); improved safety for older adults at home and detected changing cognitive status (2/3, 66%) reviews; and improved physical activity, motor control in stroke, and pain in arthritis in (3/3, 100%) rehabilitation studies. The second literature review on ethics and user-centered frameworks found 19 papers focused on ethical concerns, with privacy (12/19, 63%), autonomy (12/19, 63%), and control (10/19, 53%) being the most common. An additional 7 user-centered frameworks were studied.

CONCLUSIONS

Home health monitoring can improve health outcomes in heart failure, chronic obstructive pulmonary disease, and diabetes and increase physical activity, although review quality and consistency were limited. Long-term generalized monitoring has the least amount of evidence and requires further study. The concept of trade-offs between technology usefulness and acceptability is critical to consider, as older adults have a hierarchy of concerns. Implementing user-oriented frameworks can allow long-term and larger studies to be conducted to improve the evidence base for monitoring and increase the receptiveness of clinicians, policy makers, and end users.

Health Emergencies, Falls, and Use of Communication Technologies by Older People with Functional and Social Frailty: Ageing in Place in Deprived Areas of Italy

Frail older people ageing alone in place need help to perform daily living activities, especially when functional limitations are increasing and formal/informal supports are lacking. This context represents a risk of experiencing health emergencies, in particular falls. It is thus important to understand how seniors manage these potential difficulties and who helps them. The present study aimed to explore these dimensions in Italy, where 120 qualitative interviews were carried out in 2019 within the "Inclusive ageing in place" (IN-AGE) research project, involving frail older people living alone at home. A content analysis was conducted. Results showed that seniors need to manage health emergencies regarding heart and breathing problems but mainly episodes of falls are reported, with consequent fractures and fear of falling again. In several cases, the use of a mobile phone was crucial in order to seek for help, and the first to intervene were children, in addition to some neighbors. Some seniors also referred their ability to call independently the General Practitioner (GP) or the emergency room, in order to not disturb family members. These findings highlight new useful insights for policy makers, regarding health emergencies prevention and management measures to put in place, especially concerning falls, and the support provided by communication technologies.

Patient Acceptability of a Novel Technological Solution (Ambient Intelligent Geriatric Management System) to Prevent Falls in Geriatric and General Medicine Wards: A Mixed-Methods Study

Introduction

As effective interventions to prevent inpatient falls are lacking, a novel technological intervention was trialed. The Ambient Intelligent Geriatric Management (AmbIGeM) system used wearable sensors that detected and alerted staff of patient movements requiring supervision. While the system did not reduce falls rate, it is important to evaluate the acceptability, usability, and safety of the AmbIGeM system, from the perspectives of patients and informal carers.

Methods

We conducted a mixed-methods study using semistructured interviews, a pre-survey and post-survey. The AmbIGeM clinical trial was conducted in two geriatric evaluation and management units and a general medical ward, in two Australian hospitals, and a subset of participants were recruited. Within 3 days of being admitted to the study wards and enrolling in the trial, 31 participants completed the pre-survey. Prior to discharge (post-intervention), 30 participants completed the post-survey and 27 participants were interviewed. Interview data were thematically analyzed and survey data were descriptively analyzed.

Results

Survey and interview participants had an average age of 83 (SD 9) years, 65% were female, and 41% were admitted with a fall. Participants considered the AmbIGeM system a good idea. Most but not all thought the singlet and sensor component as acceptable and comfortable, with no privacy concerns. Participants felt reassured with extra monitoring, although sometimes misunderstood the purpose of AmbIGeM as detecting patient falls. Participants' acceptability was strongly positive, with median 8+ (0–10 scale) on pre- and post-surveys.

Discussion/Conclusion

Patients' acceptability is important to optimize outcomes. Overall older patients considered the AmbIGeM system as acceptable, usable, and improving safety. The findings will be important to guide refinement of this and other similar technology developments.

Near-Fall Detection in Unexpected Slips during Over-Ground Locomotion with Body-Worn Sensors among Older Adults

Slip-induced falls are a growing health concern for older adults, and near-fall events are associated with an increased risk of falling. To detect older adults at a high risk of slip-related falls, this study aimed to develop models for near-fall event detection based on accelerometry data collected by body-fixed sensors. Thirty-four healthy older adults who experienced 24 laboratory-induced slips were included. The slip outcomes were first identified as loss of balance (LOB) and no LOB (NLOB), and then the kinematic measures were compared between these two outcomes. Next, all the slip trials were split into a training set (90%) and a test set (10%) at sample level. The training set was used to train both machine learning models (n = 2) and deep learning models (n = 2), and the test set was used to evaluate the performance of each model. Our results indicated that the deep learning models showed higher accuracy for both LOB (>64%) and NLOB (>90%) classifications than the machine learning models. Among all the models, the Inception model showed the highest classification accuracy (87.5%) and the largest area under the receiver operating characteristic curve (AUC), indicating that the model is an effective method for near-fall (LOB) detection. Our approach can be helpful in identifying individuals at the risk of slip-related falls before they experience an actual fall.

Rationale for withholding professional resuscitation in emergency medical system-attended out-of-hospital cardiac arrest

BACKGROUND

Half of out-of-hospital cardiac arrests (OHCA) are deemed inappropriate for resuscitation by emergency medical services (EMS). We investigated patient characteristics and reasons for non-treatment of OHCAs, and determined the proportion involving illicit drug use.

