Novel sensing technology in fall risk assessment in older adults: a systematic review

Performance of digital technologies in assessing fall risks among older adults with cognitive impairment: a systematic review

Older adults with cognitive impairment (CI) are twice as likely to fall compared to the general older adult population. Traditional fall risk assessments may not be suitable for older adults with CI due to their reliance on attention and recall. Hence, there is an interest in using objective technology-based fall risk assessment tools to assess falls within this population. This systematic review aims to evaluate the features and performance of technology-based fall risk assessment tools for older adults with CI. A systematic search was conducted across several databases such as PubMed and IEEE Xplore, resulting in the inclusion of 22 studies. Most studies focused on participants with dementia. The technologies included sensors, mobile applications, motion capture, and virtual reality. Fall risk assessments were conducted in the community, laboratory, and institutional settings; with studies incorporating continuous monitoring of older adults in everyday environments. Studies used a combination of technology-based inputs of gait parameters, socio-demographic indicators, and clinical assessments. However, many missed the opportunity to include cognitive performance inputs as predictors to fall risk. The findings of this review support the use of technology-based fall risk assessment tools for older adults with CI. Further advancements incorporating cognitive measures and additional longitudinal studies are needed to improve the effectiveness and clinical applications of these assessment tools. Additional work is also required to compare the performance of existing methods for fall risk assessment, technology-based fall risk assessments, and the combination of these approaches.

Bidirectional association between falls and multimorbidity in middle-aged and elderly Chinese adults: a national longitudinal study

This study explores the bidirectional association between multimorbidity and falls in Chinese middle-aged and elderly adults. Participants aged 45 and above from the China Health and Retirement Longitudinal Study were included. Binary logistic regression assessed the impact of chronic conditions on fall incidence (stage I), while multinomial logistic regression examined the relationship between baseline falls and multimorbidity (stage II). The fully adjusted odds ratios (ORs) for one, two, or three or more chronic conditions were 1.34, 1.65, and 2.02, respectively. Among participants without baseline falls, 28.61% developed two or more chronic conditions during follow-up, compared to 37.4% of those with a history of falls. Fully adjusted ORs for one, two, or three or more chronic conditions in those with a history of falls were 1.21, 1.38 and 1.70, respectively. The bidirectional relationship held in sensitivity and subgroup analyses. A bidirectional relationship exists between multimorbidity and falls in Chinese middle-aged and elderly adults. Strengthening chronic condition screening and treatment in primary healthcare may reduce falls risk, and prioritizing fall prevention and intervention in daily life is recommended.

Identifying sensors-based parameters associated with fall risk in community-dwelling older adults: an investigation and interpretation of discriminatory parameters

BACKGROUND

Falls pose a severe threat to the health of older adults worldwide. Determining gait and kinematic parameters that are related to an increased risk of falls is essential for developing effective intervention and fall prevention strategies. This study aimed to investigate the discriminatory parameter, which lay an important basis for developing effective clinical screening tools for identifying high-fall-risk older adults.

METHODS

Forty-one individuals aged 65 years and above living in the community participated in this study. The older adults were classified as high-fall-risk and low-fall-risk individuals based on their BBS scores. The participants wore an inertial measurement unit (IMU) while conducting the Timed Up and Go (TUG) test. Simultaneously, a depth camera acquired images of the participants' movements during the experiment. After segmenting the data according to subtasks, 142 parameters were extracted from the sensor-based data. A t-test or Mann-Whitney U test was performed on the parameters for distinguishing older adults at high risk of falling. The logistic regression was used to further quantify the role of different parameters in identifying high-fall-risk individuals. Furthermore, we conducted an ablation experiment to explore the complementary information offered by the two sensors.

RESULTS

Fifteen participants were defined as high-fall-risk individuals, while twenty-six were defined as low-fall-risk individuals. 17 parameters were tested for significance with p-values less than 0.05. Some of these parameters, such as the usage of walking assistance, maximum angular velocity around the yaw axis during turn-to-sit, and step length, exhibit the greatest discriminatory abilities in identifying high-fall-risk individuals. Additionally, combining features from both devices for fall risk assessment resulted in a higher AUC of 0.882 compared to using each device separately.

CONCLUSIONS

Utilizing different types of sensors can offer more comprehensive information. Interpreting parameters to physiology provides deeper insights into the identification of high-fall-risk individuals. High-fall-risk individuals typically exhibited a cautious gait, such as larger step width and shorter step length during walking. Besides, we identified some abnormal gait patterns of high-fall-risk individuals compared to low-fall-risk individuals, such as less knee flexion and a tendency to tilt the pelvis forward during turning.

Digital technologies to prevent falls in people living with dementia or mild cognitive impairment: a rapid systematic overview of systematic reviews

OBJECTIVE

Falls are a common cause of potentially preventable death, disability and loss of independence with an annual estimated cost of £4.4bn. People living with dementia (PlwD) or mild cognitive impairment (MCI) have an increased fall risk. This overview evaluates evidence for technologies aiming to reduce falls and fall risk for PlwD or MCI.

METHODS

In October 2022, we searched five databases for evidence syntheses. We used standard methods to rapidly screen, extract data, assess risk of bias and overlap, and synthesise the evidence for each technology type.

RESULTS

We included seven systematic reviews, incorporating 22 relevant primary studies with 1,412 unique participants. All reviews had critical flaws on AMSTAR-2: constituent primary studies were small, heterogeneous, mostly non-randomised and assessed as low or moderate quality. Technologies assessed were: wearable sensors, environmental sensor-based systems, exergaming, virtual reality systems. We found no evidence relating to apps. Review evidence for the direct impact on falls was available only from environmental sensors, and this was inconclusive. For wearables and virtual reality technologies there was evidence that technologies may differentiate PlwD who fell from those who did not; and for exergaming that balance may be improved.

CONCLUSIONS

The evidence for technology to reduce falls and falls risk for PlwD and MCI is methodologically weak, based on small numbers of participants and often indirect. There is a need for higher-quality RCTs to provide robust evidence for effectiveness of fall prevention technologies. Such technologies should be designed with input from users and consideration of the wider implementation context.

A multifactorial fall risk assessment system for older people utilizing a low-cost, markerless Microsoft Kinect

Falls among older people are a major health concern. This study aims to develop a multifactorial fall risk assessment system for older people using a low-cost, markerless Microsoft Kinect. A Kinect-based test battery was designed to comprehensively assess major fall risk factors. A follow-up experiment was conducted with 102 older participants to assess their fall risks. Participants were divided into high and low fall risk groups based on their prospective falls over a 6-month period. Results showed that the high fall risk group performed significantly worse on the Kinect-based test battery. The developed random forest classification model achieved an average classification accuracy of 84.7%. In addition, the individual's performance was computed as the percentile value of a normative database to visualise deficiencies and targets for intervention. These findings indicate that the developed system can not only screen out 'at risk' older individuals with good accuracy, but also identify potential fall risk factors for effective fall intervention.Practitioner summary: Falls are the leading cause of injuries in older people. We newly developed a multifactorial fall risk assessment system for older people utilising a low-cost, markerless Kinect. Results showed that the developed system can screen out 'at risk' individuals and identify potential risk factors for effective fall intervention.

