Toward Automating Clinical Assessments: A Survey of the Timed Up and Go

Relationship between Driving Ability and Physical Fitness Factors in Older Adults: A Multiple Linear Regression Analysis

The number of older drivers is increasing with the aging population; this has led to concerns about traffic accidents involving older drivers. For older adults, the automobile is not just a means of transportation, but a life necessity that promotes social activities and maintains and improves health-related quality of life. In this study, we aimed to clarify the relationship between driving ability and physical fitness factors among 70 older adult drivers using a single regression analysis and multiple regression models adjusted for age, sex, and other factors. Driving ability was evaluated by driving an actual car on an ordinary road without a simulator. The single regression analysis revealed no relationship between driving ability and any physical fitness factor. In the multiple regression model analysis, only grip strength was an important explanatory factor; however, the driving ability scores decreased as grip strength scores increased. By clarifying the physical fitness factors that influence the maintenance and improvement of driving ability, it is possible to propose more efficient intervention programs to maintain and improve driving ability. We could not identify the relevant physical fitness factors in this study; therefore, further research is required to improve safe driving among older adults.

"TiC-TUG": technology in clinical practice using the instrumented timed up and go test-a scoping review

Digitized assessments have a considerable potential to guide clinicial decision making and monitor progress and disease trajectories. The Timed Up and Go test (TUG) has been long established for assessment in geriatric medicine and instrumented versions (iTUG) have been developed and validated. This scoping review includes studies that applied the iTUG and aims to identify use cases to show where and how iTUG assessment could guide interventions and clinical management. The literature search was limited to peer-reviewed studies that performed pre- and post-intervention measurements with a 3-meter TUG instrumented with body-worn technology in samples of at least 20 subjects aged 60+ years. Of 3018 identified articles 20 were included. Four clinical use cases were identified: stratification for subsequent therapy, monitoring of disease or treatment-associated changes and evaluation of interventions in patients with idiopathic normal pressure hydrocephalus (1), and patients with Parkinson's disease (2); monitoring after joint replacement surgery (3), and evaluation after different exercise and rehabilitation interventions (4). The included studies show diversity in terms of iTUG technology and procedures. The identified use cases highlight clinical relevance and high potential for the clinical application of the iTUG. A consensual approach as well as comprehensive reporting would help to further exploit the potential of the iTUG to support clinical management. Future studies should investigate the benefits of segmental iTUG analysis, responsiveness and participants' perspectives on clinically meaningful changes in iTUG.

Enhancing Driving Ability in Older Adults through Health Exercises and Physical Activity: A Randomized Controlled Trial

The global rise in the aging driving population has heightened concerns about traffic incidents involving this demographic. Beyond transportation, automobiles represent a vital lifeline for older adults, fostering social activities and influencing their health-related quality of life. This study explores improving and sustaining driving ability among older adults with anticipated declines through health-conscious exercises. Sixty-eight participants were randomly allocated into two groups. The exercise-oriented group (E-group) engaged in twelve 90 min health and exercise sessions over twelve weeks, while the control group (C-group) maintained their regular daily routines and did not receive any specific interventions during this period. The focal point of assessment was driving ability, as evaluated by a person using a real car on public roads without using a simulator. Driving ability and physical fitness were assessed before the intervention in both groups. Post-intervention measurements occurred twelve weeks after the initial gauging, encompassing both cohorts. Comparative analysis of pre- and post-intervention changes was executed between the two groups. The E-group demonstrated improved overall driving ability compared to the C-group. The results suggest that healthy exercise and physical activity may maintain and enhance driving ability for older adults.

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.

All Knitted and Integrated Soft Wearable of High Stretchability and Sensitivity for Continuous Monitoring of Human Joint Motion

E-textiles have recently gained significant traction in the development of soft wearables for healthcare applications. However, there have been limited works on wearable e-textiles with embedded stretchable circuits. Here, stretchable conductive knits with tuneable macroscopic electrical and mechanical properties are developed by varying the yarn combination and the arrangement of stitch types at the meso-scale. Highly extensible piezoresistive strain sensors are designed (>120% strain) with high sensitivity (gauge factor 8.47) and durability (>100,000 cycles), interconnects (>140% strain) and resistors (>250% strain), optimally arranged to form a highly stretchable sensing circuit. The wearable is knitted with a computer numerical control (CNC) knitting machine that offers a cost effective and scalable fabrication method with minimal post-processing. The real-time data from the wearable is transmitted wirelessly using a custom designed circuit board. In this work, an all knitted and fully integrated soft wearable is demonstrated for wireless and continuous real-time sensing of knee joint motion of multiple subjects performing various activities of daily living.

Automation of the Timed Up and Go Test Using a Doppler Radar System for Gait and Balance Analysis in Elderly People

The timed up and go (TUG) test is a simple, valid, and reliable clinical tool that is widely used to assess mobility in elderly people. Several research studies have been conducted to automate the TUG test using wearable sensors or motion-tracking systems. Despite their promising results, the adopted technological systems present inconveniences in terms of acceptability and privacy protection. In this work, we propose to overcome these problems by using a Doppler radar system set into the backrest of a chair in order to automate the TUG test and extract additional information from its phases (i.e., transfer, walk, and turn). We intend to segment its phases and extract spatiotemporal gait parameters automatically. Our methodology is mainly based on a multiresolution analysis of radar signals. We proposed a segmentation technique based on the extraction of limbs oscillations signals through a semisupervised machine learning approach, on the one hand, and the application of the DARC algorithm on the other hand. Once the speed signals of torso and limbs oscillations were detected, we suggested estimating 14 gait parameters. All our approaches were validated by comparing outcomes to those obtained from a reference Vicon system. High correlation coefficients were obtained by comparing the speed signals of the torso (ρ=0.8), the speed signals of limbs oscillations (ρ=0.91), the initial and final indices of TUG phases (ρ=0.95), and the extracted parameters (percentage error < 4.8) obtained after radar signal processing to those obtained from the Vicon system.

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.

