Improved algorithm of muscle fatigue detection using linear regression analysis

• Published in: Electronics letters Vol. 49; no. 2; pp. 89 - 91
• Main Authors: Hu, W.W., Huang, Y.C., Li, C.P.
• Format: Journal Article
• Language: English
• Published: Stevenage The Institution of Engineering and Technology 17.01.2013; Institution of Engineering and Technology
• Subjects: Biological and medical sciences; biomechanics; biomedical measurement; Electrodiagnosis. Electric activity recording; Exact sciences and technology; Investigative techniques, diagnostic techniques (general aspects); Linear inference, regression; linear regression analysis; Mathematics; mean power frequency; Medical sciences; MPF value; muscle; muscle fatigue detection algorithm; muscle fatigue measurement; nonoverlapped scheme; Osteoarticular system. Muscles; overlapped scheme; Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques; Probability and statistics; regression analysis; Sciences and techniques of general use; slope sign changes; Statistics; sustained isometric constant force muscle contraction; window segmenting methods; Isometry; Segmentation; Linear regression; Fatigue; Electromyography; Regression analysis; Windows; Muscle contraction
• ISSN: 1350-911X; 0013-5194; 1350-911X
• DOI: 10.1049/el.2012.2316

Conventional linear regression analysis methods used to measure muscle fatigue based on mean power frequency (MPF) can yield results that are dependent on the epoch of each MPF value. An improved algorithm containing two window segmenting methods, overlapped and non‐overlapped schemes, is provided to investigate the effect of window length on linear regression analysis during sustained isometric constant force muscle contraction. The minimal length of each segment can be determined by making the slope sign changes to be zero. In conclusion, the minimal length is independent of the epoch of each MPF value.