Assessing Gait & Balance in Adults with Mild Balance Impairment: G&B App Reliability and Validity

Smartphone applications (apps) that utilize embedded inertial sensors have the potential to provide valid and reliable estimations of different balance and gait parameters in older adults with mild balance impairment. This study aimed to assess the reliability, validity, and sensitivity of the Gait&Balance smartphone application (G&B App) for measuring gait and balance in a sample of middle- to older-aged adults with mild balance impairment in Pakistan. Community-dwelling adults over 50 years of age (N = 83, 50 female, range 50-75 years) with a Berg Balance Scale (BBS) score between 46/56 and 54/56 were included in the study. Data collection involved securing a smartphone to the participant's lumbosacral spine. Participants performed six standardized balance tasks, including four quiet stance tasks and two gait tasks (walking looking straight ahead and walking with head turns). The G&B App collected accelerometry data during these tasks, and the tasks were repeated twice to assess test-retest reliability. The tasks in quiet stance were also recorded with a force plate, a gold-standard technology for measuring postural sway. Additionally, participants completed three clinical measures, the BBS, the Functional Reach Test (FRT), and the Timed Up and Go Test (TUG). Test-retest reliability within the same session was determined using intraclass correlation coefficients (ICCs) and the standard error of measurement (SEM). Validity was evaluated by correlating the G&B App outcomes against both the force plate data and the clinical measures using Pearson's product-moment correlation coefficients. To assess the G&B App's sensitivity to differences in balance across tasks and repetitions, one-way repeated measures analyses of variance (ANOVAs) were conducted. During quiet stance, the app demonstrated moderate reliability for steadiness on firm (ICC = 0.72) and compliant surfaces (ICC = 0.75) with eyes closed. For gait tasks, the G&B App indicated moderate to excellent reliability when walking looking straight ahead for gait symmetry (ICC = 0.65), walking speed (ICC = 0.93), step length (ICC = 0.94), and step time (ICC = 0.84). The TUG correlated with app measures under both gait conditions for walking speed (r -0.70 and 0.67), step length (r -0.56 and -0.58), and step time (r 0.58 and 0.50). The BBS correlated with app measures of walking speed under both gait conditions (r 0.55 and 0.51) and step length when walking with head turns (r = 0.53). Force plate measures of total distance wandered showed adequate to excellent correlations with G&B App measures of steadiness. Notably, G&B App measures of walking speed, gait symmetry, step length, and step time, were sensitive to detecting differences in performance between standard walking and the more difficult task of walking with head turns. This study demonstrates the G&B App's potential as a reliable and valid tool for assessing some gait and balance parameters in middle-to-older age adults, with promise for application in low-income countries like Pakistan. The app's accessibility and accuracy could enhance healthcare services and support preventive measures related to fall risk.

Types, functions and mechanisms of robot-assisted intervention for fall prevention: A systematic scoping review

BACKGROUND

Any individual may experience accidental falls, particularly older adults. Although robots can prevent falls, knowledge of their fall-preventive use is limited.

OBJECTIVE

To explore the types, functions, and mechanisms of robot-assisted intervention for fall prevention.

METHODS

A systematic scoping review of global literature published from inception to January 2022 was conducted according to Arksey and O'Malley's five-step framework. Nine electronic databases, namely, PubMed, Embase, CINAHL, IEEE Xplore, the Cochrane Library, Scopus, Web of Science, PsycINFO, and ProQuest, were searched.

RESULTS

Seventy-one articles were found with developmental (n = 63), pilot (n = 4), survey (n = 3), and proof-of-concept (n = 1) designs across 14 countries. Six types of robot-assisted intervention were found, namely cane robots, walkers, wearables, prosthetics, exoskeletons, rollators, and other miscellaneous. Five main functions were observed including (i) detection of user fall, (ii) estimation of user state, (iii) estimation of user motion, (iv) estimation of user intentional direction, and (v) detection of user balance loss. Two categories of mechanisms of robots were found. The first category was executing initiation of incipient fall prevention such as modeling, measurement of user-robot distance, estimation of center of gravity, estimation and detection of user state, estimation of user intentional direction, and measurement of angle. The second category was achieving actualization of incipient fall prevention such as adjust optimal posture, automated braking, physical support, provision of assistive force, reposition, and control of bending angle.

CONCLUSIONS

Existing literature regarding robot-assisted intervention for fall prevention is in its infancy. Therefore, future research is required to assess its feasibility and effectiveness.

Predictors of fall risk in older adults using the G-STRIDE inertial sensor: an observational multicenter case-control study

BACKGROUND

There are a lot of tools to use for fall assessment, but there is not yet one that predicts the risk of falls in the elderly. This study aims to evaluate the use of the G-STRIDE prototype in the analysis of fall risk, defining the cut-off points to predict the risk of falling and developing a predictive model that allows discriminating between subjects with and without fall risks and those at risk of future falls.

METHODS

An observational, multicenter case-control study was conducted with older people coming from two different public hospitals and three different nursing homes. We gathered clinical variables ( Short Physical Performance Battery (SPPB), Standardized Frailty Criteria, Speed 4 m walk, Falls Efficacy Scale-International (FES-I), Time-Up Go Test, and Global Deterioration Scale (GDS)) and measured gait kinematics using an inertial measure unit (IMU). We performed a logistic regression model using a training set of observations (70% of the participants) to predict the probability of falls.

RESULTS

A total of 163 participants were included, 86 people with gait and balance disorders or falls and 77 without falls; 67,8% were females, with a mean age of 82,63 ± 6,01 years. G-STRIDE made it possible to measure gait parameters under normal living conditions. There are 46 cut-off values of conventional clinical parameters and those estimated with the G-STRIDE solution. A logistic regression mixed model, with four conventional and 2 kinematic variables allows us to identify people at risk of falls showing good predictive value with AUC of 77,6% (sensitivity 0,773 y specificity 0,780). In addition, we could predict the fallers in the test group (30% observations not in the model) with similar performance to conventional methods.

CONCLUSIONS

The G-STRIDE IMU device allows to predict the risk of falls using a mixed model with an accuracy of 0,776 with similar performance to conventional model. This approach allows better precision, low cost and less infrastructures for an early intervention and prevention of future falls.

Clinical Static Balance Assessment: A Narrative Review of Traditional and IMU-Based Posturography in Older Adults and Individuals with Incomplete Spinal Cord Injury

Maintaining a stable upright posture is essential for performing activities of daily living, and impaired standing balance may impact an individual's quality of life. Therefore, accurate and sensitive methods for assessing static balance are crucial for identifying balance impairments, understanding the underlying mechanisms of the balance deficiencies, and developing targeted interventions to improve standing balance and prevent falls. This review paper first explores the methods to quantify standing balance. Then, it reviews traditional posturography and recent advancements in using wearable inertial measurement units (IMUs) to assess static balance in two populations: older adults and those with incomplete spinal cord injury (iSCI). The inclusion of these two groups is supported by their large representation among individuals with balance impairments. Also, each group exhibits distinct aspects in balance assessment due to diverse underlying causes associated with aging and neurological impairment. Given the high vulnerability of both demographics to balance impairments and falls, the significance of targeted interventions to improve standing balance and mitigate fall risk becomes apparent. Overall, this review highlights the importance of static balance assessment and the potential of emerging methods and technologies to improve our understanding of postural control in different populations.

"Fall Risk Scoring" in Outpatient Gait Analysis: Validation of a New Fall Risk Assessment for Nursing Home Residents

Falls in senior home residents are common. Individual preventive training can lower the fall risk. To detect the need for training, a systematic assessment of the individual fall risk is needed. The aim of this study was thus to assess whether a fall risk score based on free field insole measurements can distinguish between an at-risk group of senior home residents and a healthy young control group. A published fall risk score was used in senior home residents over the age of 75 and a young (< 40 years) control group to determine the individual fall risk. In addition, the fall events over 12 months were assessed. Statistical analysis including ROC analysis was performed to determine the ability of the score to detect participants at heightened fall risk. In total, 18 nursing home residents and 9 young control participants were included. Of the nursing home residents, 15 had at least one fall, with a total of 37 falls recorded over 12 months. In the control group, no falls were recorded. The fall risk score was significantly different between nursing home residents and the control group (9.2 + 3.2 vs. 5.7 ± 2.2). Furthermore, the score significantly differentiated fallers from non-fallers (10.3 ± 1.8 vs. 5.2 ± 2.5), with a cut-off > 7.5 (AUC: 0.95) and a sensitivity of 86.7% (specificity 83.3%). The fall risk score is able to detect the difference between senior nursing home residents and young, healthy controls, as well as between fallers and non-fallers. Its main proof of concept is demonstrated, as based on movement data outside special gait labs, and it can simplify the risk of fall determination in geriatric nursing home residents and can now be used in further, prospective studies.

