[Research Progress in Data Acquisition and Intelligent Sensing Methods for Lumbar Electromyographic Signals]

Area of Science:

• Biomedical Engineering
• Neurology
• Geriatrics

Context:

• Population aging increases the prevalence of neuromuscular diseases.
• Low back pain is a significant health concern in the elderly population.
• Accurate analysis of low back pain is crucial for timely patient intervention and rehabilitation.

Purpose:

• To review current methods for acquiring lumbar electromyography (EMG) signals.
• To introduce signal characteristics of various electrode types (needle, surface, array).
• To highlight emerging trends in EMG acquisition and analysis for low back pain.

Summary:

• This study examines lumbar electromyography (EMG) signal acquisition using different sensors and electrode types.
• It discusses signal processing algorithms and identifies wireless sensors and deep learning as future development directions.
• The review provides insights into the characteristics of needle, surface, and array electrodes for EMG signal acquisition.

Impact:

• Provides a comprehensive overview of lumbar EMG signal acquisition and analysis techniques.
• Identifies key technological advancements, such as wireless sensors and deep learning, for improved low back pain assessment.
• Offers prospects for future research and development in the field of neuromuscular disease monitoring in aging populations.