Subtask Segmentation Methods of the Timed Up and Go Test and L Test Using Inertial Measurement Units—A Scoping Review

The Timed Up and Go test (TUG) and L Test are functional mobility tests that allow healthcare providers to assess a person’s balance and fall risk. Segmenting these mobility tests into their respective subtasks, using sensors, can provide further and more precise information on mobility status. To identify and compare current methods for subtask segmentation using inertial sensor data, a scoping review of the literature was conducted using PubMed, Scopus, and Google Scholar. Articles were identified that described subtask segmentation methods for the TUG and L Test using only inertial sensor data. The filtering method, ground truth estimation device, demographic, and algorithm type were compared. One article segmenting the L Test and 24 articles segmenting the TUG met the criteria. The articles were published between 2008 and 2022. Five studies used a mobile smart device’s inertial measurement system, while 20 studies used a varying number of external inertial measurement units. Healthy adults, people with Parkinson’s Disease, and the elderly were the most common demographics. A universally accepted method for segmenting the TUG test and the L Test has yet to be published. Angular velocity in the vertical and mediolateral directions were common signals for subtask differentiation. Increasing sample sizes and furthering the comparison of segmentation methods with the same test sets will allow us to expand the knowledge generated from these clinically accessible tests.

Shared-Medical Appointment for Screening and Risk Assessment for Fall Prevention

Background: The median age of Americans is rising and fall risk increases with age. While the causes of falls are multifactorial, falls risk can be reduced. Only a small percentage of older-adults report being asked about fall risk or falls. The CDC has initiated a Stopping Elderly Accidents, Deaths and Injuries (STEADI) toolkit, but penetration into practice has been slow. To address this, we implemented a Falls Prevention Shared Medical Appointment (SMA) at an academic internal medicine clinic. Methods: Patients were referred to the SMA and scheduled per their preference virtually or in-person. Patients attended a nurse visit for appropriate fallrisk related screening, followed by the SMA with two physicians for review of medical history, fall screening results and implementation of fall reduction strategies. Follow-up survey of the patients assessed program effectiveness. Results: Fifty-two patients were seen/assessed between November 2021 and February 2023 with SMAs ranging from 3 to 5 patients with an average age of 77 (=/- 6.7). Questionnaire self-reported risk factors, self-reported strength, and polypharmacy were associated with objective markers of increased fall risk. Survey results indicate acceptability of this model. Conclusion: Falls prevention SMAs can be effective. More work is needed to further delineate and refine cohort selection.

Automated Cognitive Health Assessment Using Partially Complete Time Series Sensor Data

BACKGROUND

Behavior and health are inextricably linked. As a result, continuous wearable sensor data offer the potential to predict clinical measures. However, interruptions in the data collection occur, which create a need for strategic data imputation.

OBJECTIVE

The objective of this work is to adapt a data generation algorithm to impute multivariate time series data. This will allow us to create digital behavior markers that can predict clinical health measures.

METHODS

We created a bidirectional time series generative adversarial network to impute missing sensor readings. Values are imputed based on relationships between multiple fields and multiple points in time, for single time points or larger time gaps. From the complete data, digital behavior markers are extracted and are mapped to predicted clinical measures.

RESULTS

We validate our approach using continuous smartwatch data for n = 14 participants. When reconstructing omitted data, we observe an average normalized mean absolute error of 0.0197. We then create machine learning models to predict clinical measures from the reconstructed, complete data with correlations ranging from r = 0.1230 to r = 0.7623. This work indicates that wearable sensor data collected in the wild can be used to offer insights on a person's health in natural settings.

Deep Learning-Based Subtask Segmentation of Timed Up-and-Go Test Using RGB-D Cameras

The timed up-and-go (TUG) test is an efficient way to evaluate an individual's basic functional mobility, such as standing up, walking, turning around, and sitting back. The total completion time of the TUG test is a metric indicating an individual's overall mobility. Moreover, the fine-grained consumption time of the individual subtasks in the TUG test may provide important clinical information, such as elapsed time and speed of each TUG subtask, which may not only assist professionals in clinical interventions but also distinguish the functional recovery of patients. To perform more accurate, efficient, robust, and objective tests, this paper proposes a novel deep learning-based subtask segmentation of the TUG test using a dilated temporal convolutional network with a single RGB-D camera. Evaluation with three different subject groups (healthy young, healthy adult, stroke patients) showed that the proposed method demonstrated better generality and achieved a significantly higher and more robust performance (healthy young = 95.458%, healthy adult = 94.525%, stroke = 93.578%) than the existing rule-based and artificial neural network-based subtask segmentation methods. Additionally, the results indicated that the input from the pelvis alone achieved the best accuracy among many other single inputs or combinations of inputs, which allows a real-time inference (approximately 15 Hz) in edge devices, such as smartphones.

HR1 Robot: An Assistant for Healthcare Applications

According to the World Health Organization^1,2 the percentage of healthcare dependent population, such as elderly and people with disabilities, among others, will increase over the next years. This trend will put a strain on the health and social systems of most countries. The adoption of robots could assist these health systems in responding to this increased demand, particularly in high intensity and repetitive tasks. In a previous work, we compared a Socially Assistive Robot (SAR) with a Virtual Agent (VA) during the execution of a rehabilitation task. The SAR consisted of a humanoid R1 robot, while the Virtual Agent represented its simulated counter-part. In both cases, the agents evaluated the participants’ motions and provided verbal feedback. Participants reported higher levels of engagement when training with the SAR. Given that the architecture has been proven to be successful for a rehabilitation task, other sets of repetitive tasks could also take advantage of the platform, such as clinical tests. A commonly performed clinical trial is the Timed Up and Go (TUG), where the patient has to stand up, walk 3 m to a goal line and back, and sit down. To handle this test, we extended the architecture to evaluate lower limbs’ motions, follow the participants while continuously interacting with them, and verify that the test is completed successfully. We implemented the scenario in Gazebo, by simulating both participants and the interaction with the robot³. A full interactive report is created when the test is over, providing the extracted information to the specialist. We validate the architecture in three different experiments, each with 1,000 trials, using the Gazebo simulation. These experiments evaluate the ability of this architecture to analyse the patient, verify if they are able to complete the TUG test, and the accuracy of the measurements obtained during the test. This work provides the foundations towards more thorough clinical experiments with a large number of participants with a physical platform in the future. The software is publicly available in the assistive-rehab repository⁴ and fully documented.

Measurement System for Unsupervised Standardized Assessments of Timed Up and Go Test and 5 Times Chair Rise Test in Community Settings—A Usability Study

Comprehensive measurements are needed in older populations to detect physical changes, initiate prompt interventions, and prevent functional decline. While established instruments such as the Timed Up and Go (TUG) and 5 Times Chair Rise Test (5CRT) require trained clinicians to assess corresponding functional parameters, the unsupervised screening system (USS), developed in a two-stage participatory design process, has since been introduced to community-dwelling older adults. In a previous article, we investigated the USS’s measurement of the TUG and 5CRT in comparison to conventional stop-watch methods and found a high sensitivity with significant correlations and coefficients ranging from 0.73 to 0.89. This article reports insights into the design process and evaluates the usability of the USS interface. Our analysis showed high acceptance with qualitative and quantitative methods. From participant discussions, suggestions for improvement and functions for further development could be derived and discussed. The evaluated prototype offers a high potential for early detection of functional limitations in elderly people and should be tested with other target groups in other locations.