Design of a Sensor-Technology-Augmented Gait and Balance Monitoring System for Community-Dwelling Older Adults in Hong Kong: A Pilot Feasibility Study

Routine assessments of gait and balance have been recognized as an effective approach for preventing falls by issuing early warnings and implementing appropriate interventions. However, current limited public healthcare resources cannot meet the demand for continuous monitoring of deteriorations in gait and balance. The objective of this study was to develop and evaluate the feasibility of a prototype surrogate system driven by sensor technology and multi-sourced heterogeneous data analytics, for gait and balance assessment and monitoring. The system was designed to analyze users' multi-mode data streams collected via inertial sensors and a depth camera while performing a 3-m timed up and go test, a five-times-sit-to-stand test, and a Romberg test, for predicting scores on clinical measurements by physiotherapists. Generalized regression of sensor data was conducted to build prediction models for gait and balance estimations. Demographic correlations with user acceptance behaviors were analyzed using ordinal logistic regression. Forty-four older adults (38 females) were recruited in this pilot study (mean age = 78.5 years, standard deviation [SD] = 6.2 years). The participants perceived that using the system for their gait and balance monitoring was a good idea (mean = 5.45, SD = 0.76) and easy (mean = 4.95, SD = 1.09), and that the system is useful in improving their health (mean = 5.32, SD = 0.83), is trustworthy (mean = 5.04, SD = 0.88), and has a good fit between task and technology (mean = 4.97, SD = 0.84). In general, the participants showed a positive intention to use the proposed system in their gait and balance management (mean = 5.22, SD = 1.10). Demographic correlations with user acceptance are discussed. This study provides preliminary evidence supporting the feasibility of using a sensor-technology-augmented system to manage the gait and balance of community-dwelling older adults. The intervention is validated as being acceptable, viable, and valuable.

The Movement Deviation Profile Can Differentiate Faller and Non-Faller Older Adults

BACKGROUND

The World Health Organization considers falls the second leading cause of death by accidental injury worldwide and one of the most frequent complications in older adults during activities of daily living. Several tasks related to fall risk have been individually assessed describing kinematic changes in older adults. The study proposal was to identify which functional task differentiates faller and non-faller older adults using the movement deviation profile (MDP).

METHODS

This cross-sectional study recruited 68 older adults aged ≥60 years by convenience sampling. Older adults were divided into 2 groups: with and without a history of falls (34 older adults in each group). The MDP analyzed the 3-dimensional angular kinematics data of tasks (ie, gait, walking turn, stair ascent and descent, sit-to-stand, and stand-to-sit), and the Z score of the mean MDP identified which task presented the greatest difference between fallers and non-fallers. A multivariate analysis with Bonferroni post hoc verified the interaction between groups considering angular kinematic data and the cycle time of the task. Statistical significance was set at 5% (p < .05).

RESULTS

Z score of the MDPmean showed an interaction between groups (λ = 0.67, F = 5.085, p < .0001). Fallers differed significantly from non-fallers in all tasks and the greatest difference was in stair descent (Z score = 0.89). The time to complete each task was not different between groups.

CONCLUSIONS

The MDP distinguished older adult fallers from non-fallers. The stair descent task should be highlighted because it presented the greatest difference between groups.

Classification of Parkinson's disease stages with a two-stage deep neural network

Introduction

Parkinson's disease is one of the most prevalent neurodegenerative diseases. In the most advanced stages, PD produces motor dysfunction that impairs basic activities of daily living such as balance, gait, sitting, or standing. Early identification allows healthcare personnel to intervene more effectively in rehabilitation. Understanding the altered aspects and impact on the progression of the disease is important for improving the quality of life. This study proposes a two-stage neural network model for the classifying the initial stages of PD using data recorded with smartphone sensors during a modified Timed Up & Go test.

Methods

The proposed model consists on two stages: in the first stage, a semantic segmentation of the raw sensor signals classifies the activities included in the test and obtains biomechanical variables that are considered clinically relevant parameters for functional assessment. The second stage is a neural network with three input branches: one with the biomechanical variables, one with the spectrogram image of the sensor signals, and the third with the raw sensor signals.

Results

This stage employs convolutional layers and long short-term memory. The results show a mean accuracy of 99.64% for the stratified k-fold training/validation process and 100% success rate of participants in the test phase.

Discussion

The proposed model is capable of identifying the three initial stages of Parkinson's disease using a 2-min functional test. The test easy instrumentation requirements and short duration make it feasible for use feasible in the clinical context.

Depth-Imaging for Gait Analysis on a Treadmill in Older Adults at Risk of Falling

BACKGROUND

Accidental falls are a major health issue in older people. One significant and potentially modifiable risk factor is reduced gait stability. Clinicians do not have sophisticated kinematic options to measure this risk factor with simple and affordable systems. Depth-imaging with AI-pose estimation can be used for gait analysis in young healthy adults. However, is it applicable for measuring gait in older adults at a risk of falling?

METHODS

In this methodological comparison 59 older adults with and without a history of falls walked on a treadmill while their gait pattern was recorded with multiple inertial measurement units and with an Azure Kinect depth-camera. Spatiotemporal gait parameters of both systems were compared for convergent validity and with a Bland-Altman plot.

RESULTS

Correlation between systems for stride length (r=.992, [Formula: see text]) and stride time (r=0.914, [Formula: see text]) was high. Bland-Altman plots revealed a moderate agreement in stride length (-0.74 ± 3.68 cm; [-7.96 cm to 6.47 cm]) and stride time (-3.7±54 ms; [-109 ms to 102 ms]).

CONCLUSION

Gait parameters in older adults with and without a history of falls can be measured with inertial measurement units and Azure Kinect cameras. Affordable and small depth-cameras agree with IMUs for gait analysis in older adults with and without an increased risk of falling. However, tolerable accuracy is limited to the average over multiple steps of spatiotemporal parameters derived from the initial foot contact. Clinical Translation Statement- Gait parameters in older adults with and without a history of falls can be measured with inertial measurement units and Azure Kinect. Affordable and small depth-cameras, developed for various purposes in research and industry, agree with IMUs in clinical gait analysis in older adults with and without an increased risk of falling. However, tolerable accuracy to assess function or monitor changes in gait is limited to the average over multiple steps of spatiotemporal parameters derived from the initial foot contact.

Trunk range of motion: A wearable sensor-based test protocol and indicator of fall risk in older people

Conventionally, trunk range of motion (TROM) requires manual measurement by an external health professional with a general-purpose goniometer. This study aims to propose a convenient test protocol to assess TROM based on a single wearable sensor and to further investigate the relationship between TROM and fall risk of older people. We first explored the optimal sensor position by comparing TROMs from four representative locations (T1, T12, L5 and sternum) and optical motion capture system (golden reference). A follow-up experiment was conducted to evaluate the relationship between TROM and fall risk. The results showed that T12 achieved the minimum root mean square error (3.8 ± 2.2°) against the golden reference and the non-faller group had significantly higher TROMs than the faller group. These findings suggest that the newly proposed protocol is convenient yet valid and TROM can be a promising indicator of fall risk in older people.

Evaluation of a novel technology-supported fall prevention intervention - study protocol of a multi-centre randomised controlled trial in older adults at increased risk of falls

BACKGROUND

Increasing number of falls and fall-related injuries in an aging society give rise to the need for effective fall prevention and rehabilitation strategies. Besides traditional exercise approaches, new technologies show promising options for fall prevention in older adults. As a new technology-based approach, the hunova robot can support fall prevention in older adults. The objective of this study is to implement and evaluate a novel technology-supported fall prevention intervention using the hunova robot compared to an inactive control group. The presented protocol aims at introducing a two-armed, multi-centre (four sites) randomised controlled trial, evaluating the effects of this new approach on the number of falls and number of fallers as primary outcomes.

METHODS

The full clinical trial incorporates community-dwelling older adults at risk of falls with a minimum age of 65 years. Including a one-year follow-up measurement, all participants are tested four times. The training programme for the intervention group comprises 24-32 weeks in which training sessions are scheduled mostly twice a week; the first 24 training sessions use the hunova robot, these are followed by a home-based programme of 24 training sessions. Fall-related risk factors as secondary endpoints are measured using the hunova robot. For this purpose, the hunova robot measures the participants' performance in several dimensions. The test outcomes are input for the calculation of an overall score which indicates the fall risk. The hunova-based measurements are accompanied by the timed-up-and-go test as a standard test within fall prevention studies.

