An instrumented sit-to-stand test used to examine differences between older fallers and non-fallers

The relationship between acceleration in sit-to-stand and falls in community-dwelling older adults: cross-sectional study

[Purpose] This study aimed to determine the relationship between acceleration parameters in the sit-to-stand (STS) movement and falls, and the strength of the association between acceleration in STS movements and falls in older adults. [Participants and Methods] In total, 330 older adults were included. Four acceleration parameters were measured in STS movement: maximum acceleration (MA), velocity (MV), power (MP), and stand-up time (ST). For the conventional STS tests, 5 times STS test (5xSTS) and ground reaction force (maximal rate of force development per body weight: RFD/w, peak reaction force per body weight: F/w, chair-rise time: T) were measured. Poisson regression analysis adjusted for confounding factors was used. [Results] In the model adjusted for confounders, significant associations were observed among MV (Prevalence ratio (PR): 0.75; 95% confidence interval (CI): 0.58-0.98), MP (PR: 0.67; 95% Cl: 0.68-0.93), RFD/w (PR: 0.70; 95% Cl: 0.56-0.87), and T (PR: 1.14; 95% Cl: 1.05-1.24). [Conclusion] Among the acceleration parameters, MP was most strongly associated with falls and was considered the most useful parameter for evaluation. In addition, comparisons with the conventional chair rise tests suggested that MP was stronger than the 5xSTS test and may be equally related to the RFD/w.

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.

A novel approach for modelling and classifying sit-to-stand kinematics using inertial sensors

Sit-to-stand transitions are an important part of activities of daily living and play a key role in functional mobility in humans. The sit-to-stand movement is often affected in older adults due to frailty and in patients with motor impairments such as Parkinson's disease leading to falls. Studying kinematics of sit-to-stand transitions can provide insight in assessment, monitoring and developing rehabilitation strategies for the affected populations. We propose a three-segment body model for estimating sit-to-stand kinematics using only two wearable inertial sensors, placed on the shank and back. Reducing the number of sensors to two instead of one per body segment facilitates monitoring and classifying movements over extended periods, making it more comfortable to wear while reducing the power requirements of sensors. We applied this model on 10 younger healthy adults (YH), 12 older healthy adults (OH) and 12 people with Parkinson's disease (PwP). We have achieved this by incorporating unique sit-to-stand classification technique using unsupervised learning in the model based reconstruction of angular kinematics using extended Kalman filter. Our proposed model showed that it was possible to successfully estimate thigh kinematics despite not measuring the thigh motion with inertial sensor. We classified sit-to-stand transitions, sitting and standing states with the accuracies of 98.67%, 94.20% and 91.41% for YH, OH and PwP respectively. We have proposed a novel integrated approach of modelling and classification for estimating the body kinematics during sit-to-stand motion and successfully applied it on YH, OH and PwP groups.

Effect of Fear of Falling on Mobility Measured During Lab and Daily Activity Assessments in Parkinson's Disease

In chronic disorders such as Parkinson’s disease (PD), fear of falling (FOF) is associated with falls and reduced quality of life. With inertial measurement units (IMUs) and dedicated algorithms, different aspects of mobility can be obtained during supervised tests in the lab and also during daily activities. To our best knowledge, the effect of FOF on mobility has not been investigated in both of these settings simultaneously. Our goal was to evaluate the effect of FOF on the mobility of 26 patients with PD during clinical assessments and 14 days of daily activity monitoring. Parameters related to gait, sit-to-stand transitions, and turns were extracted from IMU signals on the lower back. Fear of falling was assessed using the Falls Efficacy Scale-International (FES-I) and the patients were grouped as with (PD-FOF+) and without FOF (PD-FOF−). Mobility parameters between groups were compared using logistic regression as well as the effect size values obtained using the Wilcoxon rank-sum test. The peak angular velocity of the turn-to-sit transition of the timed-up-and-go (TUG) test had the highest discriminative power between PD-FOF+ and PD-FOF− (r-value of effect size = 0.61). Moreover, PD-FOF+ had a tendency toward lower gait speed at home and a lower amount of walking bouts, especially for shorter walking bouts. The combination of lab and daily activity parameters reached a higher discriminative power [area under the curve (AUC) = 0.75] than each setting alone (AUC = 0.68 in the lab, AUC = 0.54 at home). Comparing the gait speed between the two assessments, the PD-FOF+ showed higher gait speeds in the capacity area compared with their TUG test in the lab. The mobility parameters extracted from both lab and home-based assessments contribute to the detection of FOF in PD. This study adds further evidence to the usefulness of mobility assessments that include different environments and assessment strategies. Although this study was limited in the sample size, it still provides a helpful method to consider the daily activity measurement of the patients with PD into clinical evaluation. The obtained results can help the clinicians with a more accurate prevention and treatment strategy.

