RT Journal Article
T1 UMAHand: A dataset of inertial signals of typical hand activities
A1 Casilari-Pérez, Eduardo
A1 Barbosa-Galeano, Jennifer
A1 González-Cañete, Francisco Javier
K1 Ingeniería biomédica
K1 Detectores
K1 Actividad física
AB Given the popularity of wrist-worn devices, particularly smartwatches, the identification of manual movement pat- terns has become of utmost interest within the research field of Human Activity Recognition (HAR) systems. In this con- text, by leveraging the numerous sensors natively embedded in smartwatches, the HAR functionalities that can be imple- mented in a watch via software and in a very cost-efficient way cover a wide variety of applications, ranging from fit- ness trackers to gesture detectors aimed at disabled individ- uals (e.g., for sending alarms), promoting behavioral activa- tion or healthy lifestyle habits. In this regard, for the devel- opment of artificial intelligence algorithms capable of effec- tively discriminating these activities, it is of great importance to have repositories of movements that allow the scientific community to train, evaluate, and benchmark new proposals of movement detectors. The UMAHand dataset offers a col- lection of files containing the signals captured by a Shim- mer 3 sensor node, which includes an accelerometer, a gy- roscope, a magnetometer and a barometer, during the ex- ecution of different typical hand movements. For that pur- pose, the measurements from these four sensors, gathered at a sampling rate of 100 Hz, were taken from a group of 25 volunteers (16 females and 9 males), aged between 18 and 56, during the performance of 29 daily life activities involv- ing hand mobility. Participants wore the sensor node on their dominant hand throughout the experiments.
PB Elsevier
YR 2024
FD 2024-07-10
LK https://hdl.handle.net/10630/32191
UL https://hdl.handle.net/10630/32191
LA eng
NO This research was funded by the Spanish Ministry of Science, Innovation, and Universities ( MCIN/AEI/10.13039/50110 0 011033 ) and NextGenerationEU/PRTR Funds under grant TED2021- 130456B-I00 , by Universidad de Málaga /CBUA , Campus de Excelencia Internacional Andalucia Tech (grant B4-2023-12 ) and DIANA TIC171 PAIDI research group. Partial funding for open access charge: Universidad de Málaga / CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026