DISCUSSION

This study is expected to lead to new insights which may help establish a new approach to fall prevention training for older adults at risk of falls. First positive results on risk factors can be expected after the first 24 training sessions using the hunova robot. As primary outcomes, the number of falls and fallers within the study (including the one-year follow-up period) are the most relevant parameters that should be positively influenced by our new approach to fall prevention. After the study completion, approaches to examine the cost-effectiveness and develop an implementation plan are relevant aspects for further steps.

TRIAL REGISTRATION

German Clinical Trial Register (DRKS), ID: DRKS00025897. Prospectively registered 16 August 2021, https://drks.de/search/de/trial/DRKS00025897 .

Clinical Sensor-Based Fall Risk Assessment at an Orthopedic Clinic: A Case Study of the Staff's Views on Utility and Effectiveness

In-hospital falls are a serious threat to patient security and fall risk assessment (FRA) is important to identify high-risk patients. Although sensor-based FRA (SFRA) can provide objective FRA, its clinical use is very limited and research to identify meaningful SFRA methods is required. This study aimed to investigate whether examples of SFRA methods might be relevant for FRA at an orthopedic clinic. Situations where SFRA might assist FRA were identified in a focus group interview with clinical staff. Thereafter, SFRA methods were identified in a literature review of SFRA methods developed for older adults. These were screened for potential relevance in the previously identified situations. Ten SFRA methods were considered potentially relevant in the identified FRA situations. The ten SFRA methods were presented to staff at the orthopedic clinic, and they provided their views on the SFRA methods by filling out a questionnaire. Clinical staff saw that several SFRA tasks could be clinically relevant and feasible, but also identified time constraints as a major barrier for clinical use of SFRA. The study indicates that SFRA methods developed for community-dwelling older adults may be relevant also for hospital inpatients and that effectiveness and efficiency are important for clinical use of SFRA.

Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall

Nowadays, it becomes of paramount societal importance to support many frail-prone groups in our society (elderly, patients with neurodegenerative diseases, etc.) to remain socially and physically active, maintain their quality of life, and avoid their loss of autonomy. Once older people enter the prefrail stage, they are already likely to experience falls whose consequences may accelerate the deterioration of their quality of life (injuries, fear of falling, reduction of physical activity). In that context, detecting frailty and high risk of fall at an early stage is the first line of defense against the detrimental consequences of fall. The second line of defense would be to develop original protocols to detect future fallers before any fall occur. This paper briefly summarizes the current advancements and perspectives that may arise from the combination of affordable and easy-to-use non-wearable systems (force platforms, 3D tracking motion systems), wearable systems (accelerometers, gyroscopes, inertial measurement units-IMUs) with appropriate machine learning analytics, as well as the efforts to address these challenges.

Smartphones and Threshold-Based Monitoring Methods Effectively Detect Falls Remotely: A Systematic Review

In the US, at least one fall occurs in at least 28.7% of community-dwelling seniors 65 and older each year. Falls had medical costs of USD 51 billion in 2015 and are projected to reach USD 100 billion by 2030. This review aims to discuss the extent of smartphone (SP) usage in fall detection and prevention across a range of care settings. A computerized search was conducted on six electronic databases to investigate the use of remote sensing technology, wireless technology, and other related MeSH terms for detecting and preventing falls. After applying inclusion and exclusion criteria, 44 studies were included. Most of the studies targeted detecting falls, two focused on detecting and preventing falls, and one only looked at preventing falls. Accelerometers were employed in all the experiments for the detection and/or prevention of falls. The most frequent course of action following a fall event was an alarm to the guardian. Numerous studies investigated in this research used accelerometer data analysis, machine learning, and data from previous falls to devise a boundary and increase detection accuracy. SP was found to have potential as a fall detection system but is not widely implemented. Technology-based applications are being developed to protect at-risk individuals from falls, with the objective of providing more effective and efficient interventions than traditional means. Successful healthcare technology implementation requires cooperation between engineers, clinicians, and administrators.

Incorporating gerontological and geriatrics information into picture books for 9-12 year-old children: A stakeholder engagement design

This study explored the preferences of different stakeholders when translating geriatrics and gerontology concepts into children's picture books, with the aim of developing a feasible model. Following the stakeholder engagement design and qualitative method, three types of stakeholders were enrolled: medical and educational professionals (n = 9), older adults aged over 65 (n = 9), and children aged 9 to 12 (n = 7). Individual interviews and focus groups were used to collect the views of the stakeholders as a basis for revising the picture book, as well as to analyze the opinions of different stakeholders. Results show that medical professionals' recommendations focused on intellectual content (18.0%) and written verbal narratives (16.5%). Education experts tended to recommend textual verbal narratives (18.8%) and storyline (6.0%). Older adults's suggestions focused on story content (6.8%) and included detailed descriptions of older adults. Children's suggestions were focused on plot arrangement (2.3%) and text size (2.3%). Mean scores for the appropriateness of the three picture book materials increased after the stakeholder engagement, with the communication literacy picture book achieved statistical significance (p = .042). It is concluded that the stakeholder engagement design is a viable development model for achieving intergenerational understanding, realistic and theoretical goals, and bridging heterogeneity across the stakeholders.

Responsiveness of Daily Life Gait Quality Characteristics over One Year in Older Adults Who Experienced a Fall or Engaged in Balance Exercise

Gait quality characteristics obtained from daily-life accelerometry are clinically relevant for fall risk in older adults but it is unknown whether these characteristics are responsive to changes in gait quality. We aimed to test whether accelerometry-based daily-life gait quality characteristics are reliable and responsive to changes over one year in older adults who experienced a fall or an exercise intervention. One-week trunk acceleration data were collected from 522 participants (65-97 years), at baseline and after one year. We calculated median values of walking speed, regularity (sample entropy), stability (logarithmic rate of divergence per stride), and a gait quality composite score, across all 10-s gait epochs derived from one-week gait episodes. Intraclass correlation coefficients (ICC) and limits of agreement (LOA) were determined for 198 participants who did not fall nor participated in an exercise intervention during follow-up. For responsiveness to change, we determined the number of participants who fell (n = 209) or participated in an exercise intervention (n = 115) that showed a change beyond the LOA. ICCs for agreement between baseline and follow-up exceeded 0.70 for all gait quality characteristics except for vertical gait stability (ICC = 0.69, 95% CI [0.62, 0.75]) and walking speed (ICC = 0.68, 95% CI [0.62, 0.74]). Only walking speed, vertical and mediolateral gait stability changed significantly in the exercisers over one year but effect sizes were below 0.2. The characteristic associated with most fallers beyond the LOA was mediolateral sample entropy (4.8% of fallers). For the exercisers, this was gait stability in three directions and the gait quality composite score (2.6% of exercisers). The gait quality characteristics obtained by median values over one week of trunk accelerometry were not responsive to presumed changes in gait quality after a fall or an exercise intervention in older people. This is likely due to large (within subjects) differences in gait behaviour that participants show in daily life.

Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors

Mobility impairments are a common symptom of age-related degenerative diseases. Gait features can discriminate those with mobility disorders from healthy individuals, yet phenotyping specific pathologies remains challenging. This study aims to identify if gait parameters derived from two foot-mounted inertial measurement units (IMU) during the 6 min walk test (6MWT) can phenotype mobility impairment from different pathologies (Lumbar spinal stenosis (LSS)-neurogenic diseases, and knee osteoarthritis (KOA)-structural joint disease). Bilateral foot-mounted IMU data during the 6MWT were collected from patients with LSS and KOA and matched healthy controls (N = 30, 10 for each group). Eleven gait parameters representing four domains (pace, rhythm, asymmetry, variability) were derived for each minute of the 6MWT. In the entire 6MWT, gait parameters in all four domains distinguished between controls and both disease groups; however, the disease groups demonstrated no statistical differences, with a trend toward higher stride length variability in the LSS group (p = 0.057). Additional minute-by-minute comparisons identified stride length variability as a statistically significant marker between disease groups during the middle portion of 6WMT (3rd min: p ≤ 0.05; 4th min: p = 0.06). These findings demonstrate that gait variability measures are a potential biomarker to phenotype mobility impairment from different pathologies. Increased gait variability indicates loss of gait rhythmicity, a common feature in neurologic impairment of locomotor control, thus reflecting the underlying mechanism for the gait impairment in LSS. Findings from this work also identify the middle portion of the 6MWT as a potential window to detect subtle gait differences between individuals with different origins of gait impairment.

