A Deep Learning-Based Upper Limb Rehabilitation Exercise Status Identification System

Nair, Binoy B.; Sakthivel, N. R.

doi:10.1007/s13369-022-06702-y

Cited by 8 publications

(10 citation statements)

References 61 publications

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“…Modern approaches, including in-depth learning, reduce the requirement for less detailed knowledge as discovery levels rise. Most existing techniques dealt with sensory-based waveform images [1], Kinect sensor depth images, RGB images for fitness exercises, Body key frame images [8][9][10], and Spatial transform with gradient feature images [11] to classify the exercise pose. In this work, RGB images of various exercises are captured through an intelligent phone mobile camera to analyze the classification of complex recuperation exercise poses.…”

Section: Discussionmentioning

confidence: 99%

“…An accelerometer worn by the person was used to track their mobility and record data about their upper limbs. Twelve complete exercise positions and twelve incomplete ones were employed to evaluate 24 models [1]. Kinect software development kit for 3D automated joint evaluation.…”

Section: Related Workmentioning

confidence: 99%

“…Purpose/ Objective of work Methodology Parameters measured Achieved Outcome [1] To help the patient for analyzing each of the twelve hand and arm rehabilitation exercises has been completed…”

Section: Refmentioning

confidence: 99%

“…One method used the accelerometer sensor's time series image data with CNN to recognize the exercise pose. This framework achieves 98.61% accuracy [1]. Optimization is required for the DNN with RGB image approach to improve accuracy [8].…”

Section: Figure 8 Performance Comparison Of the Proposed System With ...mentioning

confidence: 99%

“…In rural areas, personnel, still rehabilitation training facilities, are unreachable. This causes the prevalence of MSD to be wider across the globe [1]. The most significant health issue faced by IT sector personnel is WMSD.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence (AI) Powered Precise Classification of Recuperation Exercises for Musculoskeletal Disorders

Ekambaram¹,

Ponnusamy²,

Natarajan³

et al. 2023

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Musculoskeletal pain is one of the significant health issues faced by the Information Technology (IT) industries and health-care professional personnel. The current IT sector requires people working on sitting in one place for long hours (~3-4 hours). This causes severe hip, neck, and shoulder pain and may lead to paralysis. Convergence of a threedimensional (3D) image into a plane-based projection to precisely classify the trunk extension and flexion, wrist extension and flexion exercises posture images. Because the predictions in different planes are incorrectly detected during the convergence process, a deep learning algorithm is a superior technique for improving recognition accuracy and processing speed. 200 image datasets of the wrist, trunk extension, and flexion exercise posture are created at various planes. The proposed deep learning algorithm performance is compared with CNN with accelerometer sensing image data, DNN with RGB images, CNN-GRU with Kinect Depth images, Deep Hybrid CNN with body portion keyframe images, Spatial Transform Networks (STN) with attention-based multi-scale CNN with Grad CAM images. The observation demonstrates the efficiency of these systems in musculoskeletal rehabilitation therapy in that the suggested deep learning-based system successfully identifies the completion of rehabilitation activities with a recognition of training accuracy of 98.12% and validation accuracy of 95%. Our approach can track and enhance the efficiency of patients' rehabilitation training with greater satisfactory precision than some other cutting-edge conventional CNN-based baseline architecture.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Refmentioning

confidence: 99%

Section: Figure 8 Performance Comparison Of the Proposed System With ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence (AI) Powered Precise Classification of Recuperation Exercises for Musculoskeletal Disorders

Ekambaram¹,

Ponnusamy²,

Natarajan³

et al. 2023

View full text Add to dashboard Cite

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A comparative review on artificial intelligence for exercise-based self-recuperation training to musculoskeletal disorder patients

Ekambaram,

Ponnusamy

2023

Iv International Scientific Forum on Computer and Energy Sciences (Wfces Ii 2022)

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A Survey of Wearable Sensors and Machine Learning Algorithms for Automated Stroke Rehabilitation

Sengupta,

Rao,

Yan

et al. 2024

IEEE Access

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Stroke is one of the leading causes of disability among the elderly population and is a significant public health problem worldwide. The main impact of stroke is functional disabilities due to motor impairment after stroke. Advances in modern medicine and technology have significantly improved diagnosis and treatment; however, most post-stroke care is based on the effectiveness of rehabilitation. Stroke rehabilitation depends on two main components: (i) training (or therapy) to restore the patient to pre-stroke mobility and (ii) assessing motor functionality of affected patients performing activities to track motor recovery. This article highlights how combining wearable devices and machine learning (ML) produces new pathways for effective stroke rehabilitation. While wearable devices help capture patient movements at much finer time resolutions, ML allows us to build predictive models from wearable data to assist clinicians in diagnosis and treatments. Specifically, we expand on how wearable devices and ML can improve monitoring quality in training intervention, assessment, and remote monitoring. In addition, we provide our main findings from the literature, research challenges, and future directions in post-stroke therapies using wearable devices and ML.INDEX TERMS Stroke rehabilitation, wearable devices, machine learning, interventions, remote monitoring.

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A Deep Learning-Based Upper Limb Rehabilitation Exercise Status Identification System

Cited by 8 publications

References 61 publications

Artificial Intelligence (AI) Powered Precise Classification of Recuperation Exercises for Musculoskeletal Disorders

Artificial Intelligence (AI) Powered Precise Classification of Recuperation Exercises for Musculoskeletal Disorders

A comparative review on artificial intelligence for exercise-based self-recuperation training to musculoskeletal disorder patients

A Survey of Wearable Sensors and Machine Learning Algorithms for Automated Stroke Rehabilitation

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