A Stand-Alone Wearable Measurement System for Continuous Hand Tremor Monitoring

Progressive neurodegenerative diseases are often associated with motor symptoms, among which tremor is one of the most debilitating manifestations, significantly compromising daily activities and quality of life. Continuous, non-invasive monitoring of tremor characteristics helps to assess disease progression and optimize treatment strategies. For this reason, wearable measurement systems can provide an objective and non-invasive means for the quantitative characterization and monitoring of tremor-related movements. This study addresses the development of a compact, low-power wearable board for real-time tremor monitoring based on the Nicla Sense ME embedded system. The proposed device performs on-board processing to extract spectral features of movement and wirelessly sends them via Bluetooth Low Energy to a remote host, enabling on-demand visualization or storage of tremor data. The measurement system was technically validated through a comparative experimental protocol based on the simultaneous acquisition of tremor signals using the proposed device and a commercial Shimmer3 IMU as the reference system. The comparison considered both the similarity of the measured spectrograms and the agreement in dominant-frequency estimation. Spectrogram correlation coefficients reached 0.85, with amplitude MAE and RMSE values as low as 0.07 m/s2 and 0.14 m/s2, respectively. For dominant-frequency estimation, Pearson correlation coefficients reached 0.99, with MAE and RMSE values as low as 0.17 Hz and 0.20 Hz. These results support the feasibility of the proposed architecture as a practical wearable solution for quantitative hand tremor monitoring.