title: Mechanomyographic parameter extraction methods: an appraisal for clinical applications.
abstract: The research conducted in the last three decades has collectively demonstrated that the skeletal muscle performance can be alternatively assessed by mechanomyographic signal (MMG) parameters. Indices of muscle performance, not limited to force, power, work, endurance and the related physiological processes underlying muscle activities during contraction have been evaluated in the light of the signal features. As a non-stationary signal that reflects several distinctive patterns of muscle actions, the illustrations obtained from the literature support the reliability of MMG in the analysis of muscles under voluntary and stimulus evoked contractions. An appraisal of the standard practice including the measurement theories of the methods used to extract parameters of the signal is vital to the application of the signal during experimental and clinical practices, especially in areas where electromyograms are contraindicated or have limited application. As we highlight the underpinning technical guidelines and domains where each method is well-suited, the limitations of the methods are also presented to position the state of the art in MMG parameters extraction, thus providing the theoretical framework for improvement on the current practices to widen the opportunity for new insights and discoveries. Since the signal modality has not been widely deployed due partly to the limited information extractable from the signals when compared with other classical techniques used to assess muscle performance, this survey is particularly relevant to the projected future of MMG applications in the realm of musculoskeletal assessments and in the real time detection of muscle activity.
authors.affiliation: Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia. marufibitoye@yahoo.com.
authors.forename: Morufu Olusola
authors.initials: MO
authors.lastname: Ibitoye
authors.affiliation: Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia. azah.hamzaid@um.edu.my.
authors.forename: Nur Azah
authors.initials: NA
authors.lastname: Hamzaid
authors.affiliation: Department of Exercise Science, Creighton University, 2500 California Plaza, Kiewit Fitness Center 228, Omaha, NE 68178, USA. JorgeZunigaUlloa@creighton.edu.
authors.forename: Jorge M
authors.initials: JM
authors.lastname: Zuniga
authors.affiliation: Department of Rehabilitation Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia. nazirah@um.edu.my.
authors.forename: Nazirah
authors.initials: N
authors.lastname: Hasnan
authors.affiliation: Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia. khairi@um.edu.my.
authors.forename: Ahmad Khairi Abdul
authors.initials: AK
authors.lastname: Wahab
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D006801:Humans; D009119:Muscle Contraction; D018482:Muscle, Skeletal / Q000503:physiopathology*; D009135:Muscular Diseases / Q000175:diagnosis / Q000503:physiopathology*; D009213:Myography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity