Technology Innovation Enabling Falls Risk Assessment in a Community Setting

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.

Conventional video-based system for measuring the subtask speed of the Timed Up and Go Test in older adults: Validity and reliability study

The Timed Up and Go Test (TUG) is a simple fall risk screening test that covers basic functional movement; thus, quantifying the subtask movement ability may provide a clinical utility. The video-based system allows individual's movement characteristics assessment. This study aimed to investigate the concurrent validity and test-retest reliability of the video-based system for assessing the movement speed of TUG subtasks among older adults. Twenty older adults participated in the validity study, whilst ten older adults participated in the reliability study. Participant's movement speed in each subtask of the TUG under comfortable and fast speed conditions over two sessions was measured. Pearson correlation coefficient was used to identify the validity of the video-based system compared to the motion analysis system. Intraclass correlation coefficient (ICC3,2) was used to determine the reliability of the video-based system. The Bland-Altman plots were used to quantify the agreement between the two measurement systems and two repeatable sessions. The validity analysis demonstrated a moderate to very high relationship in all TUG subtask movement speeds between the two systems under the comfortable speed (r = 0.672-0.906, p < 0.05) and a moderate to high relationship under the fast speed (r = 0.681-0.876, p < 0.05). The reliability of the video-based system was good to excellent for all subtask movement speeds in both the comfortable speed (ICCs = 0.851-0.967, p < 0.05) and fast speed (ICCs = 0.720-0.979, p < 0.05). The Bland-Altman analyses showed that almost all mean differences of the subtask speed of the TUG were close to zero, within 95% limits of agreement, and symmetrical distribution of scatter plots. The video-based system was a valid and reliable tool that may be useful in measuring the subtask movement speed of TUG among healthy older adults.

Postural symmetry evaluation using phase approximations of the follow-up CoP trajectories

This paper presents a method of postural symmetry evaluation implementing the so-called follow-up posturography. The method boils down to the assessment of similarity of the phase approximation of the counter-clockwise follow-up CoP (Center of Pressure) trajectory and the mirror image against the y-axis of the phase approximation corresponding to the clockwise follow-up CoP trajectory. The usability of the presented approach was tested on the data collected in the group of 30 patients rehabilitated after total hip arthroplasty. The observed difference between the values of the proposed postural symmetry coefficient obtained at the end and at the beginning of the rehabilitation program was statistically significant (p < 0.001). These values, however, were not significantly correlated with the values of postural symmetry coefficients computed in static posturography. Lack of significant correlations between the coefficients supports the reasoning that the new postural symmetry evaluation method quantifies symmetry of posture in terms of dynamic mechanisms, which are not manifested in the case of static posturography. As a major advantage of the herein discussed approach one can distinguish its potential to evaluate postural symmetry in dynamic conditions using relatively inexpensive single-plate posturographic platform.

Overcoming an Irrational Fear of Falling: A Case Study

Irrational fear of falling is defined by the high degree of psychological fear a person has of falling when in reality, that person is at low physiological risk of falling. It is a significant problem in older people and may manifest from previous falls or fall-related depression and/or anxiety. If untreated, it may lead to increased falling, decreased mobility, social isolation, and decreased participation in daily activities. A mixed-methods, home-based, 8-week, physio-feedback/exercise program was developed for an older adult to help mitigate her irrational fear of falling. In this pilot case study, the quantitative independent variables of fear of falling, static balance, dynamic balance, fall risk factors, and fall risk appraisal were assessed pre- and post-intervention. A qualitative semi-structured interview was also conducted to help assess the participant’s reaction to the intervention. Data from quantitative, qualitative, and integrated viewpoints converged to indicate the intervention improved fear of falling, shifted the participant’s perception from irrational to rational fall risk appraisal and increased participation in daily activities.

Robotic balance assessment in community-dwelling older people with different grades of impairment of physical performance

BACKGROUND

Impaired physical performance is common in older adults and has been identified as a major risk factor for falls. To date, there are no conclusive data on the impairment of balance parameters in older subjects with different levels of physical performance.

AIMS

The aim of this study was to investigate the relationship between different grades of physical performance, as assessed by the Short Physical Performance Battery (SPPB), and the multidimensional balance control parameters, as measured by means of a robotic system, in community-dwelling older adults.