A Mobile App (FallSA) to Identify Fall Risk Among Malaysian Community-Dwelling Older Persons: Development and Validation Study

BACKGROUND

Recent falls prevention guidelines recommend early routine fall risk assessment among older persons.

OBJECTIVE

The purpose of this study was to develop a Falls Screening Mobile App (FallSA), determine its acceptance, concurrent validity, test-retest reliability, discriminative ability, and predictive validity as a self-screening tool to identify fall risk among Malaysian older persons.

METHODS

FallSA acceptance was tested among 15 participants (mean age 65.93 [SD 7.42] years); its validity and reliability among 91 participants (mean age 67.34 [SD 5.97] years); discriminative ability and predictive validity among 610 participants (mean age 71.78 [SD 4.70] years). Acceptance of FallSA was assessed using a questionnaire, and it was validated against a comprehensive fall risk assessment tool, the Physiological Profile Assessment (PPA). Participants used FallSA to test their fall risk repeatedly twice within an hour. Its discriminative ability and predictive validity were determined by comparing participant fall risk scores between fallers and nonfallers and prospectively through a 6-month follow-up, respectively.

RESULTS

The findings of our study showed that FallSA had a high acceptance level with 80% (12/15) of older persons agreeing on its suitability as a falls self-screening tool. Concurrent validity test demonstrated a significant moderate correlation (r=.518, P<.001) and agreement (k=.516, P<.001) with acceptable sensitivity (80.4%) and specificity (71.1%). FallSA also had good reliability (intraclass correlation .948; 95% CI .921-.966) and an internal consistency (α=.948, P<.001). FallSA score demonstrated a moderate to strong discriminative ability in classifying fallers and nonfallers. FallSA had a predictive validity of falls with positive likelihood ratio of 2.27, pooled sensitivity of 82% and specificity of 64%, and area under the curve of 0.802.

CONCLUSIONS

These results suggest that FallSA is a valid and reliable fall risk self-screening tool. Further studies are required to empower and engage older persons or care givers in the use of FallSA to self-screen for falls and thereafter to seek early prevention intervention.

A Comparative Evaluation of Inertial Sensors for Gait and Jump Analysis

Gait and jump anomalies are often used as indicators to identify the presence and state of disorders that involve motor symptoms. Physical tests are often performed in specialized laboratories, which offer reliable and accurate results, but require long and costly analyses performed by specialized personnel. The use of inertial sensors for gait and jump evaluation offers an easy-to-use low-cost alternative, potentially applicable by the patients themselves at home. In this paper, we compared three inertial measurement units that are available on the market by means of well-known standardized tests for the evaluation of gait and jump behavior. The aim of the study was to highlight the strengths and weaknesses of each of the tested sensors, considered in different tests, by comparing data collected on two healthy subjects. Data were processed to identify the phases of the movement and the possible inaccuracies of each sensor. The analysis showed that some of the considered inertial units could be reliably used to identify the gait and jump phases and could be employed to detect anomalies, potentially suggesting the presence of disorders.

Balance Measurement Using Microsoft Kinect v2: Towards Remote Evaluation of Patient with the Functional Reach Test

To prevent falls, it is important to measure periodically the balance ability of an individual using reliable clinical tests. As Red Green Blue Depth (RGBD) devices have been increasingly used for balance rehabilitation at home, they may also be used to assess objectively the balance ability and determine the effectiveness of a therapy. For this, we developed a system based on the Microsoft Kinect v2 for measuring the Functional Reach Test (FRT); one of the most used balance clinical tools to predict falls. Two experiments were conducted to compare the FRT measures computed by our system using the Microsoft Kinect v2 with those obtained by the standard method, i.e., manually. In terms of validity, we found a very strong correlation between the two methods (r = 0.97 and r = 0.99 (p < 0.05), for experiments 1 and 2, respectively). However, we needed to correct the measurements using a linear model to fit the data obtained by the Kinect system. Consequently, a linear regression model has been applied and examining the regression assumptions showed that the model works well for the data. Applying the paired t-test to the data after correction indicated that there is no statistically significant difference between the measurements obtained by both methods. As for the reliability of the test, we obtained good to excellent within repeatability of the FRT measurements tracked by Kinect (ICC = 0.86 and ICC = 0.99, for experiments 1 and 2, respectively). These results suggested that the Microsoft Kinect v2 device is reliable and adequate to calculate the standard FRT.

Reliability and minimal detectable change values for performance-based measures of physical functioning in the Canadian Longitudinal Study on Aging (CLSA)

BACKGROUND

The aim of this study was to determine the relative and absolute reliabilities of five key performance-based measures of physical function in the Canadian Longitudinal Study on Aging (CLSA).

METHODS

An age-stratified sub-sample of 147 participants from the CLSA who were undergoing their 3-year data collection visit participated in two repeat visits (within one week). Participants underwent tests of grip strength, 4-metre gait speed, Timed Up and Go (TUG), chair-rise and single-leg stance (left, right, mean, maximum). Intra-class correlation coefficients (ICC), standard error of measurement (SEM) and minimal detectable change (MDC) values were calculated.

RESULTS

The relative reliability for grip strength was excellent (ICC = 0.95); the TUG and single-leg stance tests had good reliability (ICC = 0.80 or 0.78-0.82, respectively); gait speed and the chair-rise test had moderate reliability (ICC=0.64 for both) for participants overall. For participants between 50 and 64 years, TUG and gait speed had poor reliabilities (ICC = 0.38 or 0.33, respectively). For participants aged 75+ years, the single-leg stance had poor reliability (ICC=0.30-0.39). The MDC90 was about 6 kg for grip strength, 2.3 seconds for TUG, 0.2 metres/second for gait speed, 5.2 seconds for chair-rise, and ranged from 22.8 to 26.2 seconds for the single-leg stance.

CONCLUSIONS

Among community-dwelling Canadians >50 years old, the reliabilities of the CLSA measures were moderate to excellent. The TUG and gait speed in the youngest age group, and the single-leg stance in oldest age group, showed poor reliability. MDC values can be used to interpret changes over time.