Early diagnosis of frailty: Technological and non-intrusive devices for clinical detection

This work analyses different concepts for frailty diagnosis based on affordable standard technology such as smartphones or wearable devices. The goal is to provide ideas that go beyond classical diagnostic tools such as magnetic resonance imaging or tomography, thus changing the paradigm; enabling the detection of frailty without expensive facilities, in an ecological way for both patients and medical staff and even with continuous monitoring. Fried's five-point phenotype model of frailty along with a model based on trials and several classical physical tests were used for device classification. This work provides a starting point for future researchers who will have to try to bridge the gap separating elderly people from technology and medical tests in order to provide feasible, accurate and affordable tools for frailty monitoring for a wide range of users.

Performance and Characteristics of Wearable Sensor Systems Discriminating and Classifying Older Adults According to Fall Risk: A Systematic Review

Sensor-based fall risk assessment (SFRA) utilizes wearable sensors for monitoring individuals’ motions in fall risk assessment tasks. Previous SFRA reviews recommend methodological improvements to better support the use of SFRA in clinical practice. This systematic review aimed to investigate the existing evidence of SFRA (discriminative capability, classification performance) and methodological factors (study design, samples, sensor features, and model validation) contributing to the risk of bias. The review was conducted according to recommended guidelines and 33 of 389 screened records were eligible for inclusion. Evidence of SFRA was identified: several sensor features and three classification models differed significantly between groups with different fall risk (mostly fallers/non-fallers). Moreover, classification performance corresponding the AUCs of at least 0.74 and/or accuracies of at least 84% were obtained from sensor features in six studies and from classification models in seven studies. Specificity was at least as high as sensitivity among studies reporting both values. Insufficient use of prospective design, small sample size, low in-sample inclusion of participants with elevated fall risk, high amounts and low degree of consensus in used features, and limited use of recommended model validation methods were identified in the included studies. Hence, future SFRA research should further reduce risk of bias by continuously improving methodology.

Comparison of Human Interpretation and a Rule-Based Algorithm for Instrumented Sit-to-Stand Test

BACKGROUND

The five times sit-to-stand test (5STS) is one of the most commonly used tests to assess the physical performance of lower extremities. This study assessed the correlation between human interpretation (5STShuman) and a rule-based algorithm (5STSrule) using instrumented 5STS with two sensors.

METHODS

We analyzed clinical records of 148 patients who visited the geriatric outpatient clinic of Asan Medical Center between December 2020 and March 2021 and underwent physical performance assessment using the electronic Short Physical Performance Battery (eSPPB) protocol. For STS, time-weight and time-distance curves were constructed using a loadcell and light detection and ranging (LiDAR). We manually assessed the grids of these curves to calculate 5STShuman, while 5STSrule used an empirical rule-based algorithm.

RESULTS

In the study population, the mean 5STShuman and 5STSrule times, i.e., 12.2±0.4 and 11.4±0.4 seconds, respectively, did not differ significantly (p=0.232). Linear regression analysis showed that 5STShuman and 5STSrule were positively correlated (β=0.99, R2=0.99). The measures also did not differ (p=0.381) in classifying sarcopenia according to the Asian Working Group Society criteria, with C-indices of 0.826 for 5STShuman and 0.820 for 5STSrule.