METHODS

We reviewed consecutive EMS-untreated OHCA from the British Columbia Cardiac Arrest Registry (2019-2020). We abstracted patient characteristics and categorized reasons for EMS non-treatment: (1) prolonged interval from the OHCA to EMS arrival ("non-recent OHCA") with or without signs of "obvious death"; (2) do-not-resuscitate (DNR) order; (3) terminal disease; (4) verbal directive; and (5) unspecified. We abstracted clinical details regarding a history of, or evidence at the scene of, illicit drug use.

RESULTS

Of 13 331 cases, 5959 (45%) were not treated by EMS. The median age was 67 (IQR 54-81) and 1903 (32%) were female. EMS withheld resuscitation due to: non-recent OHCA, with and without signs of "obvious death" in 4749 (80%) and 108 (1.8%), respectively; DNR order in 952 (16%); terminal disease in 77 (1.3%); family directive in 41 (0.69%); and unspecified in 32 (0.54%). Overall and among those with non-recent OHCA, 695/5959 (12%) and 691/4857 (14%) had either a history of or evidence of recent illicit drug use, respectively.

CONCLUSION

A prolonged interval from the OHCA until EMS assessment was the predominant reason for withholding treatment. Innovative solutions to decrease this interval may increase the proportion of OHCA that are treated by EMS and overall outcomes. Targeted interventions for illicit-drug use-related OHCAs may add additional benefit.

Falls Detection and Prevention Systems in Home Care for Older Adults: Myth or Reality?

There is an exponential increase in the range of digital products and devices promoting aging in place, in particular, devices aiming at preventing or detecting falls. However, their deployment is still limited and only few studies have been carried out in population-based settings owing to the technological challenges that remain to be overcome and the barriers that are specific to the users themselves, such as the generational digital divide and acceptability factors specific to the older adult population. To date, scarce studies consider these factors. To capitalize technological progress, the future step should be to better consider these factors and to deploy, in a broader and more ecological way, these technologies designed for older adults receiving home care to assess their effectiveness in real life.

Are wearable devices effective for preventing and detecting falls: an umbrella review (a review of systematic reviews)

Background

Falls are a common and serious health issue facing the global population, causing an estimated 646,000 deaths per year globally. Wearable devices typically combine accelerometers, gyroscopes and even barometers; using the data collected and inputting this into an algorithm that decides whether a fall has occurred. The purpose of this umbrella review was to provide a comprehensive overview of the systematic reviews on the effectiveness of wearable electronic devices for falls detection in adults.

Methods

MEDLINE, Embase, Cochrane Database of Systematic Reviews (CDSR), and CINAHL, were searched from their inceptions until April 2019 for systematic reviews that assessed the accuracy of wearable technology in the detection of falls.

Results

Seven systematic reviews were included in this review. Due to heterogeneity between the included systematic reviews in their methods and their reporting of results, a meta-analysis could not be performed. Most devices tested used accelerometers, often in combination with gyroscopes. Three systematic reviews reported an average sensitivity of 93.1% or greater and an average specificity of 86.4% or greater for the detection of falls. Placing sensors on the trunk, foot or leg appears to provide the highest accuracy for falls detection, with multiple sensors increasing the accuracy, specificity, and sensitivity of these devices.

Conclusions

This review demonstrated that wearable device technology offers a low-cost and accurate way to effectively detect falls and summon for help. There are significant differences in the effectiveness of these devices depending on the type of device and its placement. Further high-quality research is needed to confirm the accuracy of these devices in frail older people in real-world settings.

Feasibility of a continuous, multi-sensor remote health monitoring approach in persons living with neurodegenerative disease

BACKGROUND

Remote health monitoring with wearable sensor technology may positively impact patient self-management and clinical care. In individuals with complex health conditions, multi-sensor wear may yield meaningful information about health-related behaviors. Despite available technology, feasibility of device-wearing in daily life has received little attention in persons with physical or cognitive limitations. This mixed methods study assessed the feasibility of continuous, multi-sensor wear in persons with cerebrovascular (CVD) or neurodegenerative disease (NDD).

METHODS

Thirty-nine participants with CVD, Alzheimer's disease/amnestic mild cognitive impairment, frontotemporal dementia, Parkinson's disease, or amyotrophic lateral sclerosis (median age 68 (45-83) years, 36% female) wore five devices (bilateral ankles and wrists, chest) continuously for a 7-day period. Adherence to device wearing was quantified by examining volume and pattern of device removal (non-wear). A thematic analysis of semi-structured de-brief interviews with participants and study partners was used to examine user acceptance.

RESULTS

Adherence to multi-sensor wear, defined as a minimum of three devices worn concurrently, was high (median 98.2% of the study period). Non-wear rates were low across all sensor locations (median 17-22 min/day), with significant differences between some locations (p = 0.006). Multi-sensor non-wear was higher for daytime versus nighttime wear (p < 0.001) and there was a small but significant increase in non-wear over the collection period (p = 0.04). Feedback from de-brief interviews suggested that multi-sensor wear was generally well accepted by both participants and study partners.

CONCLUSION

A continuous, multi-sensor remote health monitoring approach is feasible in a cohort of persons with CVD or NDD.