Patient-level and organizational-level factors influencing in-hospital falls

AIM

In-hospital fall is one key safety issue in a healthcare setting. Although healthcare providers apply several strategies for preventing falls, falls still occur in hospitals. The aim of this study was to investigate patient-level and organizational-level factors influencing in-hospital falls.

DESIGN

A multicentre retrospective observational study.

METHODS

This study used the national healthcare database and supplemented with organizational data obtained through a survey. Data extraction and survey were conducted between July and August 2020. A mixed-effect logistic regression model was used to analyse factors influencing in in-hospital falls.

RESULTS

A total of 43,286 patients admitted in 86 hospitals were included in this study. Fall rate was 0.85 per 1000 days. Length of stay was significantly longer for fall patients than for no-fall patients. Patient-level factors (including age, mobility impairment and surgery) and organizational-level factors (including nurse staffing and proportion of new nurses) were significant factors influencing in-hospital falls.

CONCLUSION

Since in-hospital falls increase economic burden to patients, we should consider various fall prevention strategies to reduce falls. For a strategy to be applied stably to patients, organizational factors must be supported.

IMPACT

Proactive fall management in acute settings is essential to ensure patient safety. Considering that the number of patients with fall risk is increasing due to ageing, organizational factors should be supported to provide quality nursing care for fall risk patients. Therefore, nurse leaders should primarily ensure an appropriate level of nurse staffing. They also need to make efforts to strengthen clinical competency of nurses.

Skin-Attachable Sensors for Biomedical Applications

With the growing concern about human health issues, especially during the outbreak of the COVID-19 pandemic, the demand for personalized healthcare regarding disease prevention and recovery is increasing. However, tremendous challenges lie in both limited public medical resources and costly medical diagnosis approaches. Recently, skin-attachable sensors have emerged as promising health monitoring platforms to overcome such difficulties. Owing to the advantages of good comfort and high signal-to-noise ratio, skin-attachable sensors enable household, real-time, and long-term detection of weak physiological signals to efficiently and accurately monitor human motion, heart rate, blood oxygen saturation, respiratory rate, lung and heart sound, glucose, and biomarkers in biomedical applications. To further improve the integration level of biomedical skin-attachable sensors, efforts have been made in combining multiple sensing techniques with elaborate structural designs. This review summarizes the recent advances in different functional skin-attachable sensors, which monitor physical and chemical indicators of the human body. The advantages, shortcomings, and integration strategies of different mechanisms are presented. Specially, we highlight sensors monitoring pulmonary function such as respiratory rate and blood oxygen saturation for their potential usage in the COVID-19 pandemic. Finally, the future development of skin-attachable sensors is envisioned.

Improvement of Patient Classification Using Feature Selection Applied to Bidirectional Axial Transmission

Osteoporosis is still a worldwide problem, particularly due to associated fragility fractures. Patients at risk of fracture are currently detected using the X-Ray gold standard dual-energy X-ray absorptiometry (DXA), based on a calibrated 2-D image. Different alternatives, such as 3-D X-rays, magnetic resonance imaging (MRI) or ultrasound, have been proposed, the latter having advantages of being portable and sensitive to mechanical and geometrical properties. Bidirectional axial transmission (BDAT) has been used to classify between patients with or without nontraumatic fractures using "classical" ultrasonic parameters, such as velocities, as well as cortical thickness and porosity, obtained from an inverse problems. Recently, complementary parameters acquired with structural and textural analysis of guided wave spectrum images (GWSIs) have been introduced. These parameters are not limited by solution ambiguities, as for inverse problem. The aim of the study is to improve the patient classification using a feature selection strategy for all available ultrasound features completed by clinical parameters. To this end, three classical feature ranking methods were considered: analysis of variance (ANOVA), recursive feature elimination (RFE), and extreme gradient boosting importance feature (XGBI). In order to evaluate the performance of the feature selection techniques, three classical classification methods were used: logistic regression (LR), support vector machine (SVM), and extreme gradient boosting (XGB). The database was obtained from a previous clinical study [Minonzio et al., 2019]. Results indicate that the best accuracy of 71 [66-76]% was achieved by using RFE and SVM with 22 (out of 43) ultrasonic and clinical features. This value outperformed the accuracy of 68 [64-73]% reached with 2 (out of 6) DXA and clinical features. These values open promising perspectives toward improved and generalizable classification of patients at risk of fracture.

A systems approach towards remote health-monitoring in older adults: Introducing a zero-interaction digital exhaust

Using connected sensing devices to remotely monitor health is a promising way to help transition healthcare from a rather reactive to a more precision medicine oriented proactive approach, which could be particularly relevant in the face of rapid population ageing and the challenges it poses to healthcare systems. Sensor derived digital measures of health, such as digital biomarkers or digital clinical outcome assessments, may be used to monitor health status or the risk of adverse events like falls. Current research around such digital measures has largely focused on exploring the use of few individual measures obtained through mobile devices. However, especially for long-term applications in older adults, this choice of technology may not be ideal and could further add to the digital divide. Moreover, large-scale systems biology approaches, like genomics, have already proven beneficial in precision medicine, making it plausible that the same could also hold for remote-health monitoring. In this context, we introduce and describe a zero-interaction digital exhaust: a set of 1268 digital measures that cover large parts of a person’s activity, behavior and physiology. Making this approach more inclusive of older adults, we base this set entirely on contactless, zero-interaction sensing technologies. Applying the resulting digital exhaust to real-world data, we then demonstrate the possibility to create multiple ageing relevant digital clinical outcome assessments. Paired with modern machine learning, we find these assessments to be surprisingly powerful and often on-par with mobile approaches. Lastly, we highlight the possibility to discover novel digital biomarkers based on this large-scale approach.

Improved screening of fall risk using free-living based accelerometer data

Falls are one of the most costly population health issues. Screening of older adults for fall risks can allow for earlier interventions and ultimately lead to better outcomes and reduced public health spending. This work proposes a solution to limitations in existing fall screening techniques by utilizing a hip-based accelerometer worn in free-living conditions. The work proposes techniques to extract fall risk features from periods of free-living ambulatory activity. Analysis of the proposed techniques is conducted and compared with existing screening methods using Functional Tests and Lab-based Gait Analysis. 1705 Older Adults from Umea (Sweden) were assessed. Data consisted of 1 Week of hip worn accelerometer data, gait measurements and performance metrics for 3 functional tests. Retrospective and Prospective fall data were also recorded based on the incidence of falls occurring 12 months before and after the study commencing respectively. Machine learning based experiments show accelerometer based measures perform best when predicting falls. Prospective falls had a sensitivity and specificity of 0.61 and 0.66 respectively while retrospective falls had a sensitivity and specificity of 0.61 and 0.68 respectively.

Application of Machine Learning to Predict Trajectory of the Center of Pressure (COP) Path of Postural Sway Using a Triaxial Inertial Sensor

Postural sway indicates controlling stability in response to standing balance perturbations and determines risk of falling. In order to assess balance and postural sway, costly laboratory equipment is required, making it impractical for clinical settings. The study aimed to develop a triaxial inertial sensor and apply machine learning (ML) algorithms for predicting trajectory of the center of pressure (COP) path of postural sway. Fifty-three healthy adults, with a mean age of 46 years, participated. The inertial sensor prototype was investigated for its concurrent validity relative to the COP path length obtained from the force platform measurement. Then, ML was applied to predict the COP path by using sensor-sway metrics as the input. The results of the study revealed that all variables from the sensor prototype demonstrated high concurrent validity against the COP path from the force platform measurement (ρ > 0.75; $p<0.001$ ). The agreement between sway metrics, derived from the sensor and ML algorithms, illustrated good to excellent agreement (ICC; 0.89–0.95) between COP paths from the sensor metrics, with respect to the force plate measurement. This study demonstrated that the inertial sensor, in comparison to the standard tool, would be an option for balance assessment since it is of low-cost, conveniently portable, and comparable to the accuracy of standard force platform measurement.

