Use of Accelerometry for Long Term Monitoring of Stroke Patients

Abstract

Stroke patients are monitored hourly by physicians and nurses in an attempt to better understand their physical state. To quantify the patients’ level of mobility, hourly movement (i.e. motor) assessment scores are performed, which can be taxing and time-consuming for nurses and physicians. In this paper, we attempt to find a correlation between patient motor scores and continuous accelerometer data recorded in subjects who are unilaterally impaired due to stroke. The accelerometers were placed on both upper and lower extremities of four severely unilaterally impaired patients and their movements were recorded continuously for 7 to 14 days. Features that incorporate movement smoothness, strength, and characteristic movement patterns were extracted from the accelerometers using time-frequency analysis. Support vector classifiers were trained with the extracted features to test the ability of the long term accelerometer recordings in predicting dependent and antigravity sides, and significantly above baseline performance was obtained in most instances ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$P < 0.05$ \end{document} ). Finally, a leave-one-subject-out approach was carried out to assess the generalizability of the proposed methodology, and above baseline performance was obtained in two out of the three tested subjects. The methodology presented in this paper provides a simple, yet effective approach to perform long term motor assessment in neurocritical care patients.

Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review

“…In this category, systems that aim to quantify the level of correctness in executing the prescribed exercises are identified. Researchers achieved this by using popular post-stroke assessment scoring systems [65][66][67][68][69][70][71][72][73][74]:…”

“…Task performance is rated on a 4-point scale, ranging from 0 (no movement) to 3 (movement performed normally). Two of the included works used the ARAT system [66,72] Oxford Grading Motor-Scale (OGM) [81] used in a single study included [70], it evaluates the muscle strength of the rehabilitated patient and can help diagnose problems in which weakness plays a role. It is not proper to stroke rehabilitation and targets both upper and lower extremities.…”

“…Over the past few years, effort has been put into developing unobtrusive, effective and objective motion-modeling systems, taking advantage of the progress made in the sensor technology which became more compact and more power-efficient [83]. All the included works utilised IMUs for the data acquisition [42][43][44][45][46][47][48][49][50][52][53][54][55][56][57][58][59][60][61][65][66][67][68][69][70]63,[71][72][73][74]64]. IMUs are devices that combine linear acceleration from accelerometer and the angular turning rates from gyroscopes [84].…”

“…IMUs were chosen for their portability and for their low-costs, but also because they provide accurate modeling of the participant motion. Some studies used individual accelerometers [43,45,53,54,65,67,68,70,71] or gyroscopes [45] while the rest used their combination to give more detailed information. Moreover, IMUs were coupled with different sensors to acquire more information: a barometric pressure sensor to detect changes in altitude [47,48], insole pressure sensors in [55] to measure the force exercised by the feet while performing the activities, flex sensors to measure the amount of deflection or bending while griping objects [68], liquid level detectors in a cup [42] to measure drinking activity and EMG sensors [61,51] to measure the activity of the muscles that can translate as strength.…”

Motion Assessment for Accelerometric and Heart Rate Cycling Data Analysis

“…There are very wide applications of motion monitoring systems, including gait analysis [21][22][23][24][25][26], motion evaluation [27][28][29][30][31][32], stroke patients monitoring [18,33], recognition of physical activities [34][35][36][37][38], breathing [39], and detection of motion disorders during sleep [40]. The combination of accelerometer sensors at different body locations in possible combination with GPS and geographical information systems is useful in improving movement monitoring of humans [41], assessing road surface roughness [10], and activity recognition [30] as well.…”