The Determination of Musculoskeletal Disorders Based on Automated Processing of Goniometric Signals

• Published in: 2019 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon) pp. 1 - 4
• Main Authors: Dorofeev, N.V., Grecheneva, A.V., Kuzichkin, O.R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2019
• Subjects: automated diagnosis of movement disorders; Density measurement; Exoskeletons; goniometric signals; Industrial engineering; Measurement errors; Measurement uncertainty; motion classification; motion detection; Motion measurement; Task analysis
• DOI: 10.1109/FarEastCon.2019.8934290

The article is devoted to assessing the probability of occurrence of errors of the first and second kind during the automated processing of goniometric signals during the detection and classification of movement disorders. This solution is intended for use in inertial automated systems for diagnosing motion parameters that operate on a quasi-real time scale. It is established that the task of stabilizing the probability of false detection and skipping violations (errors of the first and second kind) is reduced to the problem of reducing the zone of uncertainty of a hypothesis by increasing the accuracy of recording angular (goniometric) parameters. For a probabilistic description of the reliability of assessing the norms of angular parameters in the goniometric control system, a vector of operational characteristics was compiled. As a result of the studies, the probabilities of occurrence of errors of the 1st and 2nd kind during the diagnostics of exoskeleton motion parameters were evaluated: a series of measurements was made and data were processed on samples of sizes N=100, N=200, N =500, N=1000 values.