Automated Detection of Rehabilitation Exercise by Stroke Patients Using 3-Layer CNN-LSTM Model.

Zia Ur Rahman,Inayat Khan,Abdus Salam,Badam Niazi,Syed Irfan Ullah,Taj Rahman,Liaqat Ali

doi:10.1155/2022/1563707

Abstract

According to statistics, stroke is the second or third leading cause of death and adult disability. Stroke causes losing control of the motor function, paralysis of body parts, and severe back pain for which a physiotherapist employs many therapies to restore the mobility needs of everyday life. This research article presents an automated approach to detect different therapy exercises performed by stroke patients during rehabilitation. The detection of rehabilitation exercise is a complex area of human activity recognition (HAR). Due to numerous achievements and increasing popularity of deep learning (DL) techniques, in this research article a DL model that combines convolutional neural network (CNN) and long short-term memory (LSTM) is proposed and is named as 3-Layer CNN-LSTM model. The dataset is collected through RGB (red, green, and blue) camera under the supervision of a physiotherapist, which is resized in the preprocessing stage. The 3-layer CNN-LSTM model takes preprocessed data at the convolutional layer. The convolutional layer extracts useful features from input data. The extracted features are then processed by adjusting weights through fully connected (FC) layers. The FC layers are followed by the LSTM layer. The LSTM layer further processes this data to learn its spatial and temporal dynamics. For comparison, we trained CNN model over the prescribed dataset and achieved 89.9% accuracy. The conducted experimental examination shows that the 3-Layer CNN-LSTM outperforms CNN and KNN algorithm and achieved 91.3% accuracy.

Highlights

Stroke is a worldwide healthcare problem which causes due to heart failure or malfunctioning of blood vessels
E 3-Layer convolutional neural network (CNN)-long short-term memory (LSTM) model seeks to leverage the power of merging both CNN and LSTM. e main approach is divided into two sections, the detection of physiotherapy exercise and its classification. e first section contains data collection, preprocessing of images, dimensionality reduction, and data augmentation. e second section is using a combination of deep learning (DL) models to evaluate the features and classify the physiotherapy exercises efficiently
Results and Discussion e model is trained in a fully supervised manner, and the gradient is backpropagated from the SoftMax to CNN layer to reduce the loss. rough randomly selected values the bias and weights are initialized at each layer

Summary

Introduction

Stroke is a worldwide healthcare problem which causes due to heart failure or malfunctioning of blood vessels. It is a common, dangerous, and disabling health disease that affects people all around the world. Stroke is the second or third leading cause of death in most regions, as well as one of the leading causes of acquired adult disability [1]. Stroke causes losing control of the motor function, incoordination or paralysis of all body parts, and severe back pain. Patients will have muscle and neurological trauma and disorders such as cerebrum paralysis [2], trauma and paralytic injury [3], posttraumatic stiffness [4], congenital deformity [5], and Guillain–barre syndrome [6]. Injuries to the cervical spinal cord usually result in loosened leg and arms functions where hip flexors and legs are degraded by lumbar and spinal cord injuries. e survivors of a stroke have a similar condition since they must relearn the lost skills when their brain is hit by a stroke

Methods

Results

Conclusion