Multiphase Identification Algorithm for Fall Recording Systems Using a Single Wearable Inertial Sensor

Fall-related information can help clinical professionals make diagnoses and plan fall prevention strategies. The information includes various characteristics of different fall phases, such as falling time and landing responses. To provide the information of different phases, this pilot study proposes an automatic multiphase identification algorithm for phase-aware fall recording systems. Seven young adults are recruited to perform the fall experiment. One inertial sensor is worn on the waist to collect the data of body movement, and a total of 525 trials are collected. The proposed multiphase identification algorithm combines machine learning techniques and fragment modification algorithm to identify pre-fall, free-fall, impact, resting and recovery phases in a fall process. Five machine learning techniques, including support vector machine, k-nearest neighbor (kNN), naïve Bayesian, decision tree and adaptive boosting, are applied to identify five phases. Fragment modification algorithm uses the rules to detect the fragment whose results are different from the neighbors. The proposed multiphase identification algorithm using the kNN technique achieves the best performance in 82.17% sensitivity, 85.74% precision, 73.51% Jaccard coefficient, and 90.28% accuracy. The results show that the proposed algorithm has the potential to provide automatic fine-grained fall information for clinical measurement and assessment.

Fusing Ambient and Mobile Sensor Features Into a Behaviorome for Predicting Clinical Health Scores

Advances in machine learning and low-cost, ubiquitous sensors offer a practical method for understanding the predictive relationship between behavior and health. In this study, we analyze this relationship by building a behaviorome, or set of digital behavior markers, from a fusion of data collected from ambient and wearable sensors. We then use the behaviorome to predict clinical scores for a sample of n = 21 participants based on continuous data collected from smart homes and smartwatches and automatically labeled with corresponding activity and location types. To further investigate the relationship between domains, including participant demographics, self-report and external observation-based health scores, and behavior markers, we propose a joint inference technique that improves predictive performance for these types of high-dimensional spaces. For our participant sample, we observe correlations ranging from small to large for the clinical scores. We also observe an improvement in predictive performance when multiple sensor modalities are used and when joint inference is employed.

Assessing elderly's functional balance and mobility via analyzing data from waist-mounted tri-axial wearable accelerometers in timed up and go tests

Background

Poor balance has been cited as one of the key causal factors of falls. Timely detection of balance impairment can help identify the elderly prone to falls and also trigger early interventions to prevent them. The goal of this study was to develop a surrogate approach for assessing elderly’s functional balance based on Short Form Berg Balance Scale (SFBBS) score.

Methods

Data were collected from a waist-mounted tri-axial accelerometer while participants performed a timed up and go test. Clinically relevant variables were extracted from the segmented accelerometer signals for fitting SFBBS predictive models. Regularized regression together with random-shuffle-split cross-validation was used to facilitate the development of the predictive models for automatic balance estimation.

Results

Eighty-five community-dwelling older adults (72.12 ± 6.99 year) participated in our study. Our results demonstrated that combined clinical and sensor-based variables, together with regularized regression and cross-validation, achieved moderate-high predictive accuracy of SFBBS scores (mean MAE = 2.01 and mean RMSE = 2.55). Step length, gender, gait speed and linear acceleration variables describe the motor coordination were identified as significantly contributed variables of balance estimation. The predictive model also showed moderate-high discriminations in classifying the risk levels in the performance of three balance assessment motions in terms of AUC values of 0.72, 0.79 and 0.76 respectively.

Conclusions

The study presented a feasible option for quantitatively accurate, objectively measured, and unobtrusively collected functional balance assessment at the point-of-care or home environment. It also provided clinicians and elderly with stable and sensitive biomarkers for long-term monitoring of functional balance.

Validation of a Laser Ranged Scanner-Based Detection of Spatio-Temporal Gait Parameters Using the aTUG Chair

This article covers the suitability to measure gait-parameters via a Laser Range Scanner (LRS) that was placed below a chair during the walking phase of the Timed Up&Go Test in a cohort of 92 older adults (mean age 73.5). The results of our study demonstrated a high concordance of gait measurements using a LRS in comparison to the reference GAITRite walkway. Most of aTUG's gait parameters demonstrate a strong correlation coefficient with the GAITRite, indicating high measurement accuracy for the spatial gait parameters. Measurements of velocity had a correlation coefficient of 99%, which can be interpreted as an excellent measurement accuracy. Cadence showed a slightly lower correlation coefficient of 96%, which is still an exceptionally good result, while step length demonstrated a correlation coefficient of 98% per leg and stride length with an accuracy of 99% per leg. In addition to confirming the technical validation of the aTUG regarding its ability to measure gait parameters, we compared results from the GAITRite and the aTUG for several parameters (cadence, velocity, and step length) with results from the Berg Balance Scale (BBS) and the Activities-Specific Balance Confidence-(ABC)-Scale assessments. With confidence coefficients for BBS and velocity, cadence and step length ranging from 0.595 to 0.798 and for ABC ranging from 0.395 to 0.541, both scales demonstrated only a medium-sized correlation. Thus, we found an association of better walking ability (represented by the measured gait parameters) with better balance (BBC) and balance confidence (ABC) overall scores via linear regression. This results from the fact that the BBS incorporates both static and dynamic balance measures and thus, only partly reflects functional requirements for walking. For the ABC score, this effect was even more pronounced. As this is to our best knowledge the first evaluation of the association between gait parameters and these balance scores, we will further investigate this phenomenon and aim to integrate further measures into the aTUG to achieve an increased sensitivity for balance ability.

Automatic Functional Shoulder Task Identification and Sub-task Segmentation Using Wearable Inertial Measurement Units for Frozen Shoulder Assessment

Advanced sensor technologies have been applied to support frozen shoulder assessment. Sensor-based assessment tools provide objective, continuous and quantitative information for evaluation and diagnosis. However, the current tools for assessment of functional shoulder tasks mainly rely on manual operation. It may cause several technical issues to the reliability and usability of the assessment tool, including manual bias during the recording and additional efforts for data labeling. To tackle these issues, this pilot study aims to propose an automatic functional shoulder task identification and sub-task segmentation system using inertial measurement units to provide reliable shoulder task labeling and sub-task information for clinical professionals. The proposed method combines machine learning models and rule-based modification to identify shoulder tasks and segment sub-tasks accurately. A hierarchical design is applied to enhance the efficiency and performance of the proposed approach. Nine healthy subjects and nine frozen shoulder patients are invited to perform five common shoulder tasks in the lab-based and clinical environments, respectively. The experimental results show that the proposed method can achieve 87.11% F-score for shoulder task identification, and 83.23% F-score and 427 mean absolute time errors (milliseconds) for sub-task segmentation. The proposed approach demonstrates the feasibility of the proposed method to support reliable evaluation for clinical assessment.

