Feasibility of EMG-based ANN controller for a real-time virtual reality simulation

Xiong, Anbin; Lin, Guangmo; Zhao, Xingang; Han, Jianda; Liu, Guangjun

doi:10.1109/iecon.2012.6389150

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…For stroke survivors at BS IV or above, although the synergy pattern still exists, due to the significant increment of their muscle strength and endurance in most cases, a better muscle control enables them to finish the movements at a higher standard with some consistency [ 26 ]. Stroke survivors at BS VI are very rare in hospitals, and they can perform ADLs almost the same as healthy people [ 27 ]. To some extent, the participation of healthy subjects acts as that of stroke survivors at BS VI.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…The data collected from accelerometers and gyroscopes were low-pass filtered with a fourth-order Butterworth filter (cut-off frequency: 5 Hz), while the sEMG signal was band-pass filtered with a fourth-order Butterworth filter (cut-off frequency: 10–500 Hz) to eliminate the surrounding noise [ 27 ]. The Z-score normalization method was used for signals of each activity.…”

Section: Materials and Methodsmentioning

confidence: 99%

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Exploration of Human Activity Recognition Using a Single Sensor for Stroke Survivors and Able-Bodied People

Meng

An-jing

Chen

et al. 2021

Sensors

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Commonly used sensors like accelerometers, gyroscopes, surface electromyography sensors, etc., which provide a convenient and practical solution for human activity recognition (HAR), have gained extensive attention. However, which kind of sensor can provide adequate information in achieving a satisfactory performance, or whether the position of a single sensor would play a significant effect on the performance in HAR are sparsely studied. In this paper, a comparative study to fully investigate the performance of the aforementioned sensors for classifying four activities (walking, tooth brushing, face washing, drinking) is explored. Sensors are spatially distributed over the human body, and subjects are categorized into three groups (able-bodied people, stroke survivors, and the union of both). Performances of using accelerometer, gyroscope, sEMG, and their combination in each group are evaluated by adopting the Support Vector Machine classifier with the Leave-One-Subject-Out Cross-Validation technique, and the optimal sensor position for each kind of sensor is presented based on the accuracy. Experimental results show that using the accelerometer could obtain the best performance in each group. The highest accuracy of HAR involving stroke survivors was 95.84 ± 1.75% (mean ± standard error), achieved by the accelerometer attached to the extensor carpi ulnaris. Furthermore, taking the practical application of HAR into consideration, a novel approach to distinguish various activities of stroke survivors based on a pre-trained HAR model built on healthy subjects is proposed, the highest accuracy of which is 77.89 ± 4.81% (mean ± standard error) with the accelerometer attached to the extensor carpi ulnaris.

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Section: Materials and Methodsmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

Exploration of Human Activity Recognition Using a Single Sensor for Stroke Survivors and Able-Bodied People

Meng

An-jing

Chen

et al. 2021

Sensors

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Study on sEMG-based exercise therapy for upper limb of severe hemiplegic patients

Fan

Zhao

Luo

et al. 2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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sEMG, as a kind of bioelectrical signal reflecting muscle motion state, generally applies to motion recognition and human interface. Healthy subjects are selected in most studies, while for hemiplegic patients, especially patients with severe hemiplegia, high accuracy motion recognition is difficult to acquire due to the non-ideal sEMG signal from dysfunction muscles. Therefore, this paper presents an upper limb exercise therapy, based on 5 defined motions and 6 Muscle-Units, for patients with severe hemiplegia. Through the sampling and analysis of sEMG signals from 8 subjects, including 4 healthy and 4 hemiplegic patients, we draw a conclusion of the relevance between specific motions and Muscle-Units, which can be used as a reference for paralyzed arm training. According to this relevance, six Muscle-Units can be classified into two categories: major Muscle-Units and minor Muscle-Units. In order to improve the interest and positivity of patients, a PC based virtual interactive platform is established. The sEMG signal from major Muscle-Units is processed with a moving average algorithm, and the result is used as the control signal for training interaction.

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A preliminary study of force estimation based on surface EMG: Towards neuromechanically guided soft oral rehabilitation robot

Sun

Bai

et al. 2015

2015 IEEE International Conference on Rehabilitation Robotics (ICORR)

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Feasibility of EMG-based ANN controller for a real-time virtual reality simulation

Cited by 4 publications

References 14 publications

Exploration of Human Activity Recognition Using a Single Sensor for Stroke Survivors and Able-Bodied People

Exploration of Human Activity Recognition Using a Single Sensor for Stroke Survivors and Able-Bodied People

Study on sEMG-based exercise therapy for upper limb of severe hemiplegic patients

A preliminary study of force estimation based on surface EMG: Towards neuromechanically guided soft oral rehabilitation robot

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