Accuracy of Kinovea software in estimating body segment movements during falls captured on standard video: Effects of fall direction, camera perspective and video calibration technique

Falls are a major cause of unintentional injuries. Understanding the movements of the body during falls is important to the design of fall prevention and management strategies, including exercise programs, mobility aids, fall detectors, protective gear, and safer environments. Video footage of real-life falls is increasingly available, and may be used with digitization software to extract kinematic features of falls. We examined the validity of this approach by conducting laboratory falling experiments, and comparing linear and angular positions and velocities measured from 3D motion capture to estimates from Kinovea 2D digitization software based on standard surveillance video (30 Hz, 640x480 pixels). We also examined how Kinovea accuracy depended on fall direction, camera angle, filtering cut-off frequency, and calibration technique. For a camera oriented perpendicular to the plane of the fall (90 degrees), Kinovea position data filtered at 10 Hz, and video calibration using a 2D grid, mean root mean square errors were 0.050 m or 9% of the signal amplitude and 0.22 m/s (7%) for vertical position and velocity, and 0.035 m (6%) and 0.16 m/s (7%) for horizontal position and velocity. Errors in angular measures averaged over 2-fold higher in sideways than forward or backward falls, due to out-of-plane movement of the knees and elbows. Errors in horizontal velocity were 2.5-fold higher for a 30 than 90 degree camera angle, and 1.6-fold higher for calibration using participants’ height (1D) instead of a 2D grid. When compared to 10 Hz, filtering at 3 Hz caused velocity errors to increase 1.4-fold. Our results demonstrate that Kinovea can be applied to 30 Hz video to measure linear positions and velocities to within 9% accuracy. Lower accuracy was observed for angular kinematics of the upper and lower limb in sideways falls, and for horizontal measures from 30 degree cameras or 1D height-based calibration.

A smartphone-based online system for fall detection with alert notifications and contextual information of real-life falls

BACKGROUND

Falls are a leading cause of accidental deaths and injuries worldwide. The risk of falling is especially high for individuals suffering from balance impairments. Retrospective surveys and studies of simulated falling in lab conditions are frequently used and are informative, but prospective information about real-life falls remains sparse. Such data are essential to address fall risks and develop fall detection and alert systems. Here we present the results of a prospective study investigating a proof-of-concept, smartphone-based, online system for fall detection and notification.

METHODS

The system uses the smartphone's accelerometer and gyroscope to monitor the participants' motion, and falls are detected using a regularized logistic regression. Data on falls and near-fall events (i.e., stumbles) is stored in a cloud server and fall-related variables are logged onto a web portal developed for data exploration, including the event time and weather, fall probability, and the faller's location and activity before the fall.

RESULTS

In total, 23 individuals with an elevated risk of falling carried the phones for 2070 days in which the model classified 14,904,000 events. The system detected 27 of the 37 falls that occurred (sensitivity = 73.0 %) and resulted in one false alarm every 46 days (specificity > 99.9 %, precision = 37.5 %). 42.2 % of the events falsely classified as falls were validated as stumbles.

CONCLUSIONS

The system's performance shows the potential of using smartphones for fall detection and notification in real-life. Apart from functioning as a practical fall monitoring instrument, this system may serve as a valuable research tool, enable future studies to scale their ability to capture fall-related data, and help researchers and clinicians to investigate real-falls.

Data about fall events and ordinary daily activities from a sensorized smart floor

A smart floor with 16 embedded pressure sensors was used to record 420 simulated fall events performed by 60 volunteers. Each participant performed seven fall events selected from the guidelines defined in a previous study. Raw data were grouped and well organized in CSV format.

The data was collected for the development of a non-intrusive fall detection solution based on the smart floor. Indeed, the collected data can be used to further improve the current solution by proposing new fall detection techniques for the correct identification of accidental fall events on the smart floor.

The gathered fall simulation data is associated with participants’ demographic characteristics, useful for future expansions of the smart floor solution beyond the fall detection problem.

Exploring patterns of personal alarm system use and impacts on outcomes

OBJECTIVES

To describe the patterns of personal emergency response systems (PERS) use in a statewide cohort of older Australians.

METHODS

PERS data from clients enrolled in the Personal Alarm Victoria program between January 2014 and June 2017 were analysed. Alarm activation reasons were extracted, and a medical record audit was performed for a sub-cohort of patients admitted to a local hospital following an alarm event. Descriptive statistics were used.

RESULTS

There were 42,180 clients enrolled during the study (mean age 80 years, 80% female, 93% living alone). An ambulance attended 44% of the fall-related events and 81% of events coded as unwell. Activation reasons were distributed equally between a fall and feeling unwell, and a repeating pattern of activation reasons was observed. In our sub-cohort (n = 92), the majority of admissions (86%) followed an alarm activation coded as unwell.

CONCLUSION

We demonstrated recurring patterns associated with the reasons for alarm use.

Utilising LiDAR for fall detection

Autonomous driving generates several low‐cost technologies, such as light detection and ranging (LiDAR). Due to this, the LiDAR technology has experienced impressive performance improvements. Our ambition is to capitalise on this development, where LiDAR is considered as the enabling technology for a non‐invasive monitoring system for securing elder persons in their home. A motivation for technology‐based securing of elder persons is that many countries experience a demographic change. Traditional personal care by care worker or re‐location to special homes of elder persons does not scale due to the shrinking fraction of the working population. Technology can reduce some of the burden. This article proposes and assesses technology for securing a person's home. However, securing a person, based on monitoring, requires careful design because the technology should be non‐invasive, reliable and low cost. LiDAR technology offers several crucial qualities that meet these system requirements. This article provides a proof of concept for a low‐cost, non‐invasive LiDAR‐based monitoring system. Our proposed system can detect if a person has fallen, and it can trigger an alarm to the care services when required. We emphasise especially that our monitoring solution can operate in the bathroom and even in the shower.