Description of novel instrumented analysis of the Four Square Step Test with clinical application: A pilot study

BACKGROUND

Falls are a common problem for adults in the United States raising concerns about injuries and the resulting economic burden. As a result, it is critical to develop objective measures to assess dynamic balance and the track progress related to interventions or disease progression over time.

RESEARCH QUESTION

Are there differences in balance between individuals in the community, individuals post-stroke, persons with Multiple Sclerosis (MS), and individuals living with Parkinson's Disease (PD) as measured with a new instrumented Four Square Step Test (i-FSST)?

METHODS

The i-FSST was utilized to assess dynamic balance in 41 individuals (11 community dwelling adults and 10 individuals in each group of persons post stroke, with PD, and with MS). Outcome data including the overall duration of the FSST as well unique temporal-spatial stepping patterns through the test, timing of transitions between each quadrant, and the time in each quadrant prior to transitioning.

RESULTS

One-way ANOVAs were conducted to determine whether i-FSST duration, Over Double Support (ODS), and Changes in Main Support (CMS) differed by participants' groups. There was a significant difference between groups in test Duration (F = 9.56, P =  .000), ODS (F = 15.71, P =  .001), and CMS (F = 7.03, P =  .001). Further differences in these variables were found between various groups using Bonferroni post-hoc testing.

SIGNIFICANCE

The i-FSST is an innovative and potentially beneficial tool for quantitatively measuring the dynamics that occur in the traditional FSST including a general measure of dynamic balance as well as transition times and stability during the test. This technology can provide objective data on stability, weight shifting, and weight acceptance that may guide interventions and further assessment.

Dimensional reduction of balance parameters in risk of falling evaluation using a minimal number of force-sensitive resistors

Purpose. As the instrumented insole is available for a wide commercial range in the retail trade, this study aims to reduce its overall cost using fewer sensors by carrying out an effective risk of falling evaluation. Methods. We compared the effect of reducing balance parameters using four and three force-sensing resistors (FSRs) of an instrumented insole. Data were previously collected among elderly participants during a Timed Up and Go (TUG) test. Results. While reducing the number of balance parameters, during sit-to-stand and stand-to-sit activities, the risk scores using four FSRs were not significantly different compared with three FSRs. Parameter reduction did not show any significant loss of information among the study population using four FSRs. For certain configurations of three FSRs, a significant effect of information loss was found in the study participants, revealing the importance of investigating the sensor locations in the process. Conclusions. We conclude that it is feasible to estimate a risk index during a TUG test not only after reducing the number of needed sensing units from four to three FSRs but also after reducing the number of balance parameters. The three FSRs should be located at strategic positions to avoid a significant loss of information.

Predicting Advanced Balance Ability and Mobility with an Instrumented Timed Up and Go Test

Extensive test batteries are often needed to obtain a comprehensive picture of a person’s functional status. Many test batteries are not suitable for active and healthy adults due to ceiling effects, or require a lot of space, time, and training. The Community Balance and Mobility Scale (CBMS) is considered a gold standard for this population, but the test is complex, as well as time- and resource intensive. There is a strong need for a faster, yet sensitive and robust test of physical function in seniors. We sought to investigate whether an instrumented Timed Up and Go (iTUG) could predict the CBMS score in 60 outpatients and healthy community-dwelling seniors, where features of the iTUG were predictive, and how the prediction of CBMS with the iTUG compared to standard clinical tests. A partial least squares regression analysis was used to identify latent components explaining variation in CBMS total score. The model with iTUG features was able to predict the CBMS total score with an accuracy of 85.2% (84.9–85.5%), while standard clinical tests predicted 82.5% (82.2–82.8%) of the score. These findings suggest that a fast and easily administered iTUG could be used to predict CBMS score, providing a valuable tool for research and clinical care.

Mobile Computing Technologies for Health and Mobility Assessment: Research Design and Results of the Timed Up and Go Test in Older Adults

Due to the increasing age of the European population, there is a growing interest in performing research that will aid in the timely and unobtrusive detection of emerging diseases. For such tasks, mobile devices have several sensors, facilitating the acquisition of diverse data. This study focuses on the analysis of the data collected from the mobile devices sensors and a pressure sensor connected to a Bitalino device for the measurement of the Timed-Up and Go test. The data acquisition was performed within different environments from multiple individuals with distinct types of diseases. Then this data was analyzed to estimate the various parameters of the Timed-Up and Go test. Firstly, the pressure sensor is used to extract the reaction and total test time. Secondly, the magnetometer sensors are used to identify the total test time and different parameters related to turning around. Finally, the accelerometer sensor is used to extract the reaction time, total test time, duration of turning around, going time, return time, and many other derived metrics. Our experiments showed that these parameters could be automatically and reliably detected with a mobile device. Moreover, we identified that the time to perform the Timed-Up and Go test increases with age and the presence of diseases related to locomotion.

Is The Timed-Up and Go Test Feasible in Mobile Devices? A Systematic Review

The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject’s performance during the test execution.

A Step in the Right Direction: Body Location Determines Activity Tracking Device Accuracy in Total Knee and Hip Arthroplasty Patients

INTRODUCTION

Step counts measured by activity monitoring devices (AMDs) and smartphones (SPs) can objectively measure a patient's activity levels after total hip and knee arthroplasty (total joint arthroplasty [TJA]). This study investigated the use and optimal body location of AMDs and SPs to measure step counts in the postoperative period.

METHODS

This was a two-armed, prospective, observational study of TJA inpatients (n = 24) and 2-week status after TJA (n = 25) completing a 100-ft walk. Observer-counted steps were compared with those measured by AMDs (wrist and ankle) and SPs (hip and neck). Acceptable error was defined as <30%. Error rates were treated as both dichotomous and continuous variables.

RESULTS

AMD and SP step counts had overall unacceptable error in TJA inpatients. AMDs on the contralateral ankle and SPs on the contralateral hip had error rates less than 30% at 2 weeks postoperatively. Two-week postoperative patients required lower levels of assist (11/25 walker; 4/25 no assist), and significant improvements in stride length (total hip arthroplasty 1.27 versus 1.83 ft/step; total knee arthroplasty 1.42 versus 1.83 ft/step) and cadence (total hip arthroplasty 74.6 versus 166.0 steps/min; total knee arthroplasty 73.5 versus 144.4 steps/min) were seen between inpatient and postoperative patients. Regression analysis found that increases in postoperative day and cadence led to a decrease in device error.

