Abstract
Wearable devices like the OH1 are increasingly used for real-time health monitoring, particularly for measuring heart rate (BPM). However, their accuracy is often compromised by motion artifacts, introducing significant noise into the measurements. This study specifically addresses the issue of noise generated by the OH1 wearable device during eleven different hand movements. To tackle this problem, we implemented a precise experimental setup involving device calibration, stable testing conditions, and participant training to ensure high consistency in hand movements. Additionally, machine learning algorithms were employed to separate noise from desired hand movement data. Our results indicate that certain hand movements, such as lifting arms and shoulder rotations, produce higher noise levels, while others, like placing hands on the table, generate minimal noise. These findings provide valuable insights for developing effective noise reduction algorithms, ultimately enhancing the accuracy and reliability of BPM measurements from wearable devices.
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