Human knee abnormality detection from imbalanced sEMG data

• Type: Article in International Scientific Journal
• Date: 2021-04

Authors

Ankit Vijayvargiya (Author) Other; Chandra Prakash (Author) Other; Rajesh Kumar (Author) Other; Sanjeev Bansal (Author) Other; João Manuel R. S. Tavares (Author) FEUP

Journal

Title: Biomedical Signal Processing and Control

Vol. 66 No. 102406

Pages: 1-14

ISSN: 1746-8094

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

ISI Web of Science

Scopus - 1 Citation

Scientific classification

CORDIS: Technological sciences

FOS: Medical and Health sciences

Other information

Authenticus ID: P-00T-A23

DOI: 10.1016/j.bspc.2021.102406

Abstract (EN): The classification of imbalanced datasets, especially in medicine, is a major problem in data mining. Such a problem is evident in analyzing normal and abnormal subjects about knee from data collected during walking. In this work, surface electromyography (sEMG) data were collected during walking from the lower limb of 22 individuals (11 with and 11 without knee abnormality). Subjects with a knee abnormality take longer to complete the walking task than healthy subjects. Therefore, the SEMG signal length of unhealthy subjects is longer than that of healthy subjects, resulting in a problem of imbalance in the collected sEMG signal data. Thus, the development of a classification model for such datasets is challenging due to the bias towards the majority class in the data. The collected sEMG signals are challenging due to the contribution of multiple motor units at a time and their dependency on neuromuscular activity, physiological and anatomical properties of the involved muscles. Hence, automated analysis of such sEMG signals is an arduous task. A multi-step classification scheme is proposed in this research to overcome this limitation. The wavelet denoising (WD) scheme is used to denoise the collected sEMG signals, followed by the extraction of eleven time-domain features. The oversampling techniques are then used to balance the data under analysis by increasing the training minority class. The competency of the proposed scheme was assessed using various computational classifiers with 10 fold cross-validation. It was found that the oversampling techniques improve the performance of all studied classifiers when applied to the studied imbalanced sEMG data.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14