Comparing wearable devices with wet and textile electrodes for activity recognition

Area of Science:

• Biomedical Engineering
• Wearable Technology
• Signal Processing

Background:

• Wearable electrocardiogram (ECG) devices are increasingly used for health monitoring.
• Current ECG devices often filter out high-frequency components, discarding valuable physiological data.
• Muscle activation signals contain information relevant to physical activity.

Purpose of the Study:

• To explore the identification of physical activities using muscle activation signals captured by wearable ECG devices.
• To investigate the extraction of high-frequency components from ECG signals for activity recognition.
• To compare the performance of wet and dry textile electrodes in capturing muscle activation signals.

Main Methods:

• Utilized wearable ECG recording devices with both wet and textile electrodes.
• Developed methods to extract high-frequency signal components related to muscle activation.
• Analyzed and compared signal quality and artifact levels between electrode types.

Main Results:

• Successfully extracted high-frequency components corresponding to muscle activation from ECG signals.
• Achieved good performance in activity identification using both wet and dry electrodes.
• Observed that dry textile electrodes yielded signals with fewer muscle activation-related artifacts.

Conclusions:

• High-frequency components in ECG recordings can be effectively used for muscle activation-based activity identification.
• Wearable ECG devices, particularly those with dry textile electrodes, show promise for non-invasive activity monitoring.
• Dry textile electrodes offer an advantage due to reduced signal artifacts, enhancing the reliability of activity recognition.