Open Access

ARTICLE

Signal Processing and AI-based Assessment of Rehabilitation Exercises for Diastasis Recti Abdominis

by R. Karthik¹, R. Menaka^1,*, P. Ponmathi², Daehan Won³, P. Vinitha Joshy¹, J. G. Aravindan⁴, S. Harshavardhan⁴, K. V. S. D. Aashish kumar⁴, R. Akileshkumar⁴

1 Centre for Cyber Physical Systems (CCPS), School of Electronics Engineering, Vellore Institute of Technology, Chennai, 600127, India
2 Faculty of Physiotherapy, Sri Ramachandra Institute of Higher Education and Research, Chennai, 600116, India
3 System Sciences and Industrial Engineering, Binghamton University, Binghamton, 13902-6000, USA
4 School of Electronics Engineering, Vellore Institute of Technology, Chennai, 600127, India

* Corresponding Author: R. Menaka. Email:

Computer Systems Science and Engineering 2023, 47(1), 333-348. https://doi.org/10.32604/csse.2023.037661

Received 12 November 2022; Accepted 09 February 2023; Issue published 26 May 2023

Abstract

Diastasis Recti Abdominis (DRA) is the separation of abdominal recti muscles which occurs in women during their pregnancy and postpartum time. This is because of the stretching of the linea alba, a fibrous connective tissue on the abdominal wall. The Linea Alba is elastic and retracts back after the delivery of the baby. When this tissue gets overstretched, it loses its elasticity and the gap in the abdominals may not be closed leading to DRA. The motive of this research is to analyze the postpartum rehabilitation for signals from Inertial Measurement Unit (IMU) sensors. The conservative treatment for women who are experiencing DRA is given in the form of physiotherapy. These physiotherapy exercises focus on the recti abdominis muscle to bring back the Linea alba together. It will be a difficult process for the physiotherapist to monitor, whether patients did the exercises correctly or not. If the exercises are not correct, they will not be effective in the reduction of inter-recti distance. This research aims to analyze the effectiveness of IMU signals in classifying the correct and incorrect exercises. It was inferred that the IMU signals are effective in classifying the correct and incorrect exercises with an accuracy of 92%.

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Keywords

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