CONCLUSION

An empirical rule-based algorithm correlated with human-interpreted 5STS and had comparable classification ability for sarcopenia.

Instrumented Analysis of the Sit-to-Stand Movement for Geriatric Screening: A Systematic Review

The Sit-to-Stand (STS) is a widely used test of physical function to screen older people at risk of falls and frailty and is also one of the most important components of standard screening for sarcopenia. There have been many recent studies in which instrumented versions of the STS (iSTS) have been developed to provide additional parameters that could improve the accuracy of the STS test. This systematic review aimed to identify whether an iSTS is a viable alternative to a standard STS to identify older people at risk of falling, frailty, and sarcopenia. A total of 856 articles were found using the search strategy developed, with 12 articles retained in the review after screening based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Six studies evaluated the iSTS in fallers, five studies in frailty and only one study in both fallers and frailty. The results showed that power and velocity parameters extracted from an iSTS have the potential to improve the accuracy of screening when compared to a standard STS. Future work should focus on standardizing the segmentation of the STS into phases to enable comparison between studies and to develop devices integrated into the chair used for the test to improve usability.

Application of the Teagar-Kaiser energy operator and wavelet transform for detection of finger tapping contact and release times using accelerometery

The Teager-Kaiser energy operator (TKEO), when applied to a signal gives an estimation of the instantaneous energy of that signal. It, therefore, accentuates both frequency and amplitude changes in a signal. To date, it has been primarily used in communications systems and most popularly in electromyographic signal analysis to detect bursts of muscle activity, however, it has the potential to be used in a number of applications including accelerometer and movement data.A new algorithm was developed which used the TKEO to detect contact times during a finger tapping task from accelerometer data recorded from the index finger. The accuracy of the algorithm was assessed in 7 healthy control subjects during continuous finger tapping across a range of frequencies from 0.5Hz to 2.5Hz. The algorithm proved to be sensitive, correctly identifying at least 99% of all contacts in each of the finger tapping conditions that were tested. The mean absolute error of the contact detection is 14.7 ± 6 ms, while the mean absolute error of the release detection is 36.5 ± 36.3 ms. The proposed algorithm provides a method for the automatic detection of the temporal occurrences of the events of the finger tapping task using only a tri-axial accelerometer. The approach presented provides a means for objective assessment of finger tapping tasks for evaluation of the fine dexterity of the upper limb.

Posturographic measures did not improve the predictive power to identify recurrent falls in community-dwelling elderly fallers

OBJECTIVE

This study aimed to evaluate if posturography can be considered a recurrent fall predictor in elderly individuals.

METHODS

This was a cross-sectional study. A total of 124 subjects aged 60 to 88 years were evaluated and divided into two groups-the recurrent fallers (89) and single fallers (35) groups. Patients' sociodemographic characteristics were assessed, and clinical testing was performed. The functional test assessment instruments used were timed up and go test (TUGT), Berg Balance Scale (BBS), five times sit-to-stand test, and Falls Efficacy Scale (to measure fear of falling). Static posturography was performed in a force platform in the following three different situations-eyes open (EO), eyes closed (EC), and EO dual task.

RESULTS

There were significant differences between the single and recurrent fallers groups regarding the fear of falling, the Geriatric Depression Scale score, the mean speed calculated from the total displacement of the center point of pressure (COP) in all directions with EO, and the root mean square of the displacement from the COP in the mediolateral axis with EC. Based on the hierarchical logistic regression model, none of the studied posturographic variables was capable of significantly increasing the power of differentiation between the recurrent and single fallers groups. Only TUGT with a cognitive distractor (p<0.05) and the BBS (p<0.01) presented with significant independent predictive power.

CONCLUSION

TUGT with a cognitive distractor and the BBS were considered recurrent fall predictors in elderly fallers.

