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Development of a Hand Spasticity Testing Device for Quantitative Wrist Spasticity Assessment and Automated Evaluation of the Modified Tardieu Scale

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Development of a Hand Spasticity Testing Device for Quantitative Wrist Spasticity Assessment and Automated Evaluation of the Modified Tardieu Scale

Author Information
1
Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Aichi, Japan
2
Department of Rehabilitation, Shonan University of Medical Sciences, Yokohama 244-0806, Kanagawa, Japan
3
Institute of Automatic Control, Lodz University of Technology, Stefanowskiego 18, 90-537 Lodz, Poland
4
Department of Neurological Rehabilitation, Medical University of Lodz, Milionowa 14, 93-113 Lodz, Poland
*
Authors to whom correspondence should be addressed.

Received: 02 December 2025 Revised: 11 May 2026 Accepted: 17 June 2026 Published: 07 July 2026

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© 2026 The authors. This is an open access article under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

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Intell. Rehabil. Eng. 2026, 1(1), 10003; DOI: 10.70322/ire.2026.10003
ABSTRACT: The Modified Tardieu Scale is commonly used to assess spasticity by differentiating between neural and mechanical resistance. However, its manual administration may reduce objectivity and reproducibility. This study aimed to automate the Quality of Muscle Reaction (QMR) assessment in the wrist flexors. To this end, we developed a Hand Spasticity Testing (HaST) device and QMR classification model. The device integrates two inertial measurement units, surface electromyography sensors, and a force sensor to record joint angle, angular velocity, muscle activity, and reaction force during passive wrist extension. A classification model was then constructed using decision trees based on the acquired features, with training and evaluation performed via leave-one-out cross-validation. Using the developed device, 19 participants with upper-limb spasticity were evaluated. Key features, such as the number of local maxima in joint angle, velocity, and reaction force, along with other derived parameters, were extracted and classified to estimate QMR grades (0–2). The proposed method achieved an overall accuracy of 76% and a weighted average F1-score of 0.76. These results demonstrate the feasibility of objective and automated QMR quantification using the HaST device. The proposed system may serve as a preliminary screening and documentation tool to support objective spasticity assessment in clinical settings.
Keywords: Stroke; Spasticity; Hand; Modified Tardieu Scale; Inertial measurement unit; Machine learning; Decision tree

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