Usefulness of Mobile Devices in the Diagnosis and Rehabilitation of Patients with Dizziness and Balance Disorders: A State of the Art Review

Objective

The gold standard for objective body posture examination is posturography. Body movements are detected through the use of force platforms that assess static and dynamic balance (conventional posturography). In recent years, new technologies like wearable sensors (mobile posturography) have been applied during complex dynamic activities to diagnose and rehabilitate balance disorders. They are used in healthy people, especially in the aging population, for detecting falls in the older adults, in the rehabilitation of different neurological, osteoarticular, and muscular system diseases, and in vestibular disorders. Mobile devices are portable, lightweight, and less expensive than conventional posturography. The vibrotactile system can consist of an accelerometer (linear acceleration measurement), gyroscopes (angular acceleration measurement), and magnetometers (heading measurement, relative to the Earth’s magnetic field). The sensors may be mounted to the trunk (most often in the lumbar region of the spine, and the pelvis), wrists, arms, sternum, feet, or shins. Some static and dynamic clinical tests have been performed with the use of wearable sensors. Smartphones are widely used as a mobile computing platform and to evaluate the results or monitor the patient during the movement and rehabilitation. There are various mobile applications for smartphone-based balance systems. Future research should focus on validating the sensitivity and reliability of mobile device measurements compared to conventional posturography.

Conclusion

Smartphone based mobile devices are limited to one sensor lumbar level posturography and offer basic clinical evaluation. Single or multi sensor mobile posturography is available from different manufacturers and offers single to multi-level measurements, providing more data and in some instances even performing sophisticated clinical balance tests.

An IoT Smart Environment in Support of Disease Diagnosis Decentralization

The percentage of seniors in the global population is constantly growing and solutions in the field of fall detection and early detection of neuro-degenerative pathologies have a crucial role in order to increase life expectancy and quality of life. This study aims to extend fall detection and effective recognition of early signs of diseases to new smart environments, conceiving the decentralization of diagnostic monitoring in everyday life activities in a more pervasive paradigm. Inspiring to research outcomes, in this work an architecture is designed to detect falls in crowded indoor environments during events/exhibitions, for favoring a timely and effective intervention. It also foresees a continue monitoring of the gait for seniors during the visit, thus extracting key features which are stored on a dedicated database. The proposed solution allows third party researchers to perform analysis on the obtained gait datasets, through the adoption of advanced data-mining techniques for the detection of early signs of neuro-degenerative diseases and other pathologies. The architecture designed here aims to provide a step forward concerning the extension of smart monitoring environments for the detection of falls and early signs of pathologies in everyday life, in a more pervasive and decentralized paradigm.

Deep Learning Based Fall Detection Algorithms for Embedded Systems, Smartwatches, and IoT Devices Using Accelerometers

A fall of an elderly person often leads to serious injuries or even death. Many falls occur in the home environment and remain unrecognized. Therefore, a reliable fall detection is absolutely necessary for a fast help. Wrist-worn accelerometer based fall detection systems are developed, but the accuracy and precision are not standardized, comparable, or sometimes even known. In this work, we present an overview about existing public databases with sensor based fall datasets and harmonize existing wrist-worn datasets for a broader and robust evaluation. Furthermore, we are analyzing the current possible recognition rate of fall detection using deep learning algorithms for mobile and embedded systems. The presented results and databases can be used for further research and optimizations in order to increase the recognition rate to enhance the independent life of the elderly. Furthermore, we give an outlook for a convenient application and wrist device.

Implementing an intelligent video monitoring system to detect falls of older adults at home: a multiple case study

Purpose

Older adults are at a high risk of falling. The consequences of falls are worse when the person is unable to get up afterward. Thus, an intelligent video monitoring system (IVS) was developed to detect falls and send alerts to a respondent. This study aims to explore the implementation of the IVS at home.

Design/methodology/approach

A multiple case study was conducted with four dyads: older adults and informal caregivers. The IVS was implemented for two months at home. Perceptions of the IVS and technical variables were documented. Interviews were thematically analyzed, and technical data were descriptively analyzed.

Findings

The rate of false alarms was 0.35 per day. Participants had positive opinions of the IVS and mentioned its ease of use. They also made suggestions for improvement.

Originality/value

This study showed the feasibility of a two-month implementation of this IVS. Its development should be continued and tested with a larger experimental group.

Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls

Falling is a significant health problem. Fall detection, to alert for medical attention, has been gaining increasing attention. Still, most of the existing studies use falls simulated in a laboratory environment to test the obtained performance. We analyzed the acceleration signals recorded by an inertial sensor on the lower back during 143 real-world falls (the most extensive collection to date) from the FARSEEING repository. Such data were obtained from continuous real-world monitoring of subjects with a moderate-to-high risk of falling. We designed and tested fall detection algorithms using features inspired by a multiphase fall model and a machine learning approach. The obtained results suggest that algorithms can learn effectively from features extracted from a multiphase fall model, consistently overperforming more conventional features. The most promising method (support vector machines and features from the multiphase fall model) obtained a sensitivity higher than 80%, a false alarm rate per hour of 0.56, and an F-measure of 64.6%. The reported results and methodologies represent an advancement of knowledge on real-world fall detection and suggest useful metrics for characterizing fall detection systems for real-world use.

