A novel infrared laser device that measures multilateral parameters of stepping performance for assessment of fall risk in elderly individuals [corrected]

Sensor-Based Fall Risk Assessment: A Survey

Fall is a major problem leading to serious injuries in geriatric populations. Sensor-based fall risk assessment is one of the emerging technologies to identify people with high fall risk by sensors, so as to implement fall prevention measures. Research on this domain has recently made great progress, attracting the growing attention of researchers from medicine and engineering. However, there is a lack of studies on this topic which elaborate the state of the art. This paper presents a comprehensive survey to discuss the development and current status of various aspects of sensor-based fall risk assessment. Firstly, we present the principles of fall risk assessment. Secondly, we show knowledge of fall risk monitoring techniques, including wearable sensor based and non-wearable sensor based. After that we discuss features which are extracted from sensors in fall risk assessment. Then we review the major methods of fall risk modeling and assessment. We also discuss some challenges and promising directions in this field at last.

Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility Study

Early and self-identification of locomotive degradation facilitates us with awareness and motivation to prevent further deterioration. We propose the usage of nine squat and four one-leg standing exercise features as input parameters to Machine Learning (ML) classifiers in order to perform lower limb skill assessment. The significance of this approach is that it does not demand manpower and infrastructure, unlike traditional methods. We base the output layer of the classifiers on the Short Test Battery Locomotive Syndrome (STBLS) test used to detect Locomotive Syndrome (LS) approved by the Japanese Orthopedic Association (JOA). We obtained three assessment scores by using this test, namely sit-stand, 2-stride, and Geriatric Locomotive Function Scale (GLFS-25). We tested two ML methods, namely an Artificial Neural Network (ANN) comprised of two hidden layers with six nodes per layer configured with Rectified-Linear-Unit (ReLU) activation function and a Random Forest (RF) regressor with number of estimators varied from 5 to 100. We could predict the stand-up and 2-stride scores of the STBLS test with correlation of 0.59 and 0.76 between the real and predicted data, respectively, by using the ANN. The best accuracies (R-squared values) obtained through the RF regressor were 0.86, 0.79, and 0.73 for stand-up, 2-stride, and GLFS-25 scores, respectively.

Stepping impairment and falls in older adults: A systematic review and meta-analysis of volitional and reactive step tests

OBJECTIVE

To systematically examine stepping performance as a risk factor for falls. More specifically, we examined (i) if step tests can distinguish fallers from non-fallers and (ii) the type of step test (e.g. volitional vs reactive stepping) that is required to distinguish fallers from non-fallers.

DATA SOURCE

PubMed, EMBASE, CINAHL, Cochrane Database of Systematic Reviews and reference lists of included articles.

STUDY SELECTION

Cross-sectional and cohort studies that assessed the association between at least one step test and falls in older people (age ≥ 60 and/or mean age of 65).

RESULTS

A meta-analysis of 61 studies (n = 9536) showed stepping performance was significantly worse in fallers compared to non-fallers (Cohen'sd 0.56, 95 % CI 0.48 to 0.64, p < 0.001, I² 66 %). This was the case for both volitional and reactive step tests. Twenty-three studies (n = 3615) were included in a diagnostic meta-analysis that showed that step tests have moderate sensitivity (0.70, 95 % CI 0.62 to 0.77), specificity (0.68, 95 % CI 0.58 to 0.77) and area under the receiver operating characteristics curve (AUC) (0.75, 95 % CI 0.59 to 0.86) in discriminating fallers from non-fallers.

CONCLUSIONS

This large systematic review demonstrated that both volitional and reactive stepping impairments are significant fall risk factors among older adults. Step tests can identify fallers from non-fallers with moderate accuracy.

