RT Conference Proceedings
T1 Deep Neural Network to Remove Motion Artifacts from Heart Rate Sensor Embedded on Handle Cane.
A1 Villalba-Bravo, Rafael
A1 Ruiz Barroso, Paula
A1 Castro, Francisco M.
A1 Trujillo-León, Andrés
A1 Guil-Mata, Nicolás
A1 Vidal-Verdú, Fernando
K1 Biosensores
K1 Ayudas técnicas para ancianos
AB Devices worn on the body that track physiological metrics, such as heart rate (HR) and skin conductance, have gained popularity and are typically found in items like smart-watches and bracelets. However, these measurements can be compromised by the movement of the device relative to the skin, which creates artifacts. For certain groups, such as the elderly, embedding sensors into daily-use items, like walking sticks, might offer better adherence. Nonetheless, the issue of motion artifacts becomes particularly challenging in these scenarios. This document presents a method based on a Deep Neural Network to compute the HR from a noisy signal registered by a sensor embedded in a cane. We evaluate our model in a novel dataset obtaining a mean absolute error of 9.81 ± 0.45 beats per minute, which results in a deviation of 10.75% that is in the order of the results obtained by common commercial smartwatches and bracelets.
PB IEEE
YR 2024
FD 2024
LK https://hdl.handle.net/10630/36629
UL https://hdl.handle.net/10630/36629
LA eng
NO R. Villalba-Bravo, P. Ruiz-Barroso, F. M. Castro, A. Truiillo-León, N. Guil and F. Vidal-Verdú, "Deep Neural Network to Remove Motion Artifacts from Heart Rate Sensor Embedded on Handle Cane," 2024 IEEE SENSORS, Kobe, Japan, 2024, pp. 1-4, doi: 10.1109/SENSORS60989.2024.10784567
NO https://conferences.ieeeauthorcenter.ieee.org/author-ethics/guidelines-and-policies/ (submitted)
NO This work was supported by the Spanish Ministerio de Ciencia, Innovación y Universidades and by the European ERDF program funds under contracts PID2021-1250910B-IOO and PID2022-1365750B-IOO.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026