CNN-Based Fall Detection Strategy with Edge Computing Scheduling in Smart Cities

The livelihood problem, especially the medical wisdom, has played an important role during the process of the building of smart cities. For the medical wisdom, the fall detection has attracted the considerable attention from the global researchers and medical institutions. It is very difficult for the traditional fall detection strategies to realize the intelligent detection with the following three reasons: (i) the data collection cannot reach the real-time level; (ii) the adopted detection methods cannot satisfy the enough stability; and (iii) the computation overhead of collection device is very high, which causes the barely satisfactory detection effect. Therefore, this paper proposes Convolutional Neural Network (CNN)-based fall detection strategy with edge computing consideration, where the global network view ability of Software-Defined Networking (SDN) is used to collect the generated data from smartphone. Meanwhile, on one hand, the edge computing is exploited to put some computation tasks at the edge server by the scheduling technique. On the other hand, CNN is equipped with both edge server and smartphone, and it is leveraged to train the related data and further give the guidance of fall detection. The experimental results show that the novel fall detection strategy has a more accurate rate, transmission delay, and stability than two cutting-edge strategies.

Privacy-Preserved Fall Detection Method with Three-Dimensional Convolutional Neural Network Using Low-Resolution Infrared Array Sensor

Due to the rapid aging of the population in recent years, the number of elderly people in hospitals and nursing homes is increasing, which results in a shortage of staff. Therefore, the situation of elderly citizens requires real-time attention, especially when dangerous situations such as falls occur. If staff cannot find and deal with them promptly, it might become a serious problem. For such a situation, many kinds of human motion detection systems have been in development, many of which are based on portable devices attached to a user’s body or external sensing devices such as cameras. However, portable devices can be inconvenient for users, while optical cameras are affected by lighting conditions and face privacy issues. In this study, a human motion detection system using a low-resolution infrared array sensor was developed to protect the safety and privacy of people who need to be cared for in hospitals and nursing homes. The proposed system can overcome the above limitations and have a wide range of application. The system can detect eight kinds of motions, of which falling is the most dangerous, by using a three-dimensional convolutional neural network. As a result of experiments of 16 participants and cross-validations of fall detection, the proposed method could achieve 98.8% and 94.9% of accuracy and F1-measure, respectively. They were 1% and 3.6% higher than those of a long short-term memory network, and show feasibility of real-time practical application.

Artificial Intelligence-Powered Digital Health Platform and Wearable Devices Improve Outcomes for Older Adults in Assisted Living Communities: Pilot Intervention Study

BACKGROUND

Wearables and artificial intelligence (AI)-powered digital health platforms that utilize machine learning algorithms can autonomously measure a senior's change in activity and behavior and may be useful tools for proactive interventions that target modifiable risk factors.

OBJECTIVE

The goal of this study was to analyze how a wearable device and AI-powered digital health platform could provide improved health outcomes for older adults in assisted living communities.

METHODS

Data from 490 residents from six assisted living communities were analyzed retrospectively over 24 months. The intervention group (+CP) consisted of 3 communities that utilized CarePredict (n=256), and the control group (-CP) consisted of 3 communities (n=234) that did not utilize CarePredict. The following outcomes were measured and compared to baseline: hospitalization rate, fall rate, length of stay (LOS), and staff response time.

RESULTS

The residents of the +CP and -CP communities exhibit no statistical difference in age (P=.64), sex (P=.63), and staff service hours per resident (P=.94). The data show that the +CP communities exhibited a 39% lower hospitalization rate (P=.02), a 69% lower fall rate (P=.01), and a 67% greater length of stay (P=.03) than the -CP communities. The staff alert acknowledgment and reach resident times also improved in the +CP communities by 37% (P=.02) and 40% (P=.02), respectively.

CONCLUSIONS

The AI-powered digital health platform provides the community staff with actionable information regarding each resident's activities and behavior, which can be used to identify older adults that are at an increased risk for a health decline. Staff can use this data to intervene much earlier, protecting seniors from conditions that left untreated could result in hospitalization. In summary, the use of wearables and AI-powered digital health platform can contribute to improved health outcomes for seniors in assisted living communities. The accuracy of the system will be further validated in a larger trial.

Illuminating the dark spaces of healthcare with ambient intelligence

Advances in machine learning and contactless sensors have given rise to ambient intelligence-physical spaces that are sensitive and responsive to the presence of humans. Here we review how this technology could improve our understanding of the metaphorically dark, unobserved spaces of healthcare. In hospital spaces, early applications could soon enable more efficient clinical workflows and improved patient safety in intensive care units and operating rooms. In daily living spaces, ambient intelligence could prolong the independence of older individuals and improve the management of individuals with a chronic disease by understanding everyday behaviour. Similar to other technologies, transformation into clinical applications at scale must overcome challenges such as rigorous clinical validation, appropriate data privacy and model transparency. Thoughtful use of this technology would enable us to understand the complex interplay between the physical environment and health-critical human behaviours.

