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

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

Statement of the Use of Generative AI and AI-Assisted Technologies in the Writing Process

During the preparation of this manuscript, the authors used ChatGPT for refining the text and English translation. After using this service, the authors reviewed and edited the content as necessary and take full responsibility for the content of the published article.

Acknowledgments

We would like to thank all the participants for their valuable contributions to this study and Editage for English language editing.

Author Contributions

Conceptualization, Y.M., H.T., I.Z., G.G., K.M. and E.M.; Methodology, R.S., Y.M., I.Z. and G.G.; Software, R.S. and I.Z.; Validation, Y.M. and H.T.; Formal Analysis, R.S.; Investigation, Y.M., H.T., I.Z., G.G., K.M. and E.M.; Resources, Y.M.; Data Curation, R.S.; Writing—Original Draft Preparation, Y.M. and R.S.; Writing—Review & Editing, Y.M., H.T., I.Z., G.G., K.M. and E.M.; Visualization, R.S.; Supervision, Y.M.; Project Administration, Y.M.; Funding Acquisition, Y.M.

Ethics Statement

This study was approved by the Ethics Committee of Nagoya Institute of Technology (Approval No. 2020-001, approved on 21 July 2020) and the Ethics Committee of Shonan University of Medical Sciences (Approval No. 24-045, approved on 5 November 2024).

Informed Consent Statement

All participants provided written informed consent before the measurements.

Data Availability Statement

The datasets used and/or analyzed in the current study are available from the corresponding author upon reasonable request.

Funding

This study was funded by the JSPS Grant-in-Aid for Scientific Research (19K12878) and by the 2024 Nagoya Institute of Technology Internationalization Promotion Program.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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