Intelligent Fall-Risk Assessment Based on Gait Stability and Symmetry Among Older Adults Using Tri-Axial Accelerometry

This study aimed to use the k-nearest neighbor (kNN) algorithm, which combines gait stability and symmetry derived from a normalized cross-correlation (NCC) analysis of acceleration signals from the bilateral ankles of older adults, to assess fall risk. Fifteen non-fallers and 12 recurrent fallers without clinically significant musculoskeletal and neurological diseases participated in the study. Sex, body mass index, previous falls, and the results of the 10 m walking test (10 MWT) were recorded. The acceleration of the five gait cycles from the midsection of each 10 MWT was used to calculate the unilateral NCC coefficients for gait stability and bilateral NCC coefficients for gait symmetry, and then kNN was applied for classifying non-fallers and recurrent fallers. The duration of the 10 MWT was longer among recurrent fallers than it was among non-fallers (p &lt; 0.05). Since the gait signals were acquired from tri-axial accelerometry, the kNN F1 scores with the x-axis components were 92% for non-fallers and 89% for recurrent fallers, and the root sum of squares (RSS) of the signals was 95% for non-fallers and 94% for recurrent fallers. The kNN classification on gait stability and symmetry revealed good accuracy in terms of distinguishing non-fallers and recurrent fallers. Specifically, it was concluded that the RSS-based NCC coefficients can serve as effective gait features to assess the risk of falls.

Fall Risk Assessment for the Elderly Based on Weak Foot Features of Wearable Plantar Pressure

The high fall rate of the elderly brings enormous challenges to families and the medical system; therefore, early risk assessment and intervention are quite necessary. Compared to other sensor-based technologies, in-shoe plantar pressure sensors, effectiveness and low obtrusiveness are widely used for long-term fall risk assessments because of their portability. While frequently-used bipedal center-of-pressure (COP) features are derived from a pressure sensing platform, they are not suitable for the shoe system or pressure insole owing to the lack of relative position information. Therefore, in this study, a definition of "weak foot" was proposed to solve the sensitivity problem of single foot features and facilitate the extraction of temporal consistency related features. Forty-four multi-dimensional weak foot features based on single foot COP were correspondingly extracted; notably, the relationship between the fall risk and temporal inconsistency in the weak foot were discussed in this study, and probability distribution method was used to analyze the symmetry and temporal consistency of gait lines. Though experiments, foot pressure data were collected from 48 subjects with 24 high risk (HR) and 24 low risk (LR) ones obtained by the smart footwear system. The final models with 87.5% accuracy and 100% sensitivity on test data outperformed the base line models using bipedal COP. The results and feature space shown the novel features of wearable plantar pressure could comprehensively evaluate the difference between HR and LR groups. Our fall risk assessment models based on these features had good generalization performance, and showed practicability and reliability in real-life monitoring situations.

Using human factors and ergonomics principles to prevent inpatient falls

Inpatient falls are frequently reported incidents in hospitals around the world. The recent COVID-19 pandemic has further exacerbated the risk. With the rising importance of human factors and ergonomics (HF&E), a fall prevention programme was introduced by applying HF&E principles to reduce inpatient falls from a systems engineering perspective. The programme was conducted in an acute public hospital with around 750 inpatient beds in Hong Kong. A hospital falls review team (the team) was formed in June 2020 to plan and implement the programme. The ‘Define, Measure, Analyse, Improve and Control’ (DMAIC) method was adopted. Improvement actions following each fall review were implemented. Fall rates in the ‘pre-COVID-19’ period (January–December 2019), ‘COVID-19’ period (January–June 2020) and ‘programme’ period (July 2020–August 2021) were used for evaluation of the programme effectiveness. A total of 120, 85 and 142 inpatient falls in the ‘pre-COVID-19’, ‘COVID-19’ and ‘programme’ periods were reviewed, respectively. Thirteen areas with fall risks were identified by the team where improvement actions applying HF&E principles were implemented accordingly. The average fall rates were 0.476, 0.773 and 0.547 per 1000 patient bed days in these periods, respectively. The average fall rates were found to be significantly increased from the pre-COVID-19 to COVID-19 periods (mean difference=0.297 (95% CI 0.068 to 0.526), p=0.009), which demonstrated that the COVID-19 pandemic might have affected the hospitals fall rates, while a significant decrease was noted between the COVID-19 and programme periods (mean difference=−0.226 (95% CI −0.449 to –0.003), p=0.047), which proved that the programme in apply HF&E principles to prevent falls was effective. Since HF&E principles are universal, the programme can be generalised to other healthcare institutes, which the participation of staff trained in HF&E in the quality improvement team is vital to its success.

Using Low-Resolution Non-Invasive Infrared Sensors to Classify Activities and Falls in Older Adults

The population is aging worldwide, creating new challenges to the quality of life of older adults and their families. Falls are an increasing, but not inevitable, threat to older adults. Information technologies provide several solutions to address falls, but smart homes and the most available solutions require expensive and invasive infrastructures. In this study, we propose a novel approach to classify and detect falls of older adults in their homes through low-resolution infrared sensors that are affordable, non-intrusive, do not disturb privacy, and are more acceptable to older adults. Using data collected between 2019 and 2020 with the eHomeseniors platform, we determine activity scores of older adults moving across two rooms in a house and represent an older adult fall through skeletonization. We find that our twofold approach effectively detects activity patterns and precisely identifies falls. Our study provides insights to physicians about the daily activities of their older adults and could potentially help them make decisions in case of abnormal behavior.

The Microsoft HoloLens 2 Provides Accurate Measures of Gait, Turning, and Functional Mobility in Healthy Adults

Augmented-reality (AR) headsets, such as the Microsoft HoloLens 2 (HL2), have the potential to be the next generation of wearable technology as they provide interactive digital stimuli in the context of ecologically-valid daily activities while containing inertial measurement units (IMUs) to objectively quantify the movements of the user. A necessary precursor to the widespread utilization of the HL2 in the fields of movement science and rehabilitation is the rigorous validation of its capacity to generate biomechanical outcomes comparable to gold standard outcomes. This project sought to determine equivalency of kinematic outcomes characterizing lower-extremity function derived from the HL2 and three-dimensional (3D) motion capture systems (MoCap). Sixty-six healthy adults completed two lower-extremity tasks while kinematic data were collected from the HL2 and MoCap: (1) continuous walking and (2) timed up-and-go (TUG). For all the continuous walking metrics (cumulative distance, time, number of steps, step and stride length, and velocity), equivalence testing indicated that the HL2 and MoCap were statistically equivalent (error ≤ 5%). The TUG metrics, including turn duration and turn velocity, were also statistically equivalent between the two systems. The accurate quantification of gait and turning using a wearable such as the HL2 provides initial evidence for its use as a platform for the development and delivery of gait and mobility assessments, including the in-person and remote delivery of highly salient digital movement assessments and rehabilitation protocols.

Automatic and Efficient Fall Risk Assessment Based on Machine Learning

Automating fall risk assessment, in an efficient, non-invasive manner, specifically in the elderly population, serves as an efficient means for implementing wide screening of individuals for fall risk and determining their need for participation in fall prevention programs. We present an automated and efficient system for fall risk assessment based on a multi-depth camera human motion tracking system, which captures patients performing the well-known and validated Berg Balance Scale (BBS). Trained machine learning classifiers predict the patient’s 14 scores of the BBS by extracting spatio-temporal features from the captured human motion records. Additionally, we used machine learning tools to develop fall risk predictors that enable reducing the number of BBS tasks required to assess fall risk, from 14 to 4–6 tasks, without compromising the quality and accuracy of the BBS assessment. The reduced battery, termed Efficient-BBS (E-BBS), can be performed by physiotherapists in a traditional setting or deployed using our automated system, allowing an efficient and effective BBS evaluation. We report on a pilot study, run in a major hospital, including accuracy and statistical evaluations. We show the accuracy and confidence levels of the E-BBS, as well as the average number of BBS tasks required to reach the accuracy thresholds. The trained E-BBS system was shown to reduce the number of tasks in the BBS test by approximately 50% while maintaining 97% accuracy. The presented approach enables a wide screening of individuals for fall risk in a manner that does not require significant time or resources from the medical community. Furthermore, the technology and machine learning algorithms can be implemented on other batteries of medical tests and evaluations.