Care Transition Decisions After a Fall-related Emergency Department Visit: A Qualitative Study of Patients' and Caregivers' Experiences

OBJECTIVE

Falls are a leading cause of injury-related emergency department (ED) visits and may serve as a sentinel event for older adults, leading to physical and psychological injury. Our primary objective was to characterize patient- and caregiver-specific perspectives about care transitions after a fall.

METHODS

Using a semistructured interview guide, we conducted in-depth, qualitative interviews using grounded theory methodology. We included patients enrolled in the Geriatric Acute and Post-acute Fall Prevention Intervention (GAPcare) trial aged 65 years and older who had an ED visit for a fall and their caregivers. Patients with cognitive impairment (CI) were interviewed in patient-caregiver dyads. Domains assessed included the postfall recovery period, the skilled nursing facility (SNF) placement decision-making process, and the ease of obtaining outpatient follow-up. Interviews were audio-recorded, transcribed verbatim, and coded and analyzed for a priori and emergent themes.

RESULTS

A total of 22 interviews were completed with 10 patients, eight caregivers, and four patient-caregiver dyads within the 6-month period after initial ED visits. Patients were on average 83 years old, nine of 14 were female, and two of 14 had CI. Six of 12 caregivers were interviewed in reference to a patient with CI. We identified four overarching themes: 1) the fall as a trigger for psychological and physiological change, 2) SNF placement decision-making process, 3) direct effect of fall on caregivers, and 4) barriers to receipt of recommended follow-up.

CONCLUSIONS

Older adults presenting to the ED after a fall report physical limitations and a prominent fear of falling after their injury. Caregivers play a vital role in securing the home environment; the SNF placement decision-making process; and navigating the transition of care between the ED, SNF, and outpatient visits after a fall. Clinicians should anticipate and address feelings of isolation, changes in mobility, and fear of falling in older adults seeking ED care after a fall.

An Assistive Technology Solution for User Activity Monitoring Exploiting Passive RFID

Population ageing is having a direct influence on serious health issues, including hampered mobility and physical decline. Good habits in performing physical activities, in addition to eating and drinking, are essential to improve the life quality of the elderly population. Technological solutions, aiming at increasing awareness or providing reminders to eat/drink regularly, can have a significant impact in this scenario. These solutions enable the possibility to constantly monitor deviations from users' normal behavior, thus allowing reminders to be provided to users/caregivers. In this context, this paper presents a radio-frequency identification (RFID) system to monitor user's habits, such as the use of food, beverages, and/or drugs. The device was optimized to fulfill specifications imposed by the addressed application. The approach could be extended for the monitoring of home appliances, environment exploitation, and activity rate. Advantages of the approach compared to other solutions, e.g., based on cameras, are related to the low level of invasiveness and flexibility of the adopted technology. A major contribution of this paper is related to the wide investigation of system behavior, which is aimed to define the optimal working conditions of the system, with regards to the power budget, user (antenna)-tag reading range, and the optimal inter-tag distance. To investigate the performance of the system in tag detection, experiments were performed in a scenario replicating a home environment. To achieve this aim, specificity and sensitivity indexes were computed to provide an objective evaluation of the system performance. For the case considered, if proper conditions are meet, a specificity value of 0.9 and a sensitivity value of 1 were estimated.

Possible Life Saver: A Review on Human Fall Detection Technology

Among humans, falls are a serious health problem causing severe injuries and even death for the elderly population. Besides, falls are also a major safety threat to bikers, skiers, construction workers, and others. Fortunately, with the advancements of technologies, the number of proposed fall detection systems and devices has increased dramatically and some of them are already in the market. Fall detection devices/systems can be categorized based on their architectures as wearable devices, ambient systems, image processing-based systems, and hybrid systems, which employ a combination of two or more of these methodologies. In this review paper, a comparison is made among these major fall detection systems, devices, and algorithms in terms of their proposed approaches and measure of performance. Issues with the current systems such as lack of portability and reliability are presented as well. Development trends such as the use of smartphones, machine learning, and EEG are recognized. Challenges with privacy issues, limited real fall data, and ergonomic design deficiency are also discussed.

Fall definitions, faller classifications and outcomes used in falls research among people with multiple sclerosis: a systematic review

Purpose: To identify the definitions of a fall, faller classifications and outcomes used in prospectively-recorded falls research among people with Multiple Sclerosis (MS).Methods: A systematic review of peer-reviewed journal articles was conducted using electronic databases. Relevant data were extracted by one reviewer and verified by a second independent reviewer.Results: Twenty-six papers met the inclusion criteria. A relative degree of heterogeneity existed amongst studies for the outcomes of interest to this review. Thirteen different fall definitions were identified. Fourteen different falls outcomes were used across the included studies, with six of these reported by only one study each. Data regarding injurious falls were presented by only eight papers. The majority (n = 17) of papers classified individuals as a faller if they fell at least once.Conclusions: This review highlights the large variation in fall definitions, faller classifications and outcomes used in this research field. This hinders cross-comparison and pooling of data, thereby preventing researchers and clinicians from drawing conclusive findings from existing literature. The creation of an international standard for the definition of a fall, faller classification and falls outcomes would allow for transparent and coordinated falls research for people with MS, facilitating progression in this research field.Implications for rehabilitationFalls are a common occurrence among people with Multiple Sclerosis (MS) resulting in numerous negative consequences.There is large heterogeneity in the definitions, methods and outcomes used in falls research for people with MS.This lack of standardisation prevents the accurate cross-comparison and pooling of data, impeding the identification of falls risk factors and effective falls prevention interventions for people with MS.Consequently, clinicians should interpret the outcomes of falls research for people with MS with caution, particularly when comparing studies regarding falls risk assessments and falls prevention interventions for use in clinical practice.

