Wavelet analyses of electromyographic signals derived from lower extremity muscles while walking or running: A systematic review

Abstract

Surface electromyography is often used to assess muscle activity and muscle function. A wavelet approach provides information about the intensity of muscle activity and motor unit recruitment strategies at every time point of the gait cycle. The aim was to review papers that employed wavelet analyses to investigate electromyograms of lower extremity muscles during walking and running. Eleven databases were searched up until June 1st 2017. The composition was based on the PICO model and the PRISMA checklist. First author, year, subject characteristics, intervention, outcome measures & variables, results and wavelet specification were extracted. Eighteen studies included the use of wavelets to investigate electromyograms of lower extremity muscles. Three main topics were discussed: 1.) The capability of the method to correctly assign participants to a specific group (recognition rate) varied between 68.4%-100%. 2.) Patients with ankle osteoarthritis or total knee arthroplasty presented a delayed muscle activation in the early stance phase but a prolonged activation in mid stance. 3.) Atrophic muscles did not contain type II muscle fiber components but more energy in their lower frequencies. The simultaneous information of time, frequency and intensity is of high clinical relevance because it offers valuable information about pre-and reflex activation behavior on different walking and running speeds as well as spectral changes towards high or low frequencies at every time point of the gait cycle.

Correlation Between Skin Autofluorescence and Muscle Activities of Lower Limb in Aging Without Disease and Disability

“…The original EMG data were applied using a discrete wavelet transform (DWT). The DWT is becoming a standard tool for analyzing EMG data ( Koenig et al, 2018 ). The method of DWT has been described in our previous paper ( Kaneko et al, 2020 ).…”

Monitoring muscle fatigue following continuous load changes

“…Figure 2. The tiling of the time-frequency plane in the case of STFT (left) and the tiling of the time-scale plane in the case of DWT (right) (Hostens, 2004) Many previous studies proved the validity of CWT to detect the development of muscle fatigue in various muscles and motions such as elbow flexion (Hostens et al, 2004) or running (Koenig et al, 2018). However, the application of the method of detecting recovery of muscle fatigue has rarely been investigated.…”

“…The wavelet transform enables to analyze EMG waveforms in a very short duration so that changes in timing and frequency content can be evaluated simultaneously. The method has been used in determining muscle fatigue during repetitive motions such as walking without interrupting the motion (Koenig et al, 2018).…”

Lower Limb Kinematic Coordination during the Running Motion of Stroke Patient: A Single Case Study

“…Furthermore, it may provide useful information for the prevention of tendon disorders [ 5 ] that may have a negative impact on the performance of athletes and for rehabilitation approaches to muscle injuries [ 6 ]. In the analysis of the running movements of competitive athletes, the collection of joint moments [ 7 , 8 ], joint angles [ 9 ], and ground reaction forces [ 10 ] through motion capture systems as well as the collection of lower limb muscle activity through electromyography [ 11 ] mechanics work [ 12 ] and mechanical work efficiency [ 13 ] are commonly carried out in analyses. In the first place, coordination problems in addition to ROM and muscle weakness have been pointed out as factors contributing to poor performance in stroke patients, but to the best of our knowledge no study has clarified the motor coordination of these lower limb joints during running.…”