CONCLUSION

In inpatients with TJA, AMDs and SPs have unacceptable variability and limited utility for step counting when using a walker. As gait normalizes and the level of ambulatory assist decreases, AMDs on the contralateral ankle and SPs on the contralateral hip demonstrated low error rates. These devices offer a novel method for measurement of objective outcomes and potential for remote monitoring of patient progress after TJA.

LEVEL OF EVIDENCE

Level II, prospective, three-armed study, prognostic study.

Evaluation of Home-Based Rehabilitation Sensing Systems with Respect to Standardised Clinical Tests

With increased demand for tele-rehabilitation, many autonomous home-based rehabilitation systems have appeared recently. Many of these systems, however, suffer from lack of patient acceptance and engagement or fail to provide satisfactory accuracy; both are needed for appropriate diagnostics. This paper first provides a detailed discussion of current sensor-based home-based rehabilitation systems with respect to four recently established criteria for wide acceptance and long engagement. A methodological procedure is then proposed for the evaluation of accuracy of portable sensing home-based rehabilitation systems, in line with medically-approved tests and recommendations. For experiments, we deploy an in-house low-cost sensing system meeting the four criteria of acceptance to demonstrate the effectiveness of the proposed evaluation methodology. We observe that the deployed sensor system has limitations in sensing fast movement. Indicators of enhanced motivation and engagement are recorded through the questionnaire responses with more than 83% of the respondents supporting the system’s motivation and engagement enhancement. The evaluation results demonstrate that the deployed system is fit for purpose with statistically significant (ϱc>0.99, R2>0.94, ICC>0.96) and unbiased correlation to the golden standard.

Reference value for the TUGT in healthy older people: A systematic review and meta-analysis

The timed up and go test (TUGT) was recently proposed as a strong predictor of adverse outcomes. Few reviews have been conducted to identify a standard for the TUGT in healthy older people, and the aims of this study were to explore the source of heterogeneity and evaluate the range of reference values for the TUGT in healthy people over 60 years old stratified by age and sex. The VIP, EMBASE, Web of Science and PubMed databases were searched from January 1, 2000, to December 31, 2018. A subgroup analysis and meta-regression were used to assess heterogeneity. Thirty-four eligible studies were included. The mean TUGT results for the total population, males and females in the sample were 9.21 s [95% CI (9.11, 9.31)], 9.33 s [95% CI (7.82, 11.08)] and 8.87 s [95% CI (8.40, 9.38)], respectively. The mean TUGT results for older people in their 60 s, 70 s, and 80 s were 7.91 s [95% CI (6.62, 9.20)], 8.67 s [95% CI (7.23, 10.12)] and 11.68 s [95% CI (8.11, 15.26)], respectively. The meta-regression analysis results showed that the heterogeneity was related to age (P < 0.01). Age affects the results of the TUGT, and it is necessary to take age into consideration when conducting stratified physical evaluations for the evaluation of older people individuals' physical fitness.

Pilot Study of the EncephaLog Smartphone Application for Gait Analysis

Gait disorders and falls are common in elders and in many clinical conditions, yet they are typically infrequently and subjectively evaluated, limiting prevention and intervention. Completion-time of the Timed-Up-and-Go (TUG) test is a well-accepted clinical biomarker for rating mobility and prediction of falls risk. Using smartphones’ integral accelerometers and gyroscopes, we already demonstrated that TUG completion-time can be accurately measured via a smartphone app. Here we present an extended app, EncephaLog^TM, which provides gait analysis in much more detail, offering 9 additional gait biomarkers on top of the TUG completion-time. In this pilot, four healthy adults participated in a total of 32 TUG tests; simultaneously recorded by EncephaLog and motion sensor devices used in movement labs: motion capture cameras (MCC), pressure mat; and/or wearable sensors. Results show high agreement between EncephaLog biomarkers and those measured by the other devices. These preliminary results suggest that EncephaLog can provide an accurate, yet simpler, instrumented TUG (iTUG) platform than existing alternatives, offering a solution for clinics that cannot afford the cost or space required for a dedicated motion lab and for monitoring patients at their homes. Further research on a larger study population with pathologies is required to assess full validity.

Machine Learning Groups Patients by Early Functional Improvement Likelihood Based on Wearable Sensor Instrumented Preoperative Timed-Up-and-Go Tests

BACKGROUND

Wearable sensors permit efficient data collection and unobtrusive systems can be used for instrumenting knee patients for objective assessment. Machine learning can be leveraged to parse the abundant information these systems provide and segment patients into relevant groups without specifying group membership criteria. The objective of this study is to examine functional parameters influencing favorable recovery outcomes by separating patients into functional groups and tracking them through clinical follow-ups.

METHODS

Patients undergoing primary unilateral total knee arthroplasty (n = 68) completed instrumented timed-up-and-go tests preoperatively and at their 2-, 6-, and 12-week follow-up appointments. A custom wearable system extracted 55 metrics for analysis and a K-means algorithm separated patients into functionally distinguished groups based on the derived features. These groups were analyzed to determine which metrics differentiated most and how each cluster improved during early recovery.

RESULTS

Patients separated into 2 clusters (n = 46 and n = 22) with significantly different test completion times (12.6 s vs 21.6 s, P < .001). Tracking the recovery of both groups to their 12-week follow-ups revealed 64% of one group improved their function while 63% of the other maintained preoperative function. The higher improvement group shortened their test times by 4.94 s, (P = .005) showing faster recovery while the other group did not improve above a minimally important clinical difference (0.87 s, P = .07). Features with the largest effect size between groups were distinguished as important functional parameters.

CONCLUSION

This work supports using wearable sensors to instrument functional tests during clinical visits and using machine learning to parse complex patterns to reveal clinically relevant parameters.

Movement smoothness during a functional mobility task in subjects with Parkinson's disease and freezing of gait - an analysis using inertial measurement units

Background

Impairments of functional mobility may affect locomotion and quality of life in subjects with Parkinson’s disease (PD). Movement smoothness measurements, such as the spectral arc length (SPARC), are novel approaches to quantify movement quality. Previous studies analyzed SPARC in simple walking conditions. However, SPARC outcomes during functional mobility tasks in subjects with PD and freezing of gait (FOG) were never investigated. This study aimed to analyze SPARC during the Timed Up and Go (TUG) test in individuals with PD and FOG.