Elderly Fall Detection Systems: A Literature Survey

Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial.

Detection of Near Falls Using Wearable Devices: A Systematic Review

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 (i) people at high risk of falling, (ii) activities of daily living, (iii) both near falls and actual falls, and (iv) naturally occurring near falls.

Semi-Automatic Calibration Method for a Bed-Monitoring System Using Infrared Image Depth Sensors

With the aging of society, the number of fall accidents has increased in hospitals and care facilities, and some accidents have happened around beds. To help prevent accidents, mats and clip sensors have been used in these facilities but they can be invasive, and their purpose may be misinterpreted. In recent years, research has been conducted using an infrared-image depth sensor as a bed-monitoring system for detecting a patient getting up, exiting the bed, and/or falling; however, some manual calibration was required initially to set up the sensor in each instance. We propose a bed-monitoring system that retains the infrared-image depth sensors but uses semi-automatic rather than manual calibration in each situation where it is applied. Our automated methods robustly calculate the bed region, surrounding floor, sensor location, and attitude, and can recognize the spatial position of the patient even when the sensor is attached but unconstrained. Also, we propose a means to reconfigure the spatial position considering occlusion by parts of the bed and also accounting for the gravity center of the patient’s body. Experimental results of multi-view calibration and motion simulation showed that our methods were effective for recognition of the spatial position of the patient.

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.

Home-based monitoring of falls using wearable sensors in Parkinson's disease

INTRODUCTION

Falling is among the most serious clinical problems in Parkinson's disease (PD). We used body-worn sensors (falls detector worn as a necklace) to quantify the hazard ratio of falls in PD patients in real life.

METHODS

We matched all 2063 elderly individuals with self-reported PD to 2063 elderly individuals without PD based on age, gender, comorbidity, and living conditions. We analyzed fall events collected at home via a wearable sensor. Fall events were collected either automatically using the wearable falls detector or were registered by a button push on the same device. We extracted fall events from a 2.5-year window, with an average follow-up of 1.1 years. All falls included were confirmed immediately by a subsequent telephone call. The outcomes evaluated were (1) incidence rate of any fall, (2) incidence rate of a new fall after enrollment (ie, hazard ratio), and (3) 1-year cumulative incidence of falling.

RESULTS

The incidence rate of any fall was higher among self-reported PD patients than controls (2.1 vs. 0.7 falls/person, respectively; P < .0001). The incidence rate of a new fall after enrollment (ie, hazard ratio) was 1.8 times higher for self-reported PD patients than controls (95% confidence interval, 1.6-2.0).

CONCLUSION

Having PD nearly doubles the incidence of falling in real life. These findings highlight PD as a prime "falling disease." The results also point to the feasibility of using body-worn sensors to monitor falls in daily life. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Health technologies for falls prevention and detection in adult hospital in-patients: a scoping review protocol

REVIEW OBJECTIVE/QUESTIONS

The objective of this scoping review is to map the evidence relating to the reporting and evaluation of health technologies for the prevention and detection of falls in adult hospital in-patients. The following questions will guide this scoping review.

Optimization and Technical Validation of the AIDE-MOI Fall Detection Algorithm in a Real-Life Setting with Older Adults

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 split-up 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.

Smartwatches Can Detect Walker and Cane Use in Older Adults

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 cross-validation.

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. Noncustomized smartwatch classifiers (ie, an "off-the-shelf" system) had greater than 90% accuracy for 10 of the 14 participants. A noncustomized 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.

Follow-up efficacy of physical exercise interventions on fall incidence and fall risk in healthy older adults: a systematic review and meta-analysis

Background

The risk of falling and associated injuries increases with age. Therefore, the prevention of falls is a key priority in geriatrics and is particularly based on physical exercising, aiming to improve the age-related decline in motor performance, which is crucial in response to postural threats. Although the benefits and specifications of effective exercise programs have been well documented in pre-post design studies, that is during the treatment, the definitive retention and transfer of these fall-related exercise benefits to the daily life fall risk during follow-up periods remains largely unclear. Accordingly, this meta-analysis investigates the efficacy of exercise interventions on the follow-up risk of falling.

Methods

A systematic database search was conducted. A study was considered eligible if it examined the number of falls (fall rate) and fallers (fall risk) of healthy older adults (≥ 65 years) during a follow-up period after participating in a randomized controlled physical exercise intervention. The pooled estimates of the fall rate and fall risk ratios were calculated using a random-effects meta-analysis. Furthermore, the methodological quality and the risk of bias were assessed.

Results

Twenty-six studies with 31 different intervention groups were included (4739 participants). The number of falls was significantly (p <0.001) reduced by 32% (rate ratio 0.68, 95% confidence interval 0.58 to 0.80) and the number of fallers by 22% (risk ratio 0.78, 95% confidence interval 0.68 to 0.89) following exercising when compared with controls. Interventions that applied posture-challenging exercises showed the highest effects. The methodological quality score was acceptable (73 ± 11%) and risk of bias low.