Fatigue Detection during Sit-To-Stand Test Based on Surface Electromyography and Acceleration: A Case Study

The latest studies of the 30-second sit-to-stand (30-STS) test aim to describe it by employing kinematic variables, muscular activity, or fatigue through electromyography (EMG) instead of a number of repetitions. The aim of the present study was to develop a detection system based on acceleration measured using a smartphone to analyze fatigue during the 30-STS test with surface electromyography as the criterion. This case study was carried out on one woman, who performed eight trials. EMG data from the lower limbs and trunk muscles, as well as trunk acceleration were recorded. Both signals from eight trials were preprocessed, being averaged and temporarily aligned. The EMG signal was processed, calculating the spectral centroid (SC) by Discrete Fourier Transform, while the acceleration signal was processed by Discrete Wavelet Transform to calculate its energy percentage. Regarding EMG, fatigue in the vastus medialis of the quadriceps appeared as a decrease in SC, with a descending slope of 12% at second 12, indicating fatigue. However, acceleration analysis showed an increase in the percentage of relative energy, acting like fatigue firing at second 19. This assessed fatigue according to two variables of a different nature. The results will help clinicians to obtain information about fatigue using an accessible and inexpensive device, i.e., as a smartphone.

Expanding instrumented gait testing in the community setting: A portable, depth-sensing camera captures joint motion in older adults

BACKGROUND

Currently, it is not feasible to obtain laboratory-based measures of joint motion in large numbers of older adults. We assessed the utility of a portable depth-sensing camera for quantifying hip and knee joint motion of older adults during mobility testing in the community.

METHODS

Participants were 52 older adults enrolled in the Rush Memory and Aging Project, a community-based cohort study of aging. In a subset, we compared dynamic hip and knee flexion/extension obtained via the depth-sensing camera with that obtained concurrently using a laboratory-based optoelectronic motion capture system. Then we recorded participants' annual instrumented gait assessment in the community setting with the depth-sensing camera and examined the inter-relationships of hip and knee range of motion (ROM) with mobility metrics derived from a wearable sensor and other mobility-related health measures.

RESULTS

In the community, we successfully acquired joint motion from 49/52 participants using the depth-sensing camera. Hip and knee ROMs were related to diverse sensor-derived metrics of mobility performance (hip: Pearson's r = 0.31 to 0.58; knee: Pearson's r = 0.29 to 0.51), as well as daily physical activity, conventional motor measures, self-report hip and knee pain and dysfunction, mobility disability, and falls.

CONCLUSIONS

The depth-sensing camera's high rate of successful data acquisition and correlations of its hip and knee ROMs with other mobility measures suggest that this device can provide a cost-efficient means of quantifying joint motion in large numbers of community-dwelling older adults who span the health spectrum.

Mobile Fall Risk Assessment Solution for Daily-Life Settings

Prevention of falls requires accurate means for fall risk assessment in order to identify persons at risk. This paper introduces a novel mobile fall risk assessment solution for daily-life settings. The solution contains an Android application that uses acceleration sensor data received via Bluetooth LE connection. The application guides through a simple walk test, analyzes the acceleration data measured from the acceleration sensor attached to the lower back and gives feedback about the fall risk for the user. Preliminary user tests with 12 healthy subjects were conducted to evaluate the feasibility of the solution. Each test subject performed three walks demonstrating normal, dragging and slow gait. The results showed that the acceleration features calculated by the application distinguish normal gait from dragging and slow gaits. Further collection of comprehensive data set with older adults is needed to adjust the application parameters appropriately for the target group.

Observational Study of a Wearable Sensor and Smartphone Application Supporting Unsupervised Exercises to Assess Pain and Stiffness

Background

Evaluation of pain and stiffness in patients with arthritis is largely based on participants retrospectively reporting their self-perceived pain/stiffness. This is subjective and may not accurately reflect the true impact of therapeutic interventions. We now have access to sensor-based systems to continuously capture objective information regarding movement and activity.

Objectives

We present an observational study aimed to collect sensor data from participants monitored while performing an unsupervised version of a standard motor task, known as the Five Times Sit to Stand (5×STS) test. The first objective was to explore whether the participants would perform the test regularly in their home environment, and do so in a correct and consistent manner. The second objective was to demonstrate that the measurements collected would enable us to derive an objective signal related to morning pain and stiffness.