Methods

Thirty-one participants with PD and FOG and six healthy controls were included. SPARC during TUG test was calculated for linear and angular accelerations using an inertial measurement unit system. SPARC data were correlated with clinical parameters: motor section of the Unified Parkinson’s Disease Rating Scale, Hoehn & Yahr scale, Freezing of Gait Questionnaire, and TUG test.

Results

We reported lower SPARC values (reduced smoothness) during the entire TUG test, turn and stand to sit in subjects with PD and FOG, compared to healthy controls. Unlike healthy controls, individuals with PD and FOG displayed a broad spectral range that encompassed several dominant frequencies. SPARC metrics also correlated with all the above-mentioned clinical parameters.

Conclusion

SPARC values provide valid and relevant clinical data about movement quality (e.g., smoothness) of subjects with PD and FOG during a functional mobility test.

Electronic supplementary material

The online version of this article (10.1186/s12984-019-0579-8) contains supplementary material, which is available to authorized users.

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.

Instrumented Crutch Tip for Monitoring Force and Crutch Pitch Angle

In rehabilitation procedures related to the lower limbs, gait monitoring is an important source of information for the therapist. However, many of the approaches proposed in the literature require the use of uncomfortable and invasive devices. In this work, an instrumented tip is developed and detailed, which can be connected to any crutch. The instrumented tip provides objective data of the crutch motion, which, combined with patient movement data, might be used to monitor the daily activities or assess the recovery status of the patient. For that purpose, the tip integrates a two-axis inclinometer, a tri-axial gyroscope, and a force sensor to measure the force exerted on the crutch. In addition, a novel algorithm to estimate the pitch angle of the crutch is developed. The proposed approach is tested experimentally, obtaining acceptable accuracies and demonstrating the validity of the proposed lightweight, portable solution for gait monitoring.

Extended Timed Up & Go test: Is walking forward and returning back to the chair equivalent gait?

The Timed Up & Go test (TUG) is functional test and is a part of routine clinical examinations. The instrumented Timed Up & Go test enables its segmentation to sub-tasks: sit-to-stand, walking forward, turning, walking back, stand-to-sit, and consequently the computation of task-specific parameters and sub-tasks separately. However, there are no data on whether walking forward parameters differ from the walking back parameters. This study tested the differences between walking forward and walking back in the TUG extended to 10 m for 17 spatio-temporal gait parameters. All parameters were obtained from a GAITRite® pressure sensitive walkway (CIR Systems, Inc.). The differences were assessed for healthy controls and Parkinson's disease (PD) patients. None of investigated parameters exhibited a difference between both gait subtasks for healthy subjects group. Five parameters of interest, namely velocity, step length, stride length, stride velocity, and the proportion of the double support phase with respect to gait cycle duration, showed a statistically significant difference between gait for walking forward and walking back in PD patients. Therefore, we recommend a separate assessment for walking forward and walking back rather than averaging both gaits together.

Remote timed up and go evaluation from activities of daily living reveals changing mobility after surgery

BACKGROUND

Mobility impairment is common in older adults and negatively influences the quality of life. Mobility level may change rapidly following surgery or hospitalization in the elderly. The timed up and go (TUG) is a simple, frequently used clinical test for functional mobility; however, TUG requires supervision from a trained clinician, resulting in infrequent assessments. Additionally, assessment by TUG in clinic settings may not be completely representative of the individual's mobility in their home environment.

OBJECTIVE

In this paper, we introduce a method to estimate TUG from activities detected in free-living, enabling continuous remote mobility monitoring without expert supervision. The method is used to monitor changes in mobility following total hip arthroplasty (THA).

METHODS

Community-living elderly (n  =  239, 65-91 years) performed a standardized TUG in a laboratory and wore a wearable pendant device that recorded accelerometer and barometric sensor data for at least three days. Activities of daily living (ADLs), including walks and sit-to-stand transitions, and their related mobility features were extracted and used to develop a regularized linear model for remote TUG test estimation. Changes in the remote TUG were evaluated in orthopaedic patients (n  =  15, 55-75 years), during 12-weeks period following THA.

MAIN RESULTS

In leave-one-out-cross-validation (LOOCV), a strong correlation (ρ  =  0.70) was observed between the new remote TUG and standardized TUG times. Test-retest reliability of 3-days estimates was high (ICC  =  0.94). Compared to week 2 post-THA, remote TUG was significantly improved at week 6 (11.7  ±  3.9 s versus 8.0  ±  1.8 s, p   <  0.001), with no further change at 12-weeks (8.1  ±  3.9 s, p   =  0.37).

SIGNIFICANCE

Remote TUG can be estimated in older adults using 3-days of ADLs data recorded using a wearable pendant. Remote TUG has discriminatory potential for identifying frail elderly and may provide a convenient way to monitor changes in mobility in unsupervised settings.

Assessment of frailty: a survey of quantitative and clinical methods

BACKGROUND

Frailty assessment is a critical approach in assessing the health status of older people. The clinical tools deployed by geriatricians to assess frailty can be grouped into two categories; using a questionnaire-based method or analyzing the physical performance of the subject. In performance analysis, the time taken by a subject to complete a physical task such as walking over a specific distance, typically three meters, is measured. The questionnaire-based method is subjective, and the time-based performance analysis does not necessarily identify the kinematic characteristics of motion and their root causes. However, kinematic characteristics are crucial in measuring the degree of frailty.

RESULTS

The studies reviewed in this paper indicate that the quantitative analysis of activity of daily living, balance and gait are significant methods for assessing frailty in older people. Kinematic parameters (such as gait speed) and sensor-derived parameters are also strong markers of frailty. Seventeen gait parameters are found to be sensitive for discriminating various frailty levels. Gait velocity is the most significant parameter. Short term monitoring of daily activities is a more significant method for frailty assessment than is long term monitoring and can be implemented easily using clinical tests such as sit to stand or stand to sit. The risk of fall can be considered an outcome of frailty.

CONCLUSION

Frailty is a multi-dimensional phenomenon that is defined by various domains; physical, social, psychological and environmental. The physical domain has proven to be essential in the objective determination of the degree of frailty in older people. The deployment of inertial sensor in clinical tests is an effective method for the objective assessment of frailty.

