Electromyogram (EMG) Signal Processing Analysis for Clinical Rehabilitation Application

Saad, Ismail; Bais, Nur Husna; Seng, C. Bun; H., Mohd. Zuhir; Bolong, Nurmin

doi:10.1109/aims.2015.76

Cited by 18 publications

(5 citation statements)

References 5 publications

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“…The results illustrated in Figure 12 showing that without adopting this adaptive control strategy, the intensity of muscle activity decreases with time, and after using the adaptive control strategy, the intensity of muscle activity could be maintained at a required level to ensure and sustain the rehabilitation training effectiveness. Although there are some similar systems for muscle activity monitoring and rehabilitation [ 51 , 71 , 72 ], few studies developed a real time wearable system with simple operation, and traditional methods indicate the difficulty to effectively apply intensive training programs without real time monitoring [ 47 ]. With the purpose to improve rehabilitation effects, it is important to ensure that the involved patients are in the excited status for repetitive movements in order to stimulate neuroplasticity [ 73 ].…”

Section: Resultsmentioning

confidence: 99%

“…Full-wave precision rectifiers, which are integrated for high-frequency noise filtering, are used to rectify the measured EMG signals. Operational amplifiers are applied to amplify the measured EMG signals again [ 47 , 48 ]. EMG signals are delivered to the A/D converter of the STM32L152 chip for digital DAQ, and the obtained data are sent to a remote receiver (such as smartphone or laptop) for the recognition of upper limb activities.…”

Section: Methodsmentioning

confidence: 99%

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Wearable Physiological Monitoring System Based on Electrocardiography and Electromyography for Upper Limb Rehabilitation Training

Zhao

Liu

Gong

et al. 2020

Sensors

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Secondary injuries are common during upper limb rehabilitation training because of uncontrollable physical force and overexciting activities, and long-time training may cause fatigue and reduce the training effect. This study proposes a wearable monitoring device for upper limb rehabilitation by integrating electrocardiogram and electromyogram (ECG/EMG) sensors and using data acquisition boards to obtain accurate signals during robotic glove assisting training. The collected ECG/EMG signals were filtered, amplified, digitized, and then transmitted to a remote receiver (smart phone or laptop) via a low-energy Bluetooth module. A software platform was developed for data analysis to visualize ECG/EMG information, and integrated into the robotic glove control module. In the training progress, various hand activities (i.e., hand closing, forearm pronation, finger flexion, and wrist extension) were monitored by the EMG sensor, and the changes in the physiological status of people (from excited to fatigue) were monitored by the ECG sensor. The functionality and feasibility of the developed physiological monitoring system was demonstrated by the assisting robotic glove with an adaptive strategy for upper limb rehabilitation training improvement. The feasible results provided a novel technique to monitor individual ECG and EMG information holistically and practically, and a technical reference to improve upper limb rehabilitation according to specific treatment conditions and the users’ demands. On the basis of this wearable monitoring system prototype for upper limb rehabilitation, many ECG-/EMG-based mobile healthcare applications could be built avoiding some complicated implementation issues such as sensors management and feature extraction.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Wearable Physiological Monitoring System Based on Electrocardiography and Electromyography for Upper Limb Rehabilitation Training

Zhao

Liu

Gong

et al. 2020

Sensors

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“…It is a small electrical signal generated by human muscle during contraction. The research shows that the amplitude of sEMG signal is related to the size of muscle force, movement speed and acceleration, and the Root Mean Square (RMS) of sEMG reflects the number of motor units activated during muscle activity [27][28]. Therefore, in this paper, the ROM and sEMG signals are selected as features to evaluate the UL motor function of stroke patients.…”

Section: A Features Selectionmentioning

confidence: 99%

Rehabilitation Evaluation of Upper Limb Motor Function for Stroke Patients Based on Belief Rule Base

Li,

Wang,

Yin

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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In the process of rehabilitation treatment for stroke patients, rehabilitation evaluation is a significant part in rehabilitation medicine. Researchers intellectualized the evaluation of rehabilitation evaluation methods and proposed quantitative evaluation methods based on evaluation scales, without the clinical background of physiatrist. However, in clinical practice, the experience of physiatrist plays an important role in the rehabilitation evaluation of patients. Therefore, this paper designs a 5 degrees of freedom (DoFs) upper limb (UL) rehabilitation robot and proposes a rehabilitation evaluation model based on Belief Rule Base (BRB) which can add the expert knowledge of physiatrist to the rehabilitation evaluation. The motion data of stroke patients during active training are collected by the rehabilitation robot and signal collection system, and then the upper limb motor function of the patients is evaluated by the rehabilitation evaluation model. To verify the accuracy of the proposed method, Back Propagation Neural Network (BPNN) and Support Vector Machines (SVM) are used to evaluate. Comparative analysis shows that the BRB model has high accuracy and effectiveness among the three evaluation models. The results show that the rehabilitation evaluation model of stroke patients based on BRB could help physiatrists to evaluate the UL motor function of patients and master the rehabilitation status of stroke patients.

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“…The differential input of the precision n instrumentation amplifiers was used to amplify the sEMG signals for STM32 chip acquisition. The full-wave precision rectifiers were applied to filter the noise with the amplified signals, and operational amplifiers were applied to amplify them again [48] [49]. The hardware processed sEMG signals were delivered to the STM32 chip for converting digital value, and the obtained data were sent to a remote receiver via the BLE module for further analysis and finger activity recognition.…”

Section: The Finger Sensing and Robotic Controlling Systemmentioning

confidence: 99%

A bionic soft robotic glove mimicking finger actions based on sEMG recognition

Zhao¹,

Wang²,

Lei³

et al. 2021

Preprint

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Compared with rigid robots, soft robotics is more suitable to develop anthropomorphic digits that mimics the biological structures and dexterous motions of human finger. This study proposed a surface electromyogram (sEMG) sensors-based soft robotic glove system which was able to recognize the finger activities and execute the same operation via the bionic glove. Finger activities can be recognized by using electrodes sensors to monitor the electric potential variations on specific surface of the forearm muscle regions. A hybrid robotic digit was designed that utilizes pneumatic bellow actuators to satisfy the anatomical range of the finger motion in order to mimic finger action according to sEMG information. The moving trajectory of digit tip and the range motion of each joint of the robotic digit were measured in experiments under the pressure from 0kPa to 70kPa. The bionic soft robotic glove successfully demonstrated the finger action recognition and robotic digits controlling for a variety of manipulation tasks. The feasible results provided a novel technique for controlling the soft robotic glove through sEMG signals holistically and practically, and also give inspiration and guidance for multiple fingers remote operational applications.

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Electromyogram (EMG) Signal Processing Analysis for Clinical Rehabilitation Application

Cited by 18 publications

References 5 publications

Wearable Physiological Monitoring System Based on Electrocardiography and Electromyography for Upper Limb Rehabilitation Training

Wearable Physiological Monitoring System Based on Electrocardiography and Electromyography for Upper Limb Rehabilitation Training

Rehabilitation Evaluation of Upper Limb Motor Function for Stroke Patients Based on Belief Rule Base

A bionic soft robotic glove mimicking finger actions based on sEMG recognition

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