Analysis on the Subdivision of Skilled Mowing Movements on Slopes

Owing to the aging of the rural population in the hilly and mountainous areas of Japan, mowing on narrow ridges and steep slopes is done manually by the elderly—individuals over 65 years of age. Studies have shown that many accidents that occurred during mowing were caused by workers’ unstable posture, especially when mowing on steep surfaces where there is a high risk of falling. It is necessary to analyze the body movements of mowing workers to elucidate the elements related to the risk of falls. Therefore, in this study, based on a high-precision motion-capture device and a series of experiments with elderly, skilled mowing workers, we focused on the movements of mowing. We sought to identify effective and safe mowing patterns and the factors that lead to the risk of falls. In various mowing styles, compared to the stride (S) and downward (D) mowing patterns, the basic (B) and moving (M) patterns were the most efficient; however, the risk of falls was also the highest among these patterns. While mowing, workers need to pay more attention to their arm strength and take appropriate measures to reduce the risk of falls according to their age and physique. The results can be used as data for the development of fall-detection systems and offer useful insights for the training of new mowing workers.

Pathway of Trends and Technologies in Fall Detection: A Systematic Review

Falling is one of the most serious health risk problems throughout the world for elderly people. Considerable expenses are allocated for the treatment of after-fall injuries and emergency services after a fall. Fall risks and their effects would be substantially reduced if a fall is predicted or detected accurately on time and prevented by providing timely help. Various methods have been proposed to prevent or predict falls in elderly people. This paper systematically reviews all the publications, projects, and patents around the world in the field of fall prediction, fall detection, and fall prevention. The related works are categorized based on the methodology which they used, their types, and their achievements.

Introducing CatchU™: A Novel Multisensory Tool for Assessing Patients' Risk of Falling

To date, only a few studies have investigated the clinical translational value of multisensory integration. Our previous research has linked the magnitude of visual-somatosensory integration (measured behaviorally using simple reaction time tasks) to important cognitive (attention) and motor (balance, gait, and falls) outcomes in healthy older adults. While multisensory integration effects have been measured across a wide array of populations using various sensory combinations and different neuroscience research approaches, multisensory integration tests have not been systematically implemented in clinical settings. We recently developed a step-by-step protocol for administering and calculating multisensory integration effects to facilitate innovative and novel translational research across diverse clinical populations and age-ranges. In recognizing that patients with severe medical conditions and/or mobility limitations often experience difficulty traveling to research facilities or joining time-demanding research protocols, we deemed it necessary for patients to be able to benefit from multisensory testing. Using an established protocol and methodology, we developed a multisensory falls-screening tool called CatchU ^™ (an iPhone app) to quantify multisensory integration performance in clinical practice that is currently undergoing validation studies. Our goal is to facilitate the identification of patients who are at increased risk of falls and promote physician-initiated falls counseling during clinical visits (e.g., annual wellness, sick, or follow-up visits). This will thereby raise falls-awareness and foster physician efforts to alleviate disability, promote independence, and increase quality of life for our older adults. This conceptual overview highlights the potential of multisensory integration in predicting clinical outcomes from a research perspective, while also showcasing the practical application of a multisensory screening tool in routine clinical practice.

Automatic Recognition and Analysis of Balance Activity in Community-Dwelling Older Adults: Algorithm Validation

BACKGROUND

Clinical mobility and balance assessments identify older adults who have a high risk of falls in clinics. In the past two decades, sensors have been a popular supplement to mobility and balance assessment to provide quantitative information and a cost-effective solution in the community environment. Nonetheless, the current sensor-based balance assessment relies on manual observation or motion-specific features to identify motions of research interest.

OBJECTIVE

The objective of this study was to develop an automatic motion data analytics framework using signal data collected from an inertial sensor for balance activity analysis in community-dwelling older adults.

METHODS

In total, 59 community-dwelling older adults (19 males and 40 females; mean age = 81.86 years, SD 6.95 years) were recruited in this study. Data were collected using a body-worn inertial measurement unit (including an accelerometer and a gyroscope) at the L4 vertebra of each individual. After data preprocessing and motion detection via a convolutional long short-term memory (LSTM) neural network, a one-class support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighborhood (k-NN) were adopted to classify high-risk individuals.

RESULTS

The framework developed in this study yielded mean accuracies of 87%, 86%, and 89% in detecting sit-to-stand, turning 360°, and stand-to-sit motions, respectively. The balance assessment classification showed accuracies of 90%, 92%, and 86% in classifying abnormal sit-to-stand, turning 360°, and stand-to-sit motions, respectively, using Tinetti Performance Oriented Mobility Assessment-Balance (POMA-B) criteria by the one-class SVM and k-NN.

CONCLUSIONS

The sensor-based approach presented in this study provided a time-effective manner with less human efforts to identify and preprocess the inertial signal and thus enabled an efficient balance assessment tool for medical professionals. In the long run, the approach may offer a flexible solution to relieve the community's burden of continuous health monitoring.

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.

Human Behavior Recognition Model Based on Feature and Classifier Selection

With the rapid development of the computer and sensor field, inertial sensor data have been widely used in human activity recognition. At present, most relevant studies divide human activities into basic actions and transitional actions, in which basic actions are classified by unified features, while transitional actions usually use context information to determine the category. For the existing single method that cannot well realize human activity recognition, this paper proposes a human activity classification and recognition model based on smartphone inertial sensor data. The model fully considers the feature differences of different properties of actions, uses a fixed sliding window to segment the human activity data of inertial sensors with different attributes and, finally, extracts the features and recognizes them on different classifiers. The experimental results show that dynamic and transitional actions could obtain the best recognition performance on support vector machines, while static actions could obtain better classification effects on ensemble classifiers; as for feature selection, the frequency-domain feature used in dynamic action had a high recognition rate, up to 99.35%. When time-domain features were used for static and transitional actions, higher recognition rates were obtained, 98.40% and 91.98%, respectively.

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.

Development and validation of a continuous fall risk score in community-dwelling older people: an ecological approach

BACKGROUND

Fall risk assessment in older people is of major importance for providing adequate preventive measures. Current predictive models are mainly focused on intrinsic risk factors and do not adjust for contextual exposure. The validity and utility of continuous risk scores have already been demonstrated in clinical practice in several diseases. In this study, we aimed to develop and validate an intrinsic-exposure continuous fall risk score (cFRs) for community-dwelling older people through standardized residuals.

METHODS

Self-reported falls in the last year were recorded from 504 older persons (391 women: age 73.1 ± 6.5 years; 113 men: age 74.0 ± 6.1 years). Participants were categorized as occasional fallers (falls ≤1) or recurrent fallers (≥ 2 falls). The cFRs was derived for each participant by summing the standardized residuals (Z-scores) of the intrinsic fall risk factors and exposure factors. Receiver operating characteristic (ROC) analysis was used to determine the accuracy of the cFRs for identifying recurrent fallers.

RESULTS

The cFRs varied according to the number of reported falls; it was lowest in the group with no falls (- 1.66 ± 2.59), higher in the group with one fall (0.05 ± 3.13, p < 0.001), and highest in the group with recurrent fallers (2.82 ± 3.94, p < 0.001). The cFRs cutoff level yielding the maximal sensitivity and specificity for identifying recurrent fallers was 1.14, with an area under the ROC curve of 0.790 (95% confidence interval: 0.746-0.833; p < 0.001).

CONCLUSIONS

The cFRs was shown to be a valid dynamic multifactorial fall risk assessment tool for epidemiological analyses and clinical practice. Moreover, the potential for the cFRs to become a widely used approach regarding fall prevention in community-dwelling older people was demonstrated, since it involves a holistic intrinsic-exposure approach to the phenomena. Further investigation is required to validate the cFRs with other samples since it is a sample-specific tool.

