Identification and Analysis of Limb Rehabilitation Signal Based on Wavelet Transform

Zhang, Chao; Zou, Jian; Ma, Zhongjing

doi:10.18280/ts.380316

Cited by 4 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It plays a crucial role in guiding the diagnosis and monitoring of diseases related to bioelectric signals 3 . For instance, EMG signals can reflect the activity and functional status of muscles, making them invaluable in clinical diagnostics and rehabilitation training within the neuromuscular system 4,5 . By examining EMG signals from affected areas, parameters such as denervation potentials and nerve conduction velocity can be obtained, aiding in assessing the extent of a nerve injury 6,7 .…”

Section: Introductionmentioning

confidence: 99%

Multichannel microneedle dry electrode patches for minimally invasive transdermal recording of electrophysiological signals

Liu,

Xu,

Huang

et al. 2024

Microsyst Nanoeng

View full text Add to dashboard Cite

The collection of multiple-channel electrophysiological signals enables a comprehensive understanding of the spatial distribution and temporal features of electrophysiological activities. This approach can help to distinguish the traits and patterns of different ailments to enhance diagnostic accuracy. Microneedle array electrodes, which can penetrate skin without pain, can lessen the impedance between the electrodes and skin; however, current microneedle methods are limited to single channels and cannot achieve multichannel collection in small areas. Here, a multichannel (32 channels) microneedle dry electrode patch device was developed via a dimensionality reduction fabrication and integration approach and supported by a self-developed circuit system to record weak electrophysiological signals, including electroencephalography (EEG), electrocardiogram (ECG), and electromyography (EMG) signals. The microneedles reduced the electrode–skin contact impedance by penetrating the nonconducting stratum corneum in a painless way. The multichannel microneedle array (MMA) enabled painless transdermal recording of multichannel electrophysiological signals from the subcutaneous space, with high temporal and spatial resolution, reaching the level of a single microneedle in terms of signal precision. The MMA demonstrated the detection of the spatial distribution of ECG, EMG and EEG signals in live rabbit models, and the microneedle electrode (MNE) achieved better signal quality in the transcutaneous detection of EEG signals than did the conventional flat dry electrode array. This work offers a promising opportunity to develop advanced tools for neural interface technology and electrophysiological recording.

show abstract

Section: Introductionmentioning

confidence: 99%

Multichannel microneedle dry electrode patches for minimally invasive transdermal recording of electrophysiological signals

Liu,

Xu,

Huang

et al. 2024

Microsyst Nanoeng

View full text Add to dashboard Cite

show abstract

“…However, self-monitored patient rehab training is still a complex task [11,12]. The surface EMG signal-based upper limb rehabilitation action recognition approach extracts the feature method in the order of timedomain, frequency-domain, time-frequency, and entropy through physiological signals [13,14]. Figure 1 shows the various deep learning techniques used in image-based exercise pose analysis.…”

Section: Introductionmentioning

confidence: 99%

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

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.

show abstract

Musculoskeletal Rehabilitation Status Monitoring Based on sEMG

Han

Zhao²,

Wang³

et al. 2021

Mobile Information Systems

View full text Add to dashboard Cite

The reduction and improper movements in people’s modern life will lead to physical discomfort, pain, and inflammation, which have generally affected the quality of people’s daily life and work efficiency. The pain caused by improper movements are called musculoskeletal pain, which can be relieved or eliminated with treatment. Musculoskeletal disorders are actually one of the most common medical conditions, which affects approximately one quarter of all adults in the world. Although surface electromyography (sEMG) is an acknowledged technology in musculoskeletal rehabilitation study, it is considerably significant to monitor the musculoskeletal rehabilitation status based on sEMG. In order to monitor the musculoskeletal rehabilitation status, we combine fuzzy theory with neural network. This article proposes variable size, sliding window-based, generalized, dynamic, fuzzy neural network (GD-FNN), musculoskeletal rehabilitation status monitoring, that is, the window length of sliding window of sample data changes with the size of sample period. Finally, this study made a simulation on subjects, and the experimental results show that the proposed variable size, sliding window-based GD-FNN, musculoskeletal rehabilitation status monitoring method not only has good monitoring effect but also put on a good performance in root-mean-squared error (RMSE) and mean absolute percentage error (MAPE).

show abstract

Identification and Analysis of Limb Rehabilitation Signal Based on Wavelet Transform

Cited by 4 publications

References 24 publications

Multichannel microneedle dry electrode patches for minimally invasive transdermal recording of electrophysiological signals

Multichannel microneedle dry electrode patches for minimally invasive transdermal recording of electrophysiological signals

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

Musculoskeletal Rehabilitation Status Monitoring Based on sEMG

Contact Info

Product

Resources

About