METHODS

This study enrolled subjects aged ≥ 65 years. Balance parameters were assessed by the hunova robot in static and dynamic (unstable and perturbating) conditions, in both standing and seated positions and with the eyes open/closed.

RESULTS

The study population consisted of 96 subjects (62 females, mean age 77.2 ± 6.5 years). According to their SPPB scores, subjects were separated into poor performers (SPPB < 8, n = 29), intermediate performers (SPPB = 8-9, n = 29) and good performers (SPPB > 9, n = 38). Poor performers displayed significantly worse balance control, showing impaired trunk control in most of the standing and sitting balance tests, especially in dynamic (both with unstable and perturbating platform/seat) conditions.

CONCLUSIONS

For the first time, multidimensional balance parameters, as detected by the hunova robotic system, were significantly correlated with SPPB functional performances in community-dwelling older subjects. In addition, balance parameters in dynamic conditions proved to be more sensitive in detecting balance impairments than static tests.

Diagnostic test accuracy of an automated device as a screening tool for fall risk assessment in community-residing elderly: A STARD compliant study

We aimed to determine the accuracy and failure of OAK device, an automated screening, for the assessment of fall risk in a prospective cohort of healthy adults aged over 65 years. The algorithm for fall risk assessment of the centers for disease control and prevention (CDC) was used as reference standard. Of the 183 individuals recruited, the CDC algorithm classified 80 as being at moderate/high risk and 103 at low risk of falling. OAK device failure incidence was 4.9% (confidence interval [CI] upper limit 7.7%), below the preset threshold for futility-early termination of the study (i.e., not above 15%). The OAK device showed a sensitivity of 84% and a specificity of 67% (receiver operating characteristic [ROC] area 82%; 95% confidence interval [CI] 76-88%), not reaching the preplanned target sensitivity (not lower than 85%). Diagnostic accuracy was not far from the sensitivity levels similar to those obtained with other fall risk assessment. However, some limitations can be considered.ClinicalTrials.gov identifier: NCT02655796.

Automation of the Timed-Up-and-Go Test Using a Conventional Video Camera

The Timed-Up-and-Go (TUG) test is a simple clinical tool commonly used to quickly assess the mobility of patients. Researchers have endeavored to automate the test using sensors or motion tracking systems to improve its accuracy and to extract more resolved information about its sub-phases. While some approaches have shown promise, they often require the donning of sensors or the use of specialized hardware, such as the now discontinued Microsoft Kinect, which combines video information with depth sensors (RGBD). In this work, we leverage recent advances in computer vision to automate the TUG test using a regular RGB video camera without the need for custom hardware or additional depth sensors. Thirty healthy participants were recorded using a Kinect V2 and a standard video feed while performing multiple trials of 3 and 1.5 meter versions of the TUG test. A Mask Regional Convolutional Neural Net (R-CNN) algorithm and a Deep Multitask Architecture for Human Sensing (DMHS) were then used together to extract global 3D poses of the participants. The timing of transitions between the six key movement phases of the TUG test were then extracted using heuristic features extracted from the time series of these 3D poses. The proposed video-based vTUG system yielded the same error as the standard Kinect-based system for all six key transitions points, and average errors of less than 0.15 seconds from a multi-observer hand labeled ground truth. This work describes a novel method of video-based automation of the TUG test using a single standard camera, removing the need for specialized equipment and facilitating the extraction of additional meaningful information for clinical use.

Factors Influencing Falls in High- and Low-Risk Patients in a Tertiary Hospital in Korea

The purpose of this study was to explore the characteristics and predictors of falls in high- and low-risk inpatients in a tertiary hospital in Korea.

Validity and Reliability of a Portable Balance Tracking System, BTrackS, in Older Adults

BACKGROUND AND PURPOSE

Falls are the leading cause of disability, injury, hospital admission, and injury-related death among older adults. Balance limitations have consistently been identified as predictors of falls and increased fall risk. Field measures of balance are limited by issues of subjectivity, ceiling effects, and low sensitivity to change. The gold standard for measuring balance is the force plate; however, its field use is untenable due to high cost and lack of portability. Thus, a critical need is observed for valid objective field measures of balance to accurately assess balance and identify limitations over time. The purpose of this study was to examine the concurrent validity and 3-day test-retest reliability of Balance Tracking System (BTrackS) in community-dwelling older adults. Minimal detectable change values were also calculated to reflect changes in balance beyond measurement error.

