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Design of a Multi-Sensor System for Exploring the Relation between Finger Spasticity and Voluntary Movement in
Bor-Shing Lin1, I-Jung Lee1,2, Pei-Chi Hsiao3
1Department of Computer Science and Information Engineering, National Taipei University, New Taipei City 237303, Taiwan.
Sensors (Basel, Switzerland)
|October 14, 2022
Summary
A new wearable glove system objectively measures finger spasticity in stroke patients. This technology quantifies movement, aiding in the assessment of manual dexterity impairments.
Area of Science:
- Biomedical Engineering
- Rehabilitation Technology
- Neuroscience
Background:
- Stroke survivors often experience finger spasticity, significantly impairing manual dexterity.
- Current assessment relies on subjective neurological tests like the modified Ashworth scale (MAS).
- Objective and quantitative methods are needed to complement existing diagnostic tools.
Purpose of the Study:
- To develop and validate a novel wearable multi-sensor data glove system.
- To explore the relationship between finger spasticity and voluntary movement in stroke patients.
- To provide an objective, quantitative assessment of finger spasticity.
Main Methods:
- Described the hardware and software of a multi-sensor data glove system.
- Recorded biomechanical measurements (speed, acceleration, pressure) during five designated tasks.
- Collected over 1000 features per task from finger joints and upper limbs.
Main Results:
- Conducted a preliminary clinical test with 14 subjects.
- Performed statistical analysis to identify key features discriminating spasticity.
- Identified a subset of features capable of differentiating healthy individuals from stroke patients with spasticity.
Conclusions:
- The developed data glove system offers a feasible approach for objective and quantitative spasticity assessment.
- The system complements traditional neurological examinations for stroke patients.
- This technology has the potential to improve the diagnosis and management of finger spasticity.
