Assessment of abdominal rehabilitation for diastasis recti abdominis using ensemble autoencoder

Women experience major bodily changes both during pregnancy and post-pregnancy. Diastasis Recti Abdominis (DRA) is a noticeable issue in the postpartum period among the female population in the world. Though postnatal fitness has gained attention in the recent decade, there is scarce knowledge of the abnormal condition called DRA and its consequences. In the presence of an abnormality, women feel less energetic in their daily activities and may experience fatigue in the abdominal muscles. The physical way of regaining strength in core abdominal muscles includes rehabilitation through exercises prescribed by physiotherapists. The sit-up and curl-up exercises engage the core abdominal muscles and when practiced regularly can bring back the separated recti muscles together in time. In order to bring this practice unsupervised by the physicians and monitor the pace of exercises by the patient individually, wearable Inertial measurement unit (IMU) sensors were employed. The utilization of IMU wearable sensors for DRA has been sparsely explored in literature. In this study, two groups of subjects with DRA perform the rehabilitation exercises and respective inertial measurements were observed. When the situation goes unsupervised, the effective contraction of the abdominal recti muscles and the correctness of exercises were uncertain. It's a well-known fact that deep learning algorithms aid in determining the significant features thereby making the unsupervised classification problem more efficient. Here in this study an ensembled autoencoder neural network is implemented in which the IMU datasets were employed for the classification of correct and incorrect exercises. The latent vector generated in the autoencoder model encapsulates the inherent patterns of the input by undertaking all occurrences into a latent space. Thereby in this work, the reconstruction error generated from the autoencoder network is used to determine the correct and incorrect exercise. The ensemble approach, grouping two classes of autoencoders provides a model with higher predictivity.