Identification and quantification of muscular cocontraction for ankle rehabilitation through variational mode decomposition in surface electromyography

Abstract

The ankle joint plays important role in performing fundamental activities such as walking and other essential daily tasks. Ankle stabilization and muscle co-contraction are crucial for rehabilitating gait abnormalities, as impaired ankle function disrupts gait, causes pain and inflammation, and hampers recovery. Accurate assessment of muscle co-contraction is crucial for developing effective non-pharmacological interventions. This paper introduces a novel approach using Variational Mode Decomposition (VMD) combined with scalogram visualization technique to analyze surface electromyographic (sEMG) signals from antagonist muscles of the lower limb and assesses muscular co-contraction using the coscalogram function. The present study compares VMD with the Continuous Wavelet Transform (CWT) approach and shows that VMD outperforms CWT in terms of both SNR and RMSE. On average, the increase in SNR in case of VMD (-17.65 ± 8.1dB to 2.98 ± 2.2dB) was greater than that of CWT (-17.65 ± 3.7dB to 1.34 ± 1.5dB). Similarly, the reduction in RMSE with VMD (0.023 ± 0.0029 to 0.017 ± 0.0015) surpassed that achieved with CWT (0.023 ± 0.0027 to 0.020 ± 0.0025). This enhanced accuracy in identifying co-contraction events has the potential to significantly improve clinical assessment and rehabilitation strategies for patients with ankle joint pathologies. To further validate VMD's effectiveness, we quantitatively assessed co-contraction events by comparing mean peak amplitudes identified using VMD and CWT. Our analysis, which revealed that VMD consistently captured stronger co-contraction events (higher mean peak amplitudes), supports VMD's superiority in accurately identifying and quantifying ankle muscle co-contraction. These results have significant implications for clinical practice, offering the potential for more precise assessments of ankle joint function and the development of more targeted and effective rehabilitation interventions.

Keywords: Ankle rehabilitation; Co-contraction; Continuous wavelet transform; Scalogram; Variational mode decomposition; sEMG.

Fig. 1

Experimental Setup (a) Data protocol starts, as Delsys successfully initiated (b) Subject providing data (c) Electrode placement on Tibialis Anterior (TA) and Gastrocnemius Lateralis (GL) muscles of lower limb (d) sEMG acquisition system (e) Protocol ends after data collection.

Fig. 2

Decomposition of sEMG signal using VMD. The original sEMG signal is decomposed into multiple Intrinsic Mode Functions (IMFs), each representing a specific frequency band.

Fig. 3

Frequency spectrum of IMFs obtained by VMD. The first IMF contains the highest frequency range and thus the most amount of noise, while subsequent IMFs represent progressively lower frequency components with reduced noise. This spectral distribution highlights the effectiveness of VMD in decomposing the sEMG signal into distinct frequency bands for analysis.

Fig. 4

Methodology (A) Acquire the raw data through Delsys trigno wireless EMG sensors. (B) Apply a filtration process to remove unwanted noise or interference from the acquired data. (C) Utilize Variational Mode Decomposition (VMD) to decompose the signal into its constituent components. (D) Evaluate the Signal-to-Noise Ratio (SNR) to quantify the quality of the decomposed components. (E) Calculate the Root Mean Square Error (RMSE) to assess the accuracy of the decomposition process. (F) Conduct a comparative analysis between Variational Mode Decomposition (VMD) and Continuous Wavelet Transform (CWT) for signal decomposition. (G) Generate a scalogram to visualize the time-frequency representation of the signal. (H) Create a coscalogram to display the co-contraction of TA-GL muscles.

Fig. 5

Steps of quantitative assessment of co-contraction.

Fig. 6

Comparison between the original signal and the signal after denoising. The original sEMG signal contains high-frequency noise, which is effectively reduced through the applied denoising technique. The figure illustrates the effectiveness of the VMD in preserving the essential signal components while eliminating unwanted noise, ensuring a cleaner and more interpretable signal for further analysis.

Fig. 7

SNR of TA and GL muscles before and after application of VMD.

Fig. 8

RMSE of TA and GL muscles before and after application of VMD.

Fig. 9

Comparison of VMD and CWT in terms of SNR. A higher SNR indicates better noise reduction and signal preservation. The comparison highlights the effectiveness of VMD in enhancing signal quality compared to CWT.

Fig. 10

Comparison of VMD and CWT in terms of RMSE. A lower RMSE indicates better signal reconstruction with minimal error. The comparison demonstrates the effectiveness of VMD in reducing reconstruction error compared to CWT.

Fig. 11

Comparison of CWT and VMD using both online and offline datasets in terms of SNR.

Fig. 12

Comparison of CWT and VMD using both online and offline datasets in terms of RMSE.

Fig. 13

Panel (A): TA sEMG Scalogram, Panel (B): GL sEMG Scalogram, Panel (C): TA-GL Coscalogram obtained by offline data.

Fig. 14

Panel (A): TA sEMG Scalogram, Panel (B): GL sEMG Scalogram, Panel (C): TA-GL Coscalogram obtained by online data.