Do wearable sensors add meaningful information to the Timed Up and Go test? A study on obese women

The purpose of this study was to validate Time Up and Go test (TUG) as measured by a single Inertial Measurement Unit (IMU) placed on the lower back to that measured by a stopwatch in obese and normal weight women; in addition, the comparison of the performance of TUG test between obese and healthy women using the instrumented TUG (iTUG). Forty-four severely obese women and 14 age-matched healthy women were assessed simultaneously by IMU and stopwatch. The comparison between manual and instrumented assessment of total time duration showed no significant differences both in the healthy (8.32 ± 0.96 s vs. 8.52 ± 0.97 s, p > 0.05) and in the obese group (9.99 ± 2.28 s vs. 9.81 ± 2.52 s; p > 0.05). The comparison between obese and healthy group exhibited significant differences in terms of total time duration both during manual and iTUG, which is longer in obese women than normal weight women. The duration of the sub-phases in obese group is longer with the exception of sit-to-stand and stand-to-sit phase, with lower turning velocity both in mid- and final turning sub-phase. The results suggest that the iTUG is an objective and fast mobility test and it could add useful information to the manual TUG for clinical practice.

Towards an Automated Unsupervised Mobility Assessment for Older People Based on Inertial TUG Measurements

One of the most common assessments for the mobility of older people is the Timed Up and Go test (TUG). Due to its sensitivity regarding the indication of Parkinson’s disease (PD) or increased fall risk in elderly people, this assessment test becomes increasingly relevant, should be automated and should become applicable for unsupervised self-assessments to enable regular examinations of the functional status. With Inertial Measurement Units (IMU) being well suited for automated analyses, we evaluate an IMU-based analysis-system, which automatically detects the TUG execution via machine learning and calculates the test duration. as well as the duration of its single components. The complete TUG was classified with an accuracy of 96% via a rule-based model in a study with 157 participants aged over 70 years. A comparison between the TUG durations determined by IMU and criterion standard measurements (stopwatch and automated/ambient TUG (aTUG) system) showed significant correlations of 0.97 and 0.99, respectively. The classification of the instrumented TUG (iTUG)-components achieved accuracies over 96%, as well. Additionally, the system’s suitability for self-assessments was investigated within a semi-unsupervised situation where a similar movement sequence to the TUG was executed. This preliminary analysis confirmed that the self-selected speed correlates moderately with the speed in the test situation, but differed significantly from each other.

Thigh-Derived Inertial Sensor Metrics to Assess the Sit-to-Stand and Stand-to-Sit Transitions in the Timed Up and Go (TUG) Task for Quantifying Mobility Impairment in Multiple Sclerosis

Introduction: Inertial sensors generate objective and sensitive metrics of movement disability that may indicate fall risk in many clinical conditions including multiple sclerosis (MS). The Timed-Up-And-Go (TUG) task is used to assess patient mobility because it incorporates clinically-relevant submovements during standing. Most sensor-based TUG research has focused on the placement of sensors at the spine, hip or ankles; an examination of thigh activity in TUG in multiple sclerosis is wanting. Methods: We used validated sensors (x-IMU by x-io) to derive transparent metrics for the sit-to-stand (SI-ST) transition and the stand-to-sit (ST-SI) transition of TUG, and compared effect sizes for metrics from inertial sensors on the thighs to effect sizes for metrics from a sensor placed at the L3 level of the lumbar spine. Twenty-three healthy volunteers were compared to 17 ambulatory persons with MS (PwMS, HAI ≤ 2). Results: During the SI-ST transition, the metric with the largest effect size comparing healthy volunteers to PwMS was the Area Under the Curve of the thigh angular velocity in the pitch direction-representing both thigh and knee extension; the peak of the spine pitch angular velocity during SI-ST also had a large effect size, as did some temporal measures of duration of SI-ST, although less so. During the ST-SI transition the metric with the largest effect size in PwMS was the peak of the spine angular velocity curve in the roll direction. A regression was performed. Discussion: We propose for PwMS that the diminished peak angular velocity during SI-ST directly represents extensor weakness, while the increased roll during ST-SI represents diminished postural control. Conclusions: During the SI-ST transition of TUG, angular velocities can discriminate between healthy volunteers and ambulatory PwMS better than temporal features. Sensor placement on the thighs provides additional discrimination compared to sensor placement at the lumbar spine.

Turning is an important marker of balance confidence and walking limitation in persons with multiple sclerosis

The standard functional tool for gait assessment in multiple sclerosis (MS) clinical trials has been the 25-Foot Timed Walk Test, a measure of gait speed. Straight-line gait assessment may not reflect adequately upon balance and coordination. Walking tests with turns may add additional information towards understanding gait and balance status, and be more reflective of ambulation in the community. Understanding the impact of turn parameters on patient-reported outcomes of balance and walking would help MS clinicians better formulate treatment plans for persons with gait limitations. In this study, ninety-one persons with MS (Expanded Disability Status Score; EDSS, range: 0–6.5) were enrolled in an initial cross-sectional study. Twenty-four subjects (EDSS, range:1.0–6.0) completed a follow-up visit an average of 12 months later. Spatiotemporal gait analysis was collected at both visits using APDM Opal wireless body-worn sensors while performing the Timed-Up-and-Go (TUG) and 6-Minute Walk Test (6MWT). For both cross-sectional and longitudinal data, regression analyses determined the impact on the addition of turning parameters to stride velocity (SV), in the prediction of self-reported balance confidence (Activities-Specific Balance Confidence Scale (ABC)) and walking limitation (12-item Multiple Sclerosis Walking Scale (MSWS-12)). The addition of 6MWT peak turn velocity (PTV) to 6MWT SV increased the predictive power of the 6MWT for the ABC from 20% to 33%, and increased the predictive power from 28% to 41% for the MSWS-12. TUG PTV added to TUG SV also strengthened the relationship of the TUG for the ABC from 19% to 28%, and 27% to 36% for the MSWS-12. For those with 1 year follow-up, percent change in turn number of steps (TNS%Δ) during the 6MWT added to 6MWT SV%Δ improved the modeling of ABC%Δ from 24% to 33%. 6MWT PTV%Δ added to 6MWT SV%Δ increased the predictive power of MSWS-12%Δ from 8% to 27%. Conclusively, turn parameters improved modeling of self-perceived balance confidence and walking limitations when added to the commonly utilized measure of gait speed. Tests of longer durations with multiple turns, as opposed to shorter durations with a single turn, modeled longitudinal change more accurately. Turn speed and stability should be qualitatively assessed during the clinic visit, and use of multi-faceted tests such as the TUG or 6MWT may be required to fully understand gait deterioration in persons with MS.