Accuracy comparison of machine learning algorithms at various wear-locations for activity identification post stroke: A pilot analysis

Veerubhotla, Akhila; Ehrenberg, Naphtaly; Ibironke, Oluwaseun; Pilkar, Rakesh

doi:10.1109/embc46164.2021.9630745

Cited by 2 publications

(3 citation statements)

References 21 publications

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“…IMU is an integrated accelerometer and gyroscope device that can measure the acceleration and angular velocity of an object in three axes. Due to its small size, ease to wear, and low cost, IMU can assess numerous information in stroke rehabilitation, such as hand function, [ 106 ] coordinated arm movement, [ 120 ] trunk injury, [ 105 ] gait, [ 121 ] fall risk, [ 107 ] posture of sitting or standing [ 122 ] and so on, through its wear on fingers, wrists, arms, chest, waist, hips, legs, and ankles. Detailed explanations are given in the following.…”

Section: Single Mode Sensing Methodsmentioning

confidence: 99%

Section: Single Mode Sensing Methodsmentioning

confidence: 99%

“…[123] Veerubhotla's study compares the accuracy of three machine learning algorithms in identifying and distinguishing sitting, standing, and walking in stroke patients by wearing IMUs on their wrists, waist, and ankles, finding that RUSBoosted trees have the highest accuracy, 99.1%. [122] The vast majority of studies have focused on assessing motor function, especially motor quality, through standardized clinical tests, which may explain the greater prevalence of IMU use to date. [5] According to the law of inertia, the accelerometer and gyroscope can perceive the acceleration and angular velocity of the object, and the speed, position, and direction information of the object can be obtained after the integral calculation of time…”

Section: Imu Sensormentioning

confidence: 99%

See 2 more Smart Citations

Multimodal Sensing in Stroke Motor Rehabilitation

Zhao

Wang

et al. 2023

Advanced Sensor Research

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Applying sensors in biomedical institutions and home‐based stroke rehabilitation is now a global research focus. In this review paper, the relationship between stroke diseases’ physiology mechanism and diverse sensors’ functionalities is detailed explained. The review starts by interpreting how stroke influences motion abilities and then introduces broadly adopted physical training methods. After, the working principles of sensors and their use to objectively provide patients’ body information for stroke rehabilitation status are discussed. The content of the paper aims to not only review the state‐of‐the‐art works of developing sensors for assisting in evaluating stroke patients’ status but also bridging the gap between medical staff and engineers.

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Section: Single Mode Sensing Methodsmentioning

confidence: 99%

Section: Single Mode Sensing Methodsmentioning

confidence: 99%

Section: Imu Sensormentioning

confidence: 99%

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Multimodal Sensing in Stroke Motor Rehabilitation

Zhao

Wang

et al. 2023

Advanced Sensor Research

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The use of accelerometer bracelets to evaluate arm motor function over a stroke rehabilitation period – an explorative observational study

Lyckegård Finn,

Carlsson,

Ericson

et al. 2024

J NeuroEngineering Rehabil

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Background Assessments of arm motor function are usually based on clinical examinations or self-reported rating scales. Wrist-worn accelerometers can be a good complement to measure movement patterns after stroke. Currently there is limited knowledge of how accelerometry correlate to clinically used scales. The purpose of this study was therefore to evaluate the relationship between intermittent measurements of wrist-worn accelerometers and the patient’s progression of arm motor function assessed by routine clinical outcome measures during a rehabilitation period. Methods Patients enrolled in in-hospital rehabilitation following a stroke were invited. Included patients were asked to wear wrist accelerometers for 24 h at the start (T1) and end (T2) of their rehabilitation period. On both occasions arm motor function was assessed by the modified Motor Assessment Scale (M_MAS) and the Motor Activity Log (MAL). The recorded accelerometry was compared to M_MAS and MAL. Results 20 patients were included, of which 18 completed all measurements and were therefore included in the final analysis. The resulting Spearman’s rank correlation coefficient showed a strong positive correlation between measured wrist acceleration in the affected arm and M-MAS and MAL values at T1, 0.94 (p < 0.05) for M_MAS and 0.74 (p < 0.05) for the MAL values, and a slightly weaker positive correlation at T2, 0.57 (p < 0.05) for M_MAS and 0.46 − 0.45 (p = 0.06) for the MAL values. However, no correlation was seen for the difference between the two sessions. Conclusions The results confirm that the wrist acceleration can differentiate between the affected and non-affected arm, and that there is a positive correlation between accelerometry and clinical measures. Many of the patients did not change their M-MAS or MAL scores during the rehabilitation period, which may explain why no correlation was seen for the difference between measurements during the rehabilitation period. Further studies should include continuous accelerometry throughout the rehabilitation period to reduce the impact of day-to-day variability.

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Accuracy comparison of machine learning algorithms at various wear-locations for activity identification post stroke: A pilot analysis

Cited by 2 publications

References 21 publications

Multimodal Sensing in Stroke Motor Rehabilitation

Multimodal Sensing in Stroke Motor Rehabilitation

The use of accelerometer bracelets to evaluate arm motor function over a stroke rehabilitation period – an explorative observational study

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