Methods

We recruited a total of 45 participants, of whom 30 participants fulfilled pre-defined criteria for osteoarthritis, rheumatoid arthritis, or psoriatic arthritis and 15 participants were healthy volunteers. All participants wore accelerometers on their wrists, day and night for about 4 weeks. The participants were asked to perform the 5×STS test in their own home environment at the same time in the morning 3 times per week. We investigated the relationship between pain/stiffness and measurements collected during the 5×STS test by comparing the 5×STS test duration with the patient-reported outcome (PRO) questionnaires, filled in via a smartphone.

Results

During the study, we successfully captured accelerometer data from each participant for a period of 4 weeks. The participants performed 56% of the prescribed 5×STS tests. We observed that different tests made by the same participants were performed with subject-specific characteristics that remained consistent throughout the study. We showed that 5×STS test duration (the time taken to complete the 5×STS test) was significantly and robustly associated with the pain and stiffness intensity reported via the PROs, particularly the questions asked in the morning.

Conclusions

This study demonstrates the feasibility and usefulness of regular, sensor-based, monitored, unsupervised physical tests to objectively assess the impact of disease on function in the home environment. This approach may permit remote disease monitoring in clinical trials and support the development of novel endpoints from passively collected actigraphy data.

A Review of Activity Trackers for Senior Citizens: Research Perspectives, Commercial Landscape and the Role of the Insurance Industry

The objective assessment of physical activity levels through wearable inertial-based motion detectors for the automatic, continuous and long-term monitoring of people in free-living environments is a well-known research area in the literature. However, their application to older adults can present particular constraints. This paper reviews the adoption of wearable devices in senior citizens by describing various researches for monitoring physical activity indicators, such as energy expenditure, posture transitions, activity classification, fall detection and prediction, gait and balance analysis, also by adopting consumer-grade fitness trackers with the associated limitations regarding acceptability. This review also describes and compares existing commercial products encompassing activity trackers tailored for older adults, thus providing a comprehensive outlook of the status of commercially available motion tracking systems. Finally, the impact of wearable devices on life and health insurance companies, with a description of the potential benefits for the industry and the wearables market, was analyzed as an example of the potential emerging market drivers for such technology in the future.

Gait Velocity and Chair Sit-Stand-Sit Performance Improves Current Frailty-Status Identification

Frailty is characterized by a loss of functionality and is expected to affect 9.9% of people aged 65 and over. Here, current frailty classification is compared with a collection of selected kinematic parameters. A total of 718 elderly subjects (319 males and 399 females; age: 75.4 ± 6.1 years), volunteered to participate in this study and were classified according to Fried's criteria. Both the 30-s chair stand test (CST) and the 3-m walking test were performed and a set of kinematic parameters were obtained from a single inertial unit. A decision tree analysis was used to: 1) identify the most relevant frailty-related parameters and 2) compare validity of this classification. We found that a selected set of parameters from the 30-s CST (i.e., range of movement, acceleration, and power) were better at identifying frailty status than both the actual outcome of the test (i.e., cycles' number) and the normally used criteria (i.e., gait speed). For the pre-frail status, AUC improves from 0.531 using the actual test outcome and 0.516 with gait speed to 0.938 with the kinematic parameters criteria. In practice, this could improve the presyndrome identification and perform the appropriate actions to postpone the progression into the frail status.

