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The linear-envelope peak (LEP) of surface EMG signal is widely used in gait analysis to characterize muscular activity, especially in clinics. This study is designed to evaluate LEP accuracy in identifying muscular activation and assessing activation timing during walking. Surface EMG signals from gastrocnemius lateralis (GL) and tibialis anterior (TA) were analyzed in 100 strides per subject (31 healthy subjects) during ground walking. Signals were full-wave rectified and low-pass filtered (cut-off frequency=5 Hz) to extract the linear envelope. LEP accuracy in identifying muscle activations and the associated error in peak detection were assessed by direct comparison with a reference method based on wavelet transform. LEP accuracy in identifying the timing of higher signalenergy levels was also assessed, increasing the reference-algorithm selectivity. The detection error (percentage number of times when LEP falls outside the correspondent reference activation interval) is close to zero. Detection error increases up to 70% for intervals including only signal energy higher than 90% of energy peak. Mean absolute error (MAE, the absolute value of the distance between LEP timing and the correspondent actual timing of the sEMG-signal peak computed by reference algorithm) is 54.1±20.0 ms. Detection error and MAE are significantly higher (p<0.05) in TA data compared to GL signals. Differences among MAE values detected adopting different values for LE cut-off frequency are not statistically significant. LEP was found to be accurate in identifying the number of muscle activations during walking. However, the use of LEP to assess the timing of highest sEMG-signal energy (signal peak) should be considered carefully. Indeed, it could introduce a relevant inaccuracy in muscle-activation identification and peak-timing quantification. The type of muscle to analyze could also influence LEP performances, while the cut-off frequency chosen for envelope extraction appears to have a limited impact. • Linear envelope peak (LEP) is widely used to identify muscle activation during gait. • LEP accuracy is tested in 3100 strides of 31 subjects vs. a wavelet-based algorithm. • LEP provides an accurate identification of the number of activations in a stride. • The use of LEP introduces a relevant error in the quantification of EMG-peak timing. • LEP performances are also influenced by the type of muscle being analyzed. [ABSTRACT FROM AUTHOR]

Although gait analysis has been widely adopted to describe Parkinson's disease (PD) dysfunctions during walking, few efforts have been made to understand muscle activity role. The current study aims to characterize lower-limb-muscle recruitment during walking in time-frequency domain, based on Continuous Wavelet Transform (CWT) analysis of surface-electromyography (sEMG) signal from lower-limb muscles. sEMG signals from Tibialis Anterior (TA), Gastrocnemius Lateralis (GL), Rectus Femoris (RF), and Biceps Femoris (BF) of 20 people with PD and 10 age-matched healthy controls (HC) were acquired during gait. sEMG signals were processed applying a CWT-based approach to assess the occurrence frequency (OF, i.e., the percentage of strides of each muscle activation occurrence) and the frequency content of each muscle activation (in Hz). These parameters are rarely quantified in PD. Compared to HC, people with PD displayed a significant decrease (p<0.05) in median OF on RF, BF, and TA, indicating a tendency to reduce the global involvement of lower-limb muscles. No significant differences (p>0.05) in OF were detected among muscle within the same population. No significant changes (p>0.05) in frequency content were revealed in PD. This analysis suggests that people with PD are characterized by a reduced recruitment of those muscles typically adopted to finely control body-segment motion and a concomitant increased recruitment of those muscles mainly involved in locomotion. No substantial alteration in recruiting muscle fibers is associated with PD. These findings suggest that people with PD are inclined to adopt simpler muscular-recruitment strategies during walking, compared to HC. • Lower-limb-muscle recruitment is assessed during parkinsonian (PD) walking. • Surface-EMG signal is processed in time-frequency domain by Wavelet Transform. • Muscle involvement in walking is reduced in PD patients compared to healthy peers. • No substantial alteration is observed in muscle-fiber recruitment associated to PD. • People affected by PD adopt a simpler muscular-recruitment strategy during walking. [ABSTRACT FROM AUTHOR]