Design and Evaluation of an IMU Sensor-based System for the Rehabilitation of Upper Limb Motor Dysfunction

Tran, Bao; Zhang, Xiaorong; Modan, Amir; Hughes, Charmayne

doi:10.1109/biorob52689.2022.9925549

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…A deep learning model with long short-term memory networks was employed in the investigation, which yielded a recognition accuracy of 97.2% [15]. Additionally, investigated the use of IMUs to identify upper limb ADLs and evaluated the performance of five machine learning techniques, including logistic regression, support vector machines, decision trees, random forests, and random forests [16]. According to the study, support vector machines had the highest recognition accuracy for ADLs, at 91.25% [17].…”

Section: Figure 1 Glove System With Multiple Sensors [8]mentioning

confidence: 99%

Comparative Performance Analysis of Machine Learning Algorithms for Arm and Shoulder Exercises Using Wrist-Worn Band

Asghar,

Majeed,

Taseer

et al. 2023

IEEE Access

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Section: Figure 1 Glove System With Multiple Sensors [8]mentioning

confidence: 99%

Comparative Performance Analysis of Machine Learning Algorithms for Arm and Shoulder Exercises Using Wrist-Worn Band

Asghar,

Majeed,

Taseer

et al. 2023

IEEE Access

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Upper Limb Diagnostics and Rehabilitation Devices - Brief Review

Husar

Bundzel

Hliboký

2023

2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI)

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Accuracy and Validity of a Single Inertial Measurement Unit-Based System to Determine Upper Limb Kinematics for Medically Underserved Populations

Hughes¹,

Tran²,

Modan³

et al. 2022

Front. Bioeng. Biotechnol.

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Stroke is one of the leading causes of death and disability worldwide, with a disproportionate burden represented by low- and middle-income countries (LMICs). To improve post-stroke outcomes in LMICs, researchers have sought to leverage emerging technologies that overcome traditional barriers associated with stroke management. One such technology, inertial measurement units (IMUs), exhibit great potential as a low-cost, portable means to evaluate and monitor patient progress during decentralized rehabilitation protocols. As such, the aim of the present study was to determine the ability of a low-cost single IMU sensor-based wearable system (named the T’ena sensor) to reliably and accurately assess movement quality and efficiency in physically and neurologically healthy adults. Upper limb movement kinematics measured by the T’ena sensor were compared to the gold standard reference system during three functional tasks, and root mean square errors, Pearson’s correlation coefficients, intraclass correlation coefficients, and the Bland Altman method were used to compare kinematic variables of interest between the two systems for absolute accuracy and equivalency. The T’ena sensor and the gold standard reference system were significantly correlated for all tasks and measures (r range = 0.648—0.947), although less so for the Finger to Nose task (r range = 0.648—0.894). Results demonstrate that single IMU systems are a valid, reliable, and objective method by which to measure movement kinematics during functional tasks. Context-appropriate enabling technologies specifically designed to address barriers to quality health services in LMICs can accelerate progress towards the United Nations Sustainable Development Goal 3.

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Design and Evaluation of an IMU Sensor-based System for the Rehabilitation of Upper Limb Motor Dysfunction

Cited by 3 publications

References 15 publications

Comparative Performance Analysis of Machine Learning Algorithms for Arm and Shoulder Exercises Using Wrist-Worn Band

Comparative Performance Analysis of Machine Learning Algorithms for Arm and Shoulder Exercises Using Wrist-Worn Band

Upper Limb Diagnostics and Rehabilitation Devices - Brief Review

Accuracy and Validity of a Single Inertial Measurement Unit-Based System to Determine Upper Limb Kinematics for Medically Underserved Populations

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