Use of the sit-to-stand task to evaluate motor function of older adults using telemetry

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 ( c > 0.99, R 2 > 0.94, ICC > 0.96) and unbiased correlation to the golden standard. Author Contributions: Conceptualization, I.V.; methodology, I.V.; software, I.V.; validation, I.V.; formal analysis, I.V.; investigation, I.V.; resources, I.V.; data curation, I.V.; writing-original draft preparation, I.V.; writing-review and editing, L.S., V.S. and A.L.M.; visualization, I.V.; supervision, V.S. and L.S.; project administration, I.V.; funding acquisition, V.S. All authors have read and agreed to the published version of the manuscript.

Section: Camera and Video Systemsmentioning

confidence: 99%

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

Vourganas

Stanković

Michala

2019

“…A growing body of literature has examined the use of inertial measurement units (IMU) for sit-to-stand analyses [8,9,10,11,12]. However, video-based systems [13] or force plates [14,15,16] have also been utilized for analyses of chair rise tests. Additionally, various approaches have been proposed to investigate the sit-to-stand (STS) performance, especially in older people.…”

Section: Introductionmentioning

confidence: 99%

Measurement of the Chair Rise Performance of Older People Based on Force Plates and IMUs

Hellmers

Fudickar

Lau

et al. 2019

An early detection of functional decline with age is important to start interventions at an early state and to prolong the functional fitness. In order to assure such an early detection, functional assessments must be conducted on a frequent and regular basis. Since the five time chair rise test (5CRT) is a well-established test in the geriatric field, this test should be supported by technology. We introduce an approach that automatically detects the execution of the chair rise test via an inertial sensor integrated into a belt. The system’s suitability was evaluated via 20 subjects aged 72–89 years (78.2 ± 4.6 years) and was measured by a stopwatch, the inertial measurement unit (IMU), a Kinect® camera and a force plate. A Multilayer Perceptrons-based classifier detects transitions in the IMU data with an F1-Score of around 94.8%. Valid executions of the 5CRT are detected based on the correct occurrence of sequential movements via a rule-based model. The results of the automatically calculated test durations are in good agreement with the stopwatch measurements (correlation coefficient r = 0.93 (p < 0.001)). The analysis of the duration of single test cycles indicates a beginning fatigue at the end of the test. The comparison of the movement pattern within one person shows similar movement patterns, which differ only slightly in form and duration, whereby different subjects indicate variations regarding their performance strategies.

“…In terms of the sensing technology employed to capture motion data, a variety of reported approaches can be summarized into several categories, including techniques based on computer vision (i.e., cameras), inertial sensors, pressure sensors and electromyography (EMG) sensors, respectively. The computer vision-based techniques interpret motions by the means of acquiring, processing, analyzing, and understanding images of human body movements [3,4,5,6,7]. For example, the Vicon system consisting of multiple infrared high-speed cameras and an associated software can be used to capture the motion data [3], for gait analysis and even the motor function evaluation [4,5].…”

Section: Introductionmentioning

confidence: 99%

Motor Function Evaluation of Hemiplegic Upper-Extremities Using Data Fusion from Wearable Inertial and Surface EMG Sensors

Zhang

Gong

et al. 2017

Quantitative evaluation of motor function is of great demand for monitoring clinical outcome of applied interventions and further guiding the establishment of therapeutic protocol. This study proposes a novel framework for evaluating upper limb motor function based on data fusion from inertial measurement units (IMUs) and surface electromyography (EMG) sensors. With wearable sensors worn on the tested upper limbs, subjects were asked to perform eleven straightforward, specifically designed canonical upper-limb functional tasks. A series of machine learning algorithms were applied to the recorded motion data to produce evaluation indicators, which is able to reflect the level of upper-limb motor function abnormality. Sixteen healthy subjects and eighteen stroke subjects with substantial hemiparesis were recruited in the experiment. The combined IMU and EMG data yielded superior performance over the IMU data alone and the EMG data alone, in terms of decreased normal data variation rate (NDVR) and improved determination coefficient (DC) from a regression analysis between the derived indicator and routine clinical assessment score. Three common unsupervised learning algorithms achieved comparable performance with NDVR around 10% and strong DC around 0.85. By contrast, the use of a supervised algorithm was able to dramatically decrease the NDVR to 6.55%. With the proposed framework, all the produced indicators demonstrated high agreement with the routine clinical assessment scale, indicating their capability of assessing upper-limb motor functions. This study offers a feasible solution to motor function assessment in an objective and quantitative manner, especially suitable for home and community use.