Muscular Activity and Fatigue in Lower-Limb and Trunk Muscles during Different Sit-To-Stand Tests

Sit-to-stand (STS) tests measure the ability to get up from a chair, reproducing an important component of daily living activity. As this functional task is essential for human independence, STS performance has been studied in the past decades using several methods, including electromyography. The aim of this study was to measure muscular activity and fatigue during different repetitions and speeds of STS tasks using surface electromyography in lower-limb and trunk muscles. This cross-sectional study recruited 30 healthy young adults. Average muscle activation, percentage of maximum voluntary contraction, muscle involvement in motion and fatigue were measured using surface electrodes placed on the medial gastrocnemius (MG), biceps femoris (BF), vastus medialis of the quadriceps (QM), the abdominal rectus (AR), erector spinae (ES), rectus femoris (RF), soleus (SO) and the tibialis anterior (TA). Five-repetition STS, 10-repetition STS and 30-second STS variants were performed. MG, BF, QM, ES and RF muscles showed differences in muscle activation, while QM, AR and ES muscles showed significant differences in MVC percentage. Also, significant differences in fatigue were found in QM muscle between different STS tests. There was no statistically significant fatigue in the BF, MG and SO muscles of the leg although there appeared to be a trend of increasing fatigue. These results could be useful in describing the functional movements of the STS test used in rehabilitation programs, notwithstanding that they were measured in healthy young subjects.

Review: Are we stumbling in our quest to find the best predictor? Over-optimism in sensor-based models for predicting falls in older adults

The field of fall risk testing using wearable sensors is bustling with activity. In this Letter, the authors review publications which incorporated features extracted from sensor signals into statistical models intended to estimate fall risk or predict falls in older people. A review of these studies raises concerns that this body of literature is presenting over-optimistic results in light of small sample sizes, questionable modelling decisions and problematic validation methodologies (e.g. inherent problems with the overly-popular cross-validation technique, lack of external validation). There seem to be substantial issues in the feature selection process, whereby researchers select features before modelling begins based on their relation to the target, and either perform no validation or test the models on the same data used for their training. This, together with potential issues related to the large number of features and their correlations, inevitably leads to models with inflated accuracy that are unlikely to maintain their reported performance during everyday use in relevant populations. Indeed, the availability of rich sensor data and many analytical options provides intellectual and creative freedom for researchers, but should be treated with caution, and such pitfalls must be avoided if we desire to create generalisable prognostic tools of any clinical value.

Instrumenting gait with an accelerometer: a system and algorithm examination

Gait is an important clinical assessment tool since changes in gait may reflect changes in general health. Measurement of gait is a complex process which has been restricted to the laboratory until relatively recently. The application of an inexpensive body worn sensor with appropriate gait algorithms (BWM) is an attractive alternative and offers the potential to assess gait in any setting. In this study we investigated the use of a low-cost BWM, compared to laboratory reference using a robust testing protocol in both younger and older adults. We observed that the BWM is a valid tool for estimating total step count and mean spatio-temporal gait characteristics however agreement for variability and asymmetry results was poor. We conducted a detailed investigation to explain the poor agreement between systems and determined it was due to inherent differences between the systems rather than inability of the sensor to measure the gait characteristics. The results highlight caution in the choice of reference system for validation studies. The BWM used in this study has the potential to gather longitudinal (real-world) spatio-temporal gait data that could be readily used in large lifestyle-based intervention studies, but further refinement of the algorithm(s) is required.

Kinematic parameters to evaluate functional performance of sit-to-stand and stand-to-sit transitions using motion sensor devices: a systematic review

Clinicians commonly use questionnaires and tests based on daily life activities to evaluate physical function. However, the outcomes are usually more qualitative than quantitative and subtle differences are not detectable. In this review, we aim to assess the role of body motion sensors in physical performance evaluation, especially for the sit-to-stand and stand-to-sit transitions. In total, 53 full papers and conference abstracts on related topics were included and 16 different parameters related to transition performance were identified as potentially meaningful to explain certain disabilities and impairments. Transition duration is the most used to evaluate chair-related tests in real clinical settings. High-fall-risk fallers and frail subjects presented longer and more variable transition duration. Other kinematic parameters have also been highlighted in the literature as potential means to detect age-related impairments. In particular, vertical linear velocity and trunk tilt range were able to differentiate between different frailty levels. Frequency domain measures such as spectral edge frequency were also higher for elderly fallers. Lastly, approximate entropy values were larger for subjects with Parkinson's disease and were significantly reduced after treatment. This information could help clinicians in their evaluations as well as in prescribing a physical fitness program to correct a specific deficit.

