Neuro-Inspired Spiking Classifier for Trajectory-Optimized Diastasis Recti Rehabilitation With Wearable IMU Sensor

Globally there is rising concern among postnatal women about the complications of Diastasis Recti Abdominis (DRA). DRA, the abdominal muscle divarication may susceptible to loss of functions of the abdominal wall. Rehabilitation exercises re-establish the connection between the abdominal and supporting muscles. The physiotherapists are currently unable to objectively detect the effectiveness of exercises in postnatal women to implement corrective measures. DRA subjects may exhibit diverse positions and movements during exercises emphasizing the need to consider movement patterns across various exercises. The muscle activation and movement patterns are assessed by the Electromyography (EMG) and Inertial measurement unit (IMU) Sensors. IMUs are essential sensors for evaluating exercise performance. The use of wearable IMU sensors enhances subject comfort during movements and facilitates the measurement of abdominal dynamics associated with various exercises. The preeminent objective of this work is to delineate the movement patterns of abdominal muscles during seven rehabilitation exercises specified by the physiotherapist for postnatal women. The trajectory of these exercises gives a clear view of the movement patterns. To predict the effective performance of exercises, modified complimentary filter (MCF) -based sensor fusion approach is utilized in finding the position information and trajectory of the movement patterns. The trajectory features provide insights of movement patterns based on the correctness class during each exercise. K-Nearest Neighbour classifier and Extreme Gradient (XG) Boost classifier are tested and yields an accuracy of 60% and 62%. The Binary Spiking classifier model, which leverages neuromorphic principles, is energy-efficient and capable at handling temporal dynamics. This Spiking model is used to evaluate the effectiveness of rehabilitation exercises, achieving an initial accuracy of 65%. To improve the model’s performance, IMU features are incorporated along with trajectory features, resulting in a higher accuracy of 85% compared to KNN and XG Boost classifier models.