Chronic Musculoskeletal Pain and Foot Reaction Time in Older Adults

This cross-sectional study examines the association between chronic musculoskeletal pain and foot reaction time (RT) among older community-living adults. Participants were 307 adults aged 71 years and older in the MOBILIZE Boston Study II. Pain severity, interference, and location were measured by the Brief Pain Inventory and a joint pain questionnaire. With participants seated, simple foot reaction time was measured as self-selected foot response time to an intermittent light, and choice foot reaction time was measured as response time to the light on the corresponding side of the sensored gait mat. We performed multivariable linear regression to determine associations of pain and foot RT, adjusted for sociodemographic and health characteristics, and serially adjusted for cognitive function (MMSE or Trail Making A). Pain severity and interference were associated with slower simple foot reaction time (P < .05). Pain severity and knee pain were associated with slower choice foot reaction time (P < .05). Adjustment for cognitive measures had little impact on the pain-RT relationship. This significant relationship was only observed among participants with less education. These results support the idea that chronic pain may lead to slower foot RT, thus could represent a fall hazard in older adults. Neuromotor mechanisms underlying the pain-fall relationship warrant further investigation. PERSPECTIVE: This study provides insights on the mechanisms underlying the pain-fall relationship. Chronic pain may contribute to slower foot RT thus increase fall risk in older adults. This may help inform interventions such as stepping training to reduce fall risk in older adults living with chronic pain.

Establishing the waist as the better location for attaching a single accelerometer to estimate center of pressure trajectories

BACKGROUND

In this study, we seek to replace conventional force platforms with a single accelerometer for measuring Center of Pressure trajectories, in order to achieve portability and convenience without sacrificing accuracy.

METHODS

We measure the actual Anterior/Posterior and Medial/Lateral Center of Pressure trajectories of ten healthy young subjects using a force platform, and compare them with estimated measurements derived from accelerometer signals collected from three body locations (upper trunk, waist, and lower thigh) using three machine learning algorithms (Neural Network, Genetic Algorithm, and Adaptive Network-based Fuzzy Inference System). Error ratios and correlation coefficients corresponding to body locations were compared via one-way repeated-measures ANOVA. The ratios and coefficients corresponding to the three algorithms were also compared using the same approach.

FINDINGS

Estimated Anterior/Posterior trajectories indicated that measurements collected from the waist provided the lowest margins of error (8.1-8.4% v. 12.1-13.4%, P ≤ .001) and the highest correlation (.95 v. .82-.86, P ≤ .032). Estimated Medial/Lateral trajectories indicated that measurements collected from both the waist and thigh, as compared to the upper trunk, provided lower margins of error (7.0-7.3% v. 8.5-10.8%). In general, the waist is the better accelerometer attachment location.

INTERPRETATION

The results of our study corroborate our deduction that the high correlation between Center of Pressure and body's Center of Mass provides the rationale to place the single accelerometer close to the waist for Center of Pressure estimations. This study also supports the feasibility of using one single accelerometer programmed with algorithms for similar clinical applications.

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

Background

Falls are a major health problem for older adults with significant physical and psychological consequences. A first step of successful fall prevention is to identify those at risk of falling. Recent advancement in sensing technology offers the possibility of objective, low-cost and easy-to-implement fall risk assessment. The objective of this systematic review is to assess the current state of sensing technology on providing objective fall risk assessment in older adults.

Methods

A systematic review was conducted in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement (PRISMA).

Results

Twenty-two studies out of 855 articles were systematically identified and included in this review. Pertinent methodological features (sensing technique, assessment activities, outcome variables, and fall discrimination/prediction models) were extracted from each article. Four major sensing technologies (inertial sensors, video/depth camera, pressure sensing platform and laser sensing) were reported to provide accurate fall risk diagnostic in older adults. Steady state walking, static/dynamic balance, and functional mobility were used as the assessment activity. A diverse range of diagnostic accuracy across studies (47.9% - 100%) were reported, due to variation in measured kinematic/kinetic parameters and modelling techniques.

Conclusions

A wide range of sensor technologies have been utilized in fall risk assessment in older adults. Overall, these devices have the potential to provide an accurate, inexpensive, and easy-to-implement fall risk assessment. However, the variation in measured parameters, assessment tools, sensor sites, movement tasks, and modelling techniques, precludes a firm conclusion on their ability to predict future falls. Future work is needed to determine a clinical meaningful and easy to interpret fall risk diagnosis utilizing sensing technology. Additionally, the gap between functional evaluation and user experience to technology should be addressed.