Associations between quantitative mobility measures derived from components of conventional mobility testing and Parkinsonian gait in older adults

Objective

To provide objective measures which characterize mobility in older adults assessed in the community setting and to examine the extent to which these measures are associated with parkinsonian gait.

Methods

During conventional mobility testing in the community-setting, 351 ambulatory non-demented Memory and Aging Project participants wore a belt with a whole body sensor that recorded both acceleration and angular velocity in 3 directions. We used measures derived from these recordings to quantify 5 subtasks including a) walking, b) transition from sit to stand, c) transition from stand to sit, d) turning and e) standing posture. Parkinsonian gait and other mild parkinsonian signs were assessed with a modified version of the original Unified Parkinson’s Disease Rating Scale (mUPDRS).

Results

In a series of separate regression models which adjusted for age and sex, all 5 mobility subtask measures were associated with parkinsonian gait and accounted for 2% to 32% of its variance. When all 5 subtask measures were considered in a single model, backward elimination showed that measures of walking sit to stand and turning showed independent associations with parkinsonian gait and together accounted for more than 35% of its variance. Cross-validation using data from a 2^nd group of 258 older adults showed similar results. In similar analyses, only walking was associated with bradykinesia and sway with tremor.

Interpretation

Quantitative mobility subtask measures vary in their associations with parkinsonian gait scores and other parkinsonian signs in older adults. Quantifying the different facets of mobility has the potential to facilitate the clinical characterization and understanding the biologic basis for impaired mobility in older adults.

Review of fall risk assessment in geriatric populations using inertial sensors

Background

Falls are a prevalent issue in the geriatric population and can result in damaging physical and psychological consequences. Fall risk assessment can provide information to enable appropriate interventions for those at risk of falling. Wearable inertial-sensor-based systems can provide quantitative measures indicative of fall risk in the geriatric population.

Methods

Forty studies that used inertial sensors to evaluate geriatric fall risk were reviewed and pertinent methodological features were extracted; including, sensor placement, derived parameters used to assess fall risk, fall risk classification method, and fall risk classification model outcomes.

Results

Inertial sensors were placed only on the lower back in the majority of papers (65%). One hundred and thirty distinct variables were assessed, which were categorized as position and angle (7.7%), angular velocity (11.5%), linear acceleration (20%), spatial (3.8%), temporal (23.1%), energy (3.8%), frequency (15.4%), and other (14.6%). Fallers were classified using retrospective fall history (30%), prospective fall occurrence (15%), and clinical assessment (32.5%), with 22.5% using a combination of retrospective fall occurrence and clinical assessments. Half of the studies derived models for fall risk prediction, which reached high levels of accuracy (62-100%), specificity (35-100%), and sensitivity (55-99%).

Conclusions

Inertial sensors are promising sensors for fall risk assessment. Future studies should identify fallers using prospective techniques and focus on determining the most promising sensor sites, in conjunction with determination of optimally predictive variables. Further research should also attempt to link predictive variables to specific fall risk factors and investigate disease populations that are at high risk of falls.

An evaluation of the 30-s chair stand test in older adults: frailty detection based on kinematic parameters from a single inertial unit

BACKGROUND

A growing interest in frailty syndrome exists because it is regarded as a major predictor of co-morbidities and mortality in older populations. Nevertheless, frailty assessment has been controversial, particularly when identifying this syndrome in a community setting. Performance tests such as the 30-second chair stand test (30-s CST) are a cornerstone for detecting early declines in functional independence. Additionally, recent advances in body-fixed sensors have enhanced the sensors' ability to automatically and accurately evaluate kinematic parameters related to a specific movement performance. The purpose of this study is to use this new technology to obtain kinematic parameters that can identify frailty in an aged population through the performance the 30-s CST.