METHODS

Postural sway data were collected from community-dwelling older adults (N = 49, mean [SD] age = 71.3 [7.3] years) with a force plate and BTrackS in multitrial eyes open (EO) and eyes closed (EC) static balance conditions. Force sensors transmitted BTrackS data via a USB to a computer running custom software. Three approaches to concurrent validity were taken including calculation of Pearson product moment correlation coefficients, repeated-measures ANOVAs, and Bland-Altman plots. Three-day test-retest reliability of BTrackS was examined in a second sample of 47 community-dwelling older adults (mean [SD] age = 75.8 [7.7] years) using intraclass correlation coefficients and MDC values at 95% CI (MDC95) were calculated.

RESULTS

BTrackS demonstrated good validity using Pearson product moment correlations (r > 0.90). Repeated-measures ANOVA and Bland-Altman plots indicated some BTrackS bias with center of pressure (COP) values higher than FP COP values in the EO (mean [SD] bias = 4.0 [6.8]) and EC (mean [SD] bias = 9.6 [12.3]) conditions. Test-retest reliability using intraclass correlation coefficients (ICC2.1 was excellent (0.83) and calculated MDC95 for EO (9.6 cm) and EC (19.4 cm) and suggested that postural sway changes of these amounts are meaningful.

DISCUSSION

BTrackS showed some bias with values exceeding force plate values in both EO and EC conditions. Excellent test-retest reliability and resulting MDC95 values indicated that BTrackS has the potential to identify meaningful changes in balance that may warrant intervention.

CONCLUSION

BTrackS is an objective measure of balance that can be used to monitor balance in community-dwelling older adults over time. It can reliably identify changes that may require further attention (eg, fall-prevention strategies, declines in physical function) and shows promise for assessing intervention efficacy in this growing segment of the population.

Physiotherapy practice patterns in gait rehabilitation for adults with acquired brain injury

OBJECTIVE

Gait dysfunction is common following acquired brain injury (ABI). Clinical practice guidelines can improve patient outcomes; however, information regarding ABI-specific management of gait dysfunction is limited. This study aimed to describe practice patterns of Canadian physiotherapists regarding gait rehabilitation in adults post-ABI.

METHODS

An electronic questionnaire was developed and distributed across Canada to describe physiotherapy assessment methods, outcome measures, and treatment interventions used to manage gait dysfunction in adults with mild-moderate and severe ABI.

RESULTS

Of 103 respondents who accessed the questionnaire, 59 met inclusion criteria and participated in the study. Methods most frequently used "often or very often" at initial and discharge assessments included visual observation (≥88.2% for adults with mild-moderate and severe ABI) and the Berg Balance Scale (≥76.3% for adults with mild-moderate ABI). Higher level gait training exercises were used more often for adults with mild-moderate than severe ABI. Physiotherapists commonly reported further research was required to develop and validate gait-specific outcome measures (42.4%) and treatment techniques (76.3%).

CONCLUSIONS

Physiotherapists' use of gait-specific treatment interventions, but not assessment methods, differs depending on ABI severity. Future work should investigate factors influencing these practice patterns. In addition, clinician-identified research priorities include ABI gait-specific outcome measures and technology-based interventions.

Chair rise transfer detection and analysis using a pendant sensor: an algorithm for fall risk assessment in older people

Falls result in substantial disability, morbidity, and mortality among older people. Early detection of fall risks and timely intervention can prevent falls and injuries due to falls. Simple field tests, such as repeated chair rise, are used in clinical assessment of fall risks in older people. Development of on-body sensors introduces potential beneficial alternatives for traditional clinical methods. In this article, we present a pendant sensor based chair rise detection and analysis algorithm for fall risk assessment in older people. The recall and the precision of the transfer detection were 85% and 87% in standard protocol, and 61% and 89% in daily life activities. Estimation errors of chair rise performance indicators: duration, maximum acceleration, peak power and maximum jerk were tested in over 800 transfers. Median estimation error in transfer peak power ranged from 1.9% to 4.6% in various tests. Among all the performance indicators, maximum acceleration had the lowest median estimation error of 0% and duration had the highest median estimation error of 24% over all tests. The developed algorithm might be feasible for continuous fall risk assessment in older people.

