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.

As the population ages, the concept of active seniors has been emerging recently. Among various body parts that are cared for by an active elderly, the shoulder has a unique exercise structure. Therefore, the incidence of shoulder injuries might be high. In the case of a shoulder disease, the method of measuring the movement angle of the shoulder is mainly used. To measure the movement angle of a shoulder accurately, a goniometer is used. In addition, we suggested self-diagnosis, believing that if shoulder disease could be detected early through self-diagnosis, rapid treatment will be possible. This paper measured and compared shoulder angles with the goniometer, OpenCV, and motion capture systems to determine measurement errors between them. Through experimental results of this paper, the possibility of self-diagnosis with precise measurement of the movement angle of a shoulder oneself with a goniometer was confirmed even if the expert could not measure the shoulder angle.