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

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

Kinect-based choice reaching and stepping reaction time tests for clinical and in-home assessment of fall risk in older people: a prospective study

Background

Quick protective reactions such as reaching or stepping are important to avoid a fall or minimize injuries. We developed Kinect-based choice reaching and stepping reaction time tests (Kinect-based CRTs) and evaluated their ability to differentiate between older fallers and non-fallers and the feasibility of administering them at home.

Methods

A total of 94 community-dwelling older people were assessed on the Kinect-based CRTs in the laboratory and were followed-up for falls for 6 months. Additionally, a subgroup (n = 20) conducted the Kinect-based CRTs at home. Signal processing algorithms were developed to extract features for reaction, movement and the total time from the Kinect skeleton data.

Results

Nineteen participants (20.2 %) reported a fall in the 6 months following the assessment. The reaction time (fallers: 797 ± 136 ms, non-fallers: 714 ± 89 ms), movement time (fallers: 392 ± 50 ms, non-fallers: 358 ± 51 ms) and total time (fallers: 1189 ± 170 ms, non-fallers: 1072 ± 109 ms) of the reaching reaction time test differentiated well between the fallers and non-fallers. The stepping reaction time test did not significantly discriminate between the two groups in the prospective study. The correlations between the laboratory and in-home assessments were 0.689 for the reaching reaction time and 0.860 for stepping reaction time.

Conclusion

The study findings indicate that the Kinect-based CRT tests are feasible to administer in clinical and in-home settings, and thus represents an important step towards the development of sensor-based fall risk self-assessments. With further validation, the assessments may prove useful as a fall risk screen and home-based assessment measures for monitoring changes over time and effects of fall prevention interventions.

New methods for fall risk prediction

PURPOSE OF REVIEW

Accidental falls are the leading cause of injury-related death and hospitalization in old age, with over one-third of the older adults experiencing at least one fall or more each year. Because of limited healthcare resources, regular objective fall risk assessments are not possible in the community on a large scale. New methods for fall prediction are necessary to identify and monitor those older people at high risk of falling who would benefit from participating in falls prevention programmes.

RECENT FINDINGS

Technological advances have enabled less expensive ways to quantify physical fall risk in clinical practice and in the homes of older people. Recently, several studies have demonstrated that sensor-based fall risk assessments of postural sway, functional mobility, stepping and walking can discriminate between fallers and nonfallers.

SUMMARY

Recent research has used low-cost, portable and objective measuring instruments to assess fall risk in older people. Future use of these technologies holds promise for assessing fall risk accurately in an unobtrusive manner in clinical and daily life settings.

Choice stepping reaction time test using exergame technology for fall risk assessment in older people

Accidental falls remain an important problem in older people. Stepping is a common task to avoid a fall and requires good interplay between sensory functions, central processing and motor execution. Increased choice stepping reaction time has been associated with recurrent falls in older people. The aim of this study was to examine if a sensor-based Exergame Choice Stepping Reaction Time test can successfully discriminate older fallers from non-fallers. The stepping test was conducted in a cohort of 104 community-dwelling older people (mean age: 80.7 ± 7.0 years). Participants were asked to step laterally as quickly as possible after a light stimulus appeared on a TV screen. Spatial and temporal measurements of the lower and upper body were derived from a low-cost and portable 3D-depth sensor (i.e. Microsoft Kinect) and 3D-accelerometer. Fallers had a slower stepping reaction time (970 ± 228 ms vs. 858 ± 123 ms, P = 0.001) and a slower reaction of their upper body (719 ± 289 ms vs. 631 ± 166 ms, P = 0.052) compared to non-fallers. It took fallers significantly longer than non-fallers to recover their balance after initiating the step (2147 ± 800 ms vs. 1841 ± 591 ms, P = 0.029). This study demonstrated that a sensor-based, low-cost and easy to administer stepping test, with the potential to be used in clinical practice or regular unsupervised home assessments, was able to identify significant differences between performances by fallers and non-fallers.