Wearable sensors are susceptible to degradation from physical wear, moisture, and desiccation, which can result in signal attenuation and unreliable data. This pilot study, conducted in a controlled single-participant setting, introduces a framework to quantify and characterize sensor degradation while restoring corrupted electromyography (EMG) signals. Four types of sensors—polyethylene terephthalate film, parylene film, 3M bioelectrode pads, and microneedle patches—were affixed to the left forearm in a three-electrode EMG configuration. Impedance at 100 Hz was monitored as an indicator of sensor aging, while a one-dimensional convolutional autoencoder was employed to reconstruct degraded EMG signals using a loss function that incorporated both time-domain and frequency-domain error terms. The reconstruction loss showed a correlation with impedance changes, providing a practical metric for assessing sensor health. These findings highlight the feasibility of real-time signal recovery and its potential to extend the lifespan of sensors.

In this study, polymer electrolyte membrane fuel cells (PEMFCs) were humidified with NaCl solutions. NaCl solutions were provided to the cathode side of fuel cells by bubbling. De-Ionized water, 3.5 wt% NaCl solution, and 20 wt% NaCl solution were used to evaluate the effects of NaCl. Current density-voltage curves and electrochemical impedance spectroscopies (EIS) of fuel cells were measured. Additionally, the constant-voltage mode long-term stability of PEMFCs humidified with NaCl solution were investigated. Constant-voltage measurements and EIS results imply that the degradation of fuel cells is clearly related with the concentration of NaCl solutions.