Conclusions

The present review and meta-analysis provide evidence that physical exercise interventions have the potential to significantly reduce fall rate and risk in healthy older adults. Posture-challenging exercises might be particularly considered when designing fall prevention interventions.

Electronic supplementary material

The online version of this article (10.1186/s40798-018-0170-z) contains supplementary material, which is available to authorized users.

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.

Feasibility of Automated Mobility Assessment of Older Adults via an Instrumented Cane

This study explored the feasibility of automated characterization of functional mobility via an Instrumented Cane System (ICS) within an older adult sample of cane users. An off-the-shelf offset cane was instrumented with inertial, force, and ultrasound sensors for noninvasive data collection. Eighteen patients from a neurological out-patient rehabilitation clinic and nine independently mobile controls participated in standard clinical evaluations of mobility using the ICS while under the care of an attending physical therapist. Feasibility of the ICS was gauged through two studies. The first demonstrated the capability of the ICS to reliably collect meaningful usage metrics, and the second provided preliminary support for the discriminability of high and low falls risk from system-reported metrics. Specifically, the cane significantly differentiated patients and controls (p < 0.05), and a measure of the variation in rotational velocity was associated with total scores on the Functional Gait Assessment (partial r = 0.61, p < 0.01). These findings may ultimately serve to complement and even extend current clinical assessment practices.

SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning

This paper presents SmartFall, an Android app that uses accelerometer data collected from a commodity-based smartwatch Internet of Things (IoT) device to detect falls. The smartwatch is paired with a smartphone that runs the SmartFall application, which performs the computation necessary for the prediction of falls in real time without incurring latency in communicating with a cloud server, while also preserving data privacy. We experimented with both traditional (Support Vector Machine and Naive Bayes) and non-traditional (Deep Learning) machine learning algorithms for the creation of fall detection models using three different fall datasets (Smartwatch, Notch, Farseeing). Our results show that a Deep Learning model for fall detection generally outperforms more traditional models across the three datasets. This is attributed to the Deep Learning model’s ability to automatically learn subtle features from the raw accelerometer data that are not available to Naive Bayes and Support Vector Machine, which are restricted to learning from a small set of extracted features manually specified. Furthermore, the Deep Learning model exhibits a better ability to generalize to new users when predicting falls, an important quality of any model that is to be successful in the real world. We also present a three-layer open IoT system architecture used in SmartFall, which can be easily adapted for the collection and analysis of other sensor data modalities (e.g., heart rate, skin temperature, walking patterns) that enables remote monitoring of a subject’s wellbeing.

Usability of a wearable fall detection prototype from the perspective of older people-A real field testing approach

AIMS AND OBJECTIVES

Community-dwelling older people were involved in the testing of a fall detection device to improve its utilisation and acceptance in everyday life.

BACKGROUND

The usability of alerting devices remains unsatisfactory, as they are scarcely utilised by older people, despite wide recognition of the importance of rapid assistance after a fall. Moreover, the time a person remains on the floor negatively impacts the severity of fall consequences. However, it is unclear how to increase alerting device utilisation in everyday life. Therefore, older people were involved in this research to consider their perspective during prototype development.

DESIGN

A qualitative focus group study was conducted, following a real field testing approach, underpinned by the theoretical framework "Medical Device Technology Development Process."

METHODS

Fifteen community-dwelling older people tested the prototype in daily living over a period of nine days. Different means of involvement were exploited such as "user seminars" or "discussion with users." On day 9, data were collected using focus groups and analysed with qualitative content analysis.

RESULTS

The participants' perspectives yielded positive aspects of the prototype along with aspects requiring improvement. They indicated that technical requirements are essential. They also revealed that a minimal change in daily routines, support for physical activity and independent living and the inclusion of trusted contact persons could lead to wider use of the alerting device.

CONCLUSIONS

Involving users is crucial in gaining a deeper understanding of aspects influencing utilisation of an alerting device. The study revealed that usability is influenced both by technical requirements as well as habits and personal preferences. This finding is vital, as habits and personal preferences can only be identified through the involvement of target users.

RELEVANCE TO CLINICAL PRACTICE

The study provides key insights for health practitioners interested in promoting the use of an alerting device in community-dwelling older people.

Methods for the Real-World Evaluation of Fall Detection Technology: A Scoping Review

Falls in older adults present a major growing healthcare challenge and reliable detection of falls is crucial to minimise their consequences. The majority of development and testing has used laboratory simulations. As simulations do not cover the wide range of real-world scenarios performance is poor when retested using real-world data. There has been a move from the use of simulated falls towards the use of real-world data. This review aims to assess the current methods for real-world evaluation of fall detection systems, identify their limitations and propose improved robust methods of evaluation. Twenty-two articles met the inclusion criteria and were assessed with regard to the composition of the datasets, data processing methods and the measures of performance. Real-world tests of fall detection technology are inherently challenging and it is clear the field is in its infancy. Most studies used small datasets and studies differed on how to quantify the ability to avoid false alarms and how to identify non-falls, a concept which is virtually impossible to define and standardise. To increase robustness and make results comparable, larger standardised datasets are needed containing data from a range of participant groups. Measures that depend on the definition and identification of non-falls should be avoided. Sensitivity, precision and F-measure emerged as the most suitable robust measures for evaluating the real-world performance of fall detection systems.