研究目的
To develop a hybrid sensor system integrating sEMG, NIRS, and MMG for improved muscle motion monitoring and to explore the relationships between these signals, blood oxygen metabolism, grip force, and muscle fatigue.
研究成果
The hybrid sensor system successfully integrates sEMG, NIRS, and MMG, providing comprehensive muscle activity detection. Experiments show that sEMG and NIRS signal intensities increase with grip force, while MMG shows less change. In fatigue, NIRS indicates shifts in oxygen metabolism. This system enhances understanding of muscle dynamics from multiple modalities, suggesting potential for improved prosthetic control and fatigue monitoring. Future work should focus on long-term studies and clinical applications.
研究不足
The system may have limitations in robustness due to electrode-skin interface issues for sEMG, and the compact design might restrict detection depth for NIRS. Muscle fatigue experiments are short-term and may not fully represent long-term effects. Optimization could include improving signal conditioning for better noise reduction and expanding to more muscle groups.
1：Experimental Design and Method Selection:
The study designs a hybrid sensor system combining sEMG, NIRS, and MMG acquisition circuits into a compact sensor. It uses theoretical models such as the modified Beer-Lambert law for NIRS signal processing and standard electrophysiological principles for sEMG and MMG.
2：Sample Selection and Data Sources:
Human subjects are used, with the sensor fixed on the extensor digitorum muscle. Data is collected during incremental grip force and muscle fatigue experiments.
3：List of Experimental Equipment and Materials:
Includes electrodes (gold-plated copper), near-infrared LED (Epitex Inc), photoelectric detector (OPT101 by TI), instrument amplifier (INA326 by TI), operational amplifier (AD8603 by Analog Devices), constant current driver (DM11A), microcontroller (MSP430F149), Bluetooth module, and a standardized electronic dynamometer for measuring MVC.
4：Experimental Procedures and Operational Workflow:
For incremental grip force experiment, subjects maintain resting state, then increase grip force to 30%, 50%, and 80% of MVC with rests in between, while real-time feedback is provided. For muscle fatigue experiment, subjects maintain 50% MVC continuously. Signals are sampled at 1000Hz, converted to digital, and transmitted via Bluetooth.
5：Data Analysis Methods:
Signals are filtered and amplified; NIRS data is processed using modified Beer-Lambert law to calculate hemoglobin concentrations; sEMG and MMG signals are normalized for trend analysis; statistical analysis of signal trends and relationships is performed.
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Operational Amplifier
AD8603
Analog Devices
Provides two-stage amplification for sEMG signals due to their small amplitude.
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Instrument Amplifier
INA326
TI
Amplifies differential mode signals and suppresses common mode signals in the sEMG signal conditioning module.
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Near-infrared LED
Epitex Inc
Emits near-infrared light at wavelengths of 730nm, 805nm, and 850nm for NIRS signal detection.
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Constant Current Driver
DM11A
Drives the near-infrared light source in the NIRS signal conditioning module.
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Photoelectric Detector
OPT101
TI
Detects near-infrared light intensity after it passes through muscle tissues, with a linear relationship between output voltage and light intensity.
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Microcontroller Unit
MSP430F149
Controls the near-infrared LED and performs analog-to-digital conversion of signals.
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Bluetooth Module
Transmits digital signals to the host computer.
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Electrodes
Collect sEMG signals from the skin surface, made of gold-plated copper for stable contact.
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High Sensitivity Microphone
Detects MMG signals by sensing vibrations from muscle contractions, fixed in a conical cavity to isolate noise.
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Electronic Dynamometer
Measures maximum voluntary contraction (MVC) for grip force calibration in experiments.
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