Key factor cutoffs and interval reference values for stratified fall risk assessment in community-dwelling older adults: the role of physical fitness, body composition, physical activity, health condition, and environmental hazards

Background

Fall risk assessment and determination of older adults’ individual risk profiles are crucial elements in fall prevention. As such, it is essential to establish cutoffs and reference values for high and low risk according to key risk factor outcomes. This study main objective was to determine the key physical fitness, body composition, physical activity, health condition and environmental hazard risk outcome cutoffs and interval reference values for stratified fall risk assessment in community-dwelling older adults.

Methods

Five-hundred community-dwelling Portuguese older adults (72.2 ± 5.4 years) were assessed for falls, physical fitness, body composition, physical (in) activity, number of health conditions and environmental hazards, and sociodemographic characteristics.

Results

The established key outcomes and respective cutoffs and reference values used for fall risk stratification were multidimensional balance (low risk: score > 33, moderate risk: score 32–33, high risk: score 30–31, and very high: score < 30); lean body mass (low risk: > 44 kg, moderate risk: 42–44 kg, high risk: 39–41 kg, and very high: < 39 kg); fat body mass (low risk: < 37%, moderate risk: 37–38%, high risk: 39–42%, and very high: > 42%); total physical activity (low risk: > 2800 Met-min/wk., moderate risk: 2300–2800 Met-min/wk., high risk: 1900–2300 Met-min/wk., and very high: < 1900 Met-min/wk); rest period weekdays (low risk: < 4 h/day, moderate risk: 4–4.4 h/day, high risk: 4.5–5 h/day, and very high: > 5 h/day); health conditions (low risk: n < 3, moderate risk: n = 3, high risk: n = 4–5, and very high: n > 5); and environmental hazards (low risk: n < 5, moderate risk: n = 5, high risk: n = 6–8, and very high: n > 8).

Conclusions

Assessment of community-dwelling older adults’ fall risk should focus on the above outcomes to establish individual older adults’ fall risk profiles. Moreover, the design of fall prevention interventions should manage a person’s identified risks and take into account the determined cutoffs and respective interval values for fall risk stratification.

Beyond wellness monitoring: Continuous multiparameter remote automated monitoring of patients

The pursuit of more efficient patient-friendly health systems and reductions in tertiary health services use has seen enormous growth in the application and study of remote patient monitoring systems for cardiovascular patient care. While there are many consumer-grade products available to monitor patient wellness, the regulation of these technologies varies considerably, with most products having little to no evaluation data. As the science and practice of virtual care continues to evolve, clinicians and researchers can benefit from an understanding of more comprehensive solutions, capable of monitoring three or more biophysical parameters (e.g., oxygen saturation, heart rate) continuously and simultaneously. These devices, herein referred to as continuous multiparameter remote automated monitoring (CM-RAM) devices, have the potential to revolutionize virtual patient care. Through seamless integration of multiple biophysical signals, CM-RAM technologies can allow for the acquisition of high-volume big data for the development of algorithms to facilitate early detection of negative changes in patient health status and timely clinician response. In this article, we review key principles, architecture, and components of CM-RAM technologies. Work to date in this field and related implications are also presented, including strategic priorities for advancing the science and practice of CM-RAM.

Objective evaluation of the risk of falls in individuals with traumatic brain injury: feasibility and preliminary validation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:4658-4661. [PMID: 34892252 DOI: 10.1109/embc46164.2021.9630020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Falls are a significant health concern for individuals with traumatic brain injury (TBI). For developing effective preemptive strategies to reduce falls, it is essential to get an accurate and objective assessment of fall-risk. The current investigation evaluates the feasibility of a robotic, posturography-based fall-risk assessment to objectively quantify the risk of falls in individuals with TBI. Five individuals with chronic TBI (age: 56.2 ± 4.7 years, time since injury: 13.09±11.95 years) performed the fall-risk assessment on hunova- a commercial robotic platform for assessing and training balance. The unique assessment considers multifaceted fall-driving components, including static and dynamic balance, sit-to-stand, limits of stability, responses to perturbations, gait speed, and history of previous falls and provides a composite score for risk of falls, called silver index (SI), a number between 0 (no risk) and 100 (high risk) based on a machine learning-based predictive model. The SI score for individuals with TBI was 66±32.1 (min: 32, max: 100) - categorized as medium-to-high risk of falls. The construct validity of SI outcome was performed by evaluating its relationship with clinical outcomes of functional balance and mobility (Berg Balance Scale (BBS), Timed-Up and Go (TUG), and gait speed) as well as posturography outcomes (Center of Pressure (CoP) area and velocity). The bivariate Pearson correlation coefficient, although not statistically significant, suggested the presence of linear relationships (0.52 > r > 0.84) between SI and functional and posturography outcomes, supporting the construct validity of SI. A large sample is needed to further prove the validity of the SI outcome before it is used for meaningful interpretations of the risk of falls in individuals with TBI.Clinical Relevance- Clinical assessments of risk of falls are traditionally based on questionnaires that may lack objectivity, consistency, and accuracy. The current work tests the feasibility of using a robotic platform-based assessment to objectively quantify the risk of falls in individuals with TBI.

Systematic review and meta-analysis of clinical trials: In-hospital use of sensors for prevention of falls

BACKGROUND

Intra-hospital falls have become an important public health problem globally. The use of movement sensors with alarms has been studied as elements with predictive capacity for falls at hospital level. However, in spite of their use in some hospitals throughout the world, evidence is lacking about their effectiveness in reducing intra-hospital falls. Therefore, this study aims to develop a systematic review and meta-analysis of existing scientific literature exploring the impact of using sensors for fall prevention in hospitalized adults and the elderly population.

METHODS

We explored literature based on clinical trials in Spanish, English, and Portuguese, assessing the impact of devices used for hospital fall prevention in adult and elderly populations. The search included databases such as IEEE Xplore, the Cochrane Library, Scopus, PubMed, MEDLINE, and Science Direct databases. The critical appraisal was performed independently by two researchers. Methodological quality was assessed based on the ratings of individual biases. We performed the sum of the results, generating an estimation of the grouped effect (Relative Risk, 95% CI) for the outcome first fall for each patient. We assessed heterogeneity and publication bias. The study followed PRISMA guidelines.

RESULTS

Results were assessed in three randomized controlled clinical trials, including 29,691 patients. A total of 351 (3%) patients fell among 11,769 patients assigned to the intervention group, compared with 426 (2.4%) patients who fell among 17,922 patients assigned to the control group (general estimation RR 1.20, 95% CI 1.04, 1.37, P = .02, I2 = 0%; Moderate GRADE).

CONCLUSION

Our results show an increase of 19% in falls among elderly patients who are users of sensors located in their bed, bed-chair, or chair among their hospitalizations. Other types of sensors such as wearable sensors can be explored as coadjutants for fall prevention care in hospitals.

FallSkip device is a useful tool for fall risk assessment in sarcopenic older community people

PURPOSE

Fall prevention is a major health concern for the ageing population. Sarcopenia is considered a risk factor for falls. Some instruments, such as Time Up and Go (TUG), are used for screening risk. The use of sensors has also been shown to be a viable tool that can provide accurate, cost-effective, and easy to manage assessment of fall risk. One novel sensor for assessing fall risk in older people is the Fallskip device. The present study evaluates the performance of the FallSkip device against the TUG method in fall risk screening and assesses its measurement properties in sarcopenic older people.

METHODS

A cross-sectional study was made in a sample of community-dwelling sarcopenic and non-sarcopenic older people aged 70 years or over.

RESULTS

The study sample consisted of 34 older people with a mean age of 77.03 (6.58) years, of which 79.4% (n = 27) were females, and 41.2% (n = 14) were sarcopenic. The Pearson correlation coefficient between TUG time and FallSkip time was 0.70 (p < 0.001). The sarcopenic individuals took longer in performing both TUG and FallSkip. They also presented poorer reaction time, gait and sit-to-stand - though no statistically significant differences were observed. The results in terms of feasibility, acceptability, reliability and validity in sarcopenic older people with FallSkip were acceptable.

CONCLUSIONS

The FallSkip device has suitable metric properties for the assessment of fall risk in sarcopenic community-dwelling older people. FallSkip analyses more parameters than TUG in assessing fall risk and has greater discriminatory power in evaluating the risk of falls.