Spasticity is a typical complication following a stroke, affecting nearly one-third of stroke survivors, primarily characterized by increased muscle tone and abnormal muscle activation patterns. An objective and quantitative assessment of muscle spasticity levels in post-stroke patients is crucial for adjusting rehabilitation plans and preventing complications. However, current methods for detecting and assessing muscle spasticity face challenges such as the subjectivity of manual assessments, the limited clinical applicability of digital devices, and the single-functionality and complex structure of existing unimodal sensors used for monitoring muscle strength. Therefore, there is an urgent need for a simple-structured, comprehensive multimodal sensor combined with an objective quantitative spasticity assessment strategy to achieve an objective evaluation of muscle spasticity levels in patients.
Recently, Professor Wu Hao’s team at Huazhong University of Science and Technology developed a flexible epidermal sensor capable of synchronously collecting bioelectrical and biomechanical signals, suitable for the detection and assessment of muscle spasticity. This multimodal sensor can simultaneously capture the bioelectrical signals (sEMG signal) and biomechanical signals (triboelectric signal), relying on the frequency domain distribution differences of the two signals, wheresEMG signals are concentrated in the high-frequency range, and triboelectric signals are distributed in the low-frequency range, combined with wavelet transform technology to achieve efficient synchronous decoupling of the two signals.
Based on the coupling mechanism between muscle spasticity and biomechanical and bioelectrical signals, a dual-index spasticity assessment system was constructed using the muscle co-activation coefficient (Index 1) and the antagonistic efficacy index (Index 2). The multimodal epidermal sensor combined with this assessment strategy achieved an objective quantitative assessment of spasticity levels in 9 clinical patients, with results highly consistent with clinical evaluations by physicians. The two indices were strongly correlated with the modifiedAshworth scale (MAS scores (Spearman’s r=0.893, 0.850, p<0.01), enabling an objective assessment of the severity of spasticity. Meanwhile, the predictive performance of the dual-index multivariable model significantly outperformed that of the single-index model, highlighting the complementary advantages of multimodal signals. This research provides an objective and quantitative assessment strategy for clinical muscle spasticity through signal mechanism analysis and innovative assessment strategies, addressing the limitations of traditional subjective assessments and aiding in the clinical diagnosis and efficacy monitoring of related diseases.
The related research was published under the title “A Soft On-skin Sensor Simultaneously Capturing Bioelectrical and Biomechanical Signals for Muscle Spasticity Detection and Assessment” in the journal Advanced Functional Materials.
Figure 1: Multimodal epidermal sensor for spasticity detection and assessment
Figure 2: Material and structural design enhancing sensor performance
Figure 3: Muscle spasticity detection and assessment based on dual-index spasticity assessment strategy
Paper link:
https://doi.org/10.1002/adfm.202523580
Source: Polymer Science and Technology
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