METHODS

Eighteen adults with a mean age of 54 years, as well as sixteen pre-frail and thirteen frail patients with mean ages of 78 and 85 years, respectively, performed the 30-s CST while their trunk movements were measured by a sensor-unit at vertebra L3. Sit-stand-sit cycles were determined using both acceleration and orientation information to detect failed attempts. Movement-related phases (i.e. impulse, stand-up, and sit-down) were differentiated based on seat off and seat on events. Finally, the kinematic parameters of the impulse, stand-up and sit-down phases were obtained to identify potential differences across the three frailty groups.

RESULTS

For the stand-up and sit-down phases, velocity peaks and "modified impulse" parameters clearly differentiated subjects with different frailty levels (p < 0.001). The trunk orientation range during the impulse phase was also able to classify a subject according to his frail syndrome (p < 0.001). Furthermore, these parameters derived from the inertial units (IUs) are sensitive enough to detect frailty differences not registered by the number of completed cycles which is the standard test outcome.

CONCLUSIONS

This study shows that IUs can enhance the information gained from tests currently used in clinical practice, such as the 30-s CST. Parameters such as velocity peaks, impulse, and orientation range are able to differentiate between adults and older populations with different frailty levels. This study indicates that early frailty detection could be possible in clinical environments, and the subsequent interventions to correct these disabilities could be prescribed before further degradation occurs.

Using a body-fixed sensor to identify subclinical gait difficulties in older adults with IADL disability: maximizing the output of the timed up and go

Objective

The identification and documentation of subclinical gait impairments in older adults may facilitate the appropriate use of interventions for preventing or delaying mobility disability. We tested whether measures derived from a single body-fixed sensor worn during traditional Timed Up and Go (TUG) testing could identify subclinical gait impairments in community dwelling older adults without mobility disability.

Methods

We used data from 432 older adults without dementia (mean age 83.30±7.04 yrs, 76.62% female) participating in the Rush Memory and Aging Project. The traditional TUG was conducted while subjects wore a body-fixed sensor. We derived measures of overall TUG performance and different subtasks including transitions (sit-to-stand, stand-to-sit), walking, and turning. Multivariate analysis was used to compare persons with and without mobility disability and to compare individuals with and without Instrumental Activities of Daily Living disability (IADL-disability), all of whom did not have mobility disability.

Results

As expected, individuals with mobility disability performed worse on all TUG subtasks (p<0.03), compared to those who had no mobility disability. Individuals without mobility disability but with IADL disability had difficulties with turns, had lower yaw amplitude (p<0.004) during turns, were slower (p<0.001), and had less consistent gait (p<0.02).

Conclusions

A single body-worn sensor can be employed in the community-setting to complement conventional gait testing. It provides a wide range of quantitative gait measures that appear to help to identify subclinical gait impairments in older adults.

Accelerometry-based prediction of movement dynamics for balance monitoring

This paper proposes a 2D functional evaluation tool for estimating subject-specific body segment parameters, which uses a simple motor task (repeated sit-to-stand, rSTS), recorded with one single-axis accelerometer (SAA) per segment and a force plate (FP). After this preliminary estimation, the accelerometer alone is used to make quasi-real-time predictions of ground reaction force (anterior/posterior, F ( X ), and vertical, F ( Z ), components), center of pressure (CoP) and center of mass (CoM), during rSTS and postural oscillation in the sagittal plane. These predicted dynamic variables, as well as those obtained using anthropometric parameters derived from De Leva, were compared to actual FP outputs in terms of root mean-squared errors (RMSEs). Using De Leva's parameters in place of those estimated, RMSEs increase from 12 to 21 N (F ( X )), from 21 to 24 N (F ( Z )), and from 21.1 to 55.6 mm (CoP) in rSTS; similarly, RMSEs increase from 3.1 to 3.3 N (F ( X )) and from 5.5 to 6.6 mm (CoP) in oscillatory trials. A telescopic inverted pendulum model was adopted to analyze the balance control in rSTS using only predicted CoP and CoM. Results suggest that one SAA per segment is sufficient to predict the dynamics of a biomechanical model of any degrees of freedom.