Rehabilitation Exercise Recognition and Evaluation Based on Smart Sensors With Deep Learning Framework

Abstract

Exercise therapy is seen as one of the major treatments for the rehabilitation for patients, particularly using modern technologies, such as virtual reality or augmented reality. Computer-assisted physical rehabilitation training involves measuring performance by analyzing the movement data collected with a sensory system during prescribed rehabilitation exercises. Human activity recognition is a challenging topic for machine learning in the present area of research. Since the sensor-based activity recognition seeks deep knowledge from various low-level sensor readings concerning human activities. In this paper, the Smart Sensor-based Rehabilitation Exercise Recognition (SSRER) system has been proposed using a deep learning framework. For the recognition of rehabilitation exercise with sensor information, a convolutional neural network (CNN) has been used on dynamic platform(D-CNN) where it has sensory data for physical rehabilitation exercise body movement by Gaussian mixture models (GMM). The input signals and GMMs are in various segments contains shapes for many CNN routes. To retrieve the state transition likelihood of hidden states, the Sensor (S-CNN) utilizes the algorithm of improved lossless information compression as discriminant features of various movements. Therefore, the hybridized CNN of the Sensor (S-CNN) and D-CNN are combined with a deep learning classifier to assess every rehabilitation class exercise at different levels. The categorized deep learning methods show improved performance with best-learned features for any rehabilitation exercise. The difference between the best attribute and the test score analyzed mathematically with our collected data and a variety of activity recognition datasets has been illustrated in this article with test results. INDEX TERMS Deep learning model, convolutional neural network, rehabilitation exercise recognition, sensors. I. BACKGROUND AND INTRODUCTION OF REHABILITATION EXERCISE RECOGNITION

Evaluation Algorithm for the Effectiveness of Stroke Rehabilitation Treatment Using Cross-Modal Deep Learning

“…The evaluation elapsed time of the algorithms of literature [ 5 ], literature [ 6 ], literature [ 7 ], literature [ 8 ], literature [ 9 ], and this paper were compared, and the results are shown in Table 3 .…”

“…Analysis of the data in Table 3 shows that with the increasing number of samples, the evaluation time of different algorithms shows an upward trend. The evaluation time of the algorithm in literature [ 5 ] varies from 1.25 s to 1.78 s. The evaluation time of the algorithm in literature [ 6 ] varies from 1.33 s to 1.96 s. The evaluation time of the algorithm in literature [ 7 ] varies from 1.33 s to 2.13 s. The evaluation time of the algorithm in literature [ 8 ] varies from 1.64 s to 2.55 s. The evaluation time of the algorithm in literature [ 8 ] varies from 1.14 s to 1.36 s. Compared with these algorithms, the evaluation time of the algorithm in this paper varies from 0.56 s to 0.91 s, indicating that the evaluation time of the algorithm in this paper is shorter and more efficient.…”

A Novel Approach for Upper Limb Functionality Assessment Based on Deep Learning and Multimodal Sensing Data

“…Jiang et al [50] proposed the use of deep convolutional neural networks (DCNNs) and wearable sensors for human rehabilitation action recognition, aiming to provide patients with better medical care. Zhang et al [51] proposed a patient rehabilitation training recognition system based on intelligent sensors and deep learning to evaluate the advantages and disadvantages of training movements. Lemoyne et al [52] built a traditional gait analysis system that combines the functions of the system with machine learning to classify different types of gaits; this system can help select the right prosthetic limb for a patient during the recovery process.…”

Recognition and Repetition Counting for Local Muscular Endurance Exercises in Exercise-Based Rehabilitation: A Comparative Study Using Artificial Intelligence Models

“…In recent years, many ML and deep learning-based models have been used along with wearable sensors in the assessment of human movement activities in many domains including: health [ 11 ], recreation activities [ 12 ], musculoskeletal injuries or diseases [ 13 ], day-to-day routine activities (e.g., walking, jogging, running, sitting, drinking, watching TV) [ 11 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ], sporting movements [ 22 ] and exercises [ 23 , 24 , 25 , 26 , 27 ]. The ML models used for exercise recognition have predominantly used multiple wearable sensors [ 28 , 29 , 30 , 31 ], specifically in the areas of free weight exercise monitoring [ 32 ], the performance of lunge evaluation [ 24 ], limb movement rehabilitation [ 33 ], intensity recognition in strength training [ 34 ], exercise feedback [ 24 ], qualitative evaluation of human movements [ 28 ], gym activity monitoring [ 29 ], rehabilitation [ 23 , 25 , 33 , 35 ] and indoor-based exercises for strength training [ 36 ]. However, the use of multiple sensors is far from ideal in practice because of cost, negative aesthetics and reduced user uptake [ 17 ].…”

“…A small number of studies [ 46 , 47 , 49 , 51 ] have shown the significant advantage of using deep learning models in the general area of HAR. However, very few studies [ 23 , 25 , 27 , 30 ] appear to have used deep learning models in exercise recognition and repetition counting, and where employed they use multiple CNN models for the repetition counting task. To the best of our knowledge, there are no works reported using a single deep CNN model for exercise recognition and for repetition counting.…”