Weichao Guo

Development of a Multi-Channel Compact-Size Wireless Hybrid sEMG/NIRS Sensor System for Prosthetic Manipulation

It is evident that surface electromyography (sEMG)-based approaches have inherent difficulty in coping with modern dominant applications of clinical diagnosis and human machine interface such as prosthetic manipulation. This paper presents a hybrid sensor to attentively overcome the difficulty with a clinical purpose of simultaneously acquiring electrophysiological, hemodynamic, and oxidative metabolic information of muscle activity, also with front-end conditioning circuit and Bluetooth module integrated and packaged. A multi-channel compact-size wireless hybrid sEMG/near-infrared spectroscopy (NIRS) acquisition system is developed, forming a platform to demonstrate individual sEMG and NIRS measurement capabilities, and their combination. Extensive experiments are carried out to explore sensor functionality based on the sEMG, NIRS, and their combination, convincingly addressing the capabilities meeting their commercial or state-of-the-art counterparts. Future work is targeted to extract the sEMG/NIRS sensor-based muscular fatigue which plays a crucial role in biomedical and clinical applications.

Toward an Enhanced Human–Machine Interface for Upper-Limb Prosthesis Control With Combined EMG and NIRS Signals

Advanced myoelectric prosthetic hands are currently limited due to the lack of sufficient signal sources on amputation residual muscles and inadequate real-time control performance. This paper presents a novel human–machine interface for prosthetic manipulation that combines the advantages of surface electromyography (EMG) and near-infrared spectroscopy (NIRS) to overcome the limitations of myoelectric control. Experiments including 13 able-bodied and three amputee subjects were carried out to evaluate both offline classification accuracy (CA) and online performance of the forearm motion recognition system based on three types of sensors (EMG-only, NIRS-only, and hybrid EMG-NIRS). The experimental results showed that both the offline CA and real-time performance for controlling a virtual prosthetic hand were significantly (p

A wireless wearable sEMG and NIRS acquisition system for an enhanced human-computer interface

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A portable multi-channel wireless NIRS device for muscle activity real-time monitoring.

0.05) improved by combining EMG and NIRS. These findings suggest that fusion of EMG and NIRS is feasible to improve the control of upper-limb prostheses, without increasing the number of sensor nodes or complexity of signal processing. The outcomes of this study have great potential to promote the development of dexterous prosthetic hands for transradial amputees.

Mechanomyography Assisted Myoeletric Sensing for Upper-Extremity Prostheses: A Hybrid Approach

Surface electromyography (sEMG) is extensively explored in human-computer interface (HCI); complementary to the electrophysiological activity of the muscles, the hemodynamic information that measured from near infrared spectroscopy (NIRS) is less investigated. Properly combining the sEMG and NIRS would provide a novel approach for HCI applications. This paper presents a multi-channel wireless wearable sEMG and NIRS acquisition system aiming for enhanced human-computer interaction, by providing more information about the muscle activity for subjects motor intention decoding. Extensive tests were carried out to evaluate the system performance. It showed that this novel system proved to be able to capture sEMG signals similar to those of the commercialized sEMG acquisition devices, and had a comparable NIRS sensor performance. Furthermore, simultaneously recording of sEMG and NIRS signals, the system had shown the ability to provide more information about the muscle activities for a better HCI performance. The classification accuracy of 13 hand gesture motions was significantly (P<;0.001) improved by using combined sEMG and NIRS features comparing to sEMG or NIRS features individually, suggesting that the proposed sEMG and NIRS system could be potentially available for an enhanced HCI.

A prosthetic arm based on EMG pattern recognition

Near-infrared spectroscopy (NIRS) is a relative new technology in monitoring muscle oxygenation and hemo-dynamics. This paper presents a portable multi-channel wireless NIRS device for real-time monitoring of muscle activity. The NIRS sensor is designed miniaturized and modularized, to make multi-site monitoring convenient. Wireless communication is applied to data transmission avoiding of cumbersome wires and the whole system is highly integrated. Special care is taken to eliminate motion artifact when designing the NIRS sensor and attaching it to human skin. Besides, the system is designed with high sampling rate so as to monitor rapid oxygenation changes during muscle activities. Dark noise and long-term drift tests have been carried out, and the result indicates the device has a good performance of accuracy and stability. In vivo experiments including arterial occlusion and isometric voluntary forearm muscle contraction were performed, demonstrating the system has the ability to monitor muscle oxygenation parameters effectively even in exercise.

Development of a Hybrid Surface EMG and MMG Acquisition System for Human Hand Motion Analysis

The myoelectric upper-limb prosthetic manipulation is inherently limited by the unreliable sensor-skin interface. This paper presents a hybrid approach to overcome the limitation of electromyography (EMG) through mechanomyography (MMG) assisted myoelectric sensing. An integrated hybrid sensor system was developed for simultaneous EMG and MMG measurement. The hybrid system formed a platform to capture muscular activations in different frequencies. To evaluate the effectiveness of hybrid EMG-MMG sensing, hand motion experiments have been carried out on seven able-bodied and two transradial amputee subjects. It convincingly demonstrated, a significantly (

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Assessment of muscle fatigue by simultaneous sEMG and NIRS: From the perspective of electrophysiology and hemodynamics

) improved classification accuracy (CA). Furthermore, the CA was compensated by 8.7% ~ 33.7% in the presence of 2 ~ 3 fault EMG channels. These results suggest that MMG assisted myoelectric sensing can improve the control performance and robustness. It has great potential to promote the clinical application of multi-functional prosthetic hand with hybrid EMG-MMG sensor system.

Towards Finger Gestures and Force Recognition Based on Wrist Electromyography and Accelerometers.

Although myoelectric prosthesis has been researched for almost 60 years, a high quality prosthetic arm with dexterous hand manipulation and stable control system is always hard to find and amputee acceptance remains low. One of the major challenges is the lack of a portable and powerful embedded system to implement the electromyography (EMG) pattern recognition (PR) algorithms, other challenges include design of dexterous prosthetic hands, the placement of multichannel electrodes, trade-off between low power consumption and small size and so on. This paper presents our latest progress on EMG PR controlled prosthetic hand. In the system, an armband with 8-channel electrodes is used to acquire the EMG signals. A powerful embedded system which deals with the EMG signals decoding algorithms is introduced. On board EMG training and real-time surface myoelectric signals decoding is implemented in the embedded system to control a 6 degree of freedoms (DOFs) prosthetic hand. The promising results of this work show its potential of speeding up the translation of PR prosthetic arm into daily application.