Chair Rise Peak Power in Daily Life Measured With a Pendant Sensor Associates With Mobility, Limitation in Activities, and Frailty in Old People

The aim of this study was to analyze the clinical relevance of sensor-based daily life chair rise performance measured in old people. A pendant-sensor was worn during standardized tests and in daily life to detect chair rise transfers and analyze transfer peak power. Linear correlations between mean, median, 25th, and 75th percentile transfer peak powers in daily life and mean peak power in standardized tests were evaluated with Pearson correlation ( r). Associations between transfer peak powers in different experiments and outcomes of a clinical mobility test [timed-up-and-go (TUG)], a test of limitation in activities [Groningen activity restriction scale (GARS)], and a frailty test [Groningen frailty indicator (GFI)] were evaluated with Spearman correlation (ρ). Twenty-five old people (70-85 years) participated in the study. The results showed that chair rise peak powers assessed based upon one-week of daily life activities significantly correlated with peak power measured in standardized tests (r: [0.66, 0.74], p < 0.01). Chair rise peak power in daily life significantly associated with TUG scores (ρ: [-0.71, -0.58], ), GARS (ρ: [-0.62, -0.48], ), and GFI (ρ: [-0.52, -0.43], ). Chair rise peak powers in daily life had stronger associations with clinical measurements than standardized tests. In addition, chair rise peak powers measured in old people using assistive devices was significantly lower compared to those not using assistive devices. These results indicate usefulness of the pendant-sensor-based chair rise performance analysis in continuous monitoring and assessment of mobility, limitations in activities and frailty associated variables in old people's daily life.

Predicting Falls in Parkinson Disease: What Is the Value of Instrumented Testing in OFF Medication State?

Background

Falls are a common complication of advancing Parkinson's disease (PD). Although numerous risk factors are known, reliable predictors of future falls are still lacking. The objective of this prospective study was to investigate clinical and instrumented tests of balance and gait in both OFF and ON medication states and to verify their utility in the prediction of future falls in PD patients.

Methods

Forty-five patients with idiopathic PD were examined in defined OFF and ON medication states within one examination day including PD-specific clinical tests, instrumented Timed Up and Go test (iTUG) and computerized dynamic posturography. The same gait and balance tests were performed in 22 control subjects of comparable age and sex. Participants were then followed-up for 6 months using monthly fall diaries and phone calls.

Results

During the follow-up period, 27/45 PD patients and 4/22 control subjects fell one or more times. Previous falls, fear of falling, more severe motor impairment in the OFF state, higher PD stage, more pronounced depressive symptoms, higher daily levodopa dose and stride time variability in the OFF state were significant risk factors for future falls in PD patients. Increased stride time variability in the OFF state in combination with faster walking cadence appears to be the most significant predictor of future falls, superior to clinical predictors.

Conclusion

Incorporating instrumented gait measures into the baseline assessment battery as well as accounting for both OFF and ON medication states might improve future fall prediction in PD patients. However, instrumented testing in the OFF state is not routinely performed in clinical practice and has not been used in the development of fall prevention programs in PD. New assessment methods for daylong monitoring of gait, balance and falls are thus required to more effectively address the risk of falling in PD patients.

A prospective field study for sensor-based identification of fall risk in older people with dementia

OBJECTIVE

Aim of this study was to make a fall prognosis in a cohort of older people with dementia in short-term (2 month), mid-term (4 month) and long-term (8 month) intervals using accelerometry during the subjects' everyday life.

METHODS

The study was designed as a longitudinal cohort study. The subjects were recruited from a nursing home and geriatric assessment tests were conducted at baseline. Each subject underwent four visits and was measured at each visit for one week. Gait episodes were detected and gait parameters were extracted from these episodes. These gait parameters were combined with the falls occurred during the study. A decision tree induction method was used to analyze the data.

RESULTS

Forty subjects participated in the study, whereby 12 drop-outs were registered. The geriatric assessment tests were unable to distinguish between the groups (AUC < 0.6). The evaluation of the models induced with the decision tree classification showed a rate of correctly classified gait episodes of 88.4% for short-term, 74.8% for mid-term, and 88.5 % for long-term monitoring.

DISCUSSION AND CONCLUSIONS

We concluded that it is possible to classify gait episodes of fallers and non-fallers in people with dementia during everyday life using accelerometry.

Test-retest reliability of a pendant-worn sensor device in measuring chair rise performance in older persons

Chair rise performance is incorporated in clinical assessments to indicate fall risk status in older persons. This study investigated the test-retest reliability of a pendant-sensor-based assessment of chair rise performance. Forty-one older persons (28 females, 13 males, age: 72–94) were assessed in two sessions with 3 to 8 days in between. Repeated chair rise transfers were measured after different instructions. Relative and absolute test-retest reliability of chair rise measurements in individual tests and average over all tests were evaluated by means of intra-class correlation coefficients (ICCs) and standard error of measurement (SEM) as a percentage of the measurement mean. Systematic bias between the measurements in test and retest was examined with paired t-tests. Heteroscedasticity of the measurements was visually checked with Bland-Altman plots. In the different test conditions, the ICCs ranged between 0.63 and 0.93, and the SEM% ranged between 5.7% and 21.2%. The relative and absolute reliability of the average over all tests were ICC = 0.86 and SEM% = 9.5% for transfer duration, ICC = 0.93 and SEM% = 9.2% for maximum vertical acceleration, and ICC = 0.89 and SEM% = 10.0% for peak power. The results over all tests indicated that a fall risk assessment application based on pendant-worn-sensor measured chair rise performance in daily life might be feasible.

Assessing fall risk using wearable sensors: a practical discussion. A review of the practicalities and challenges associated with the use of wearable sensors for quantification of fall risk in older people

Identification of older people most at risk of falling may facilitate early preventative intervention to reduce the likelihood of falls occurring. While many clinical fall risk assessment techniques exist, they often require subjective assessor interpretation, or are not appropriate for unsupervised screening of larger populations owing to a number of issues including safety, ability to reliably perform the assessment, and requirements for unwieldy apparatus. Researchers have more recently attempted to address some of these deficits by instrumenting new or existing physical fall risk assessments with wearable motion sensors to make such assessments more objective, quicker to administer, and potentially more appropriate for deployment for unsupervised use in the community. The objective of this paper is to discuss various practical questions involving sensor-based fall risk assessment (SFRA). Many of the issues discussed contribute to answering the important question of whether SFRA should or can be used in either a supervised or an unsupervised manner, and what possible deployment scenarios exist for it.

The promise of mHealth: daily activity monitoring and outcome assessments by wearable sensors

Mobile health tools that enable clinicians and researchers to monitor the type, quantity, and quality of everyday activities of patients and trial participants have long been needed to improve daily care, design more clinically meaningful randomized trials of interventions, and establish cost-effective, evidence-based practices. Inexpensive, unobtrusive wireless sensors, including accelerometers, gyroscopes, and pressure-sensitive textiles, combined with Internet-based communications and machine-learning algorithms trained to recognize upper- and lower-extremity movements, have begun to fulfill this need. Continuous data from ankle triaxial accelerometers, for example, can be transmitted from the home and community via WiFi or a smartphone to a remote data analysis server. Reports can include the walking speed and duration of every bout of ambulation, spatiotemporal symmetries between the legs, and the type, duration, and energy used during exercise. For daily care, this readily accessible flow of real-world information allows clinicians to monitor the amount and quality of exercise for risk factor management and compliance in the practice of skills. Feedback may motivate better self-management as well as serve home-based rehabilitation efforts. Monitoring patients with chronic diseases and after hospitalization or the start of new medications for a decline in daily activity may help detect medical complications before rehospitalization becomes necessary. For clinical trials, repeated laboratory-quality assessments of key activities in the community, rather than by clinic testing, self-report, and ordinal scales, may reduce the cost and burden of travel, improve recruitment and retention, and capture more reliable, valid, and responsive ratio-scaled outcome measures that are not mere surrogates for changes in daily impairment, disability, and functioning.