RESUMEN

Given the popularity of wrist-worn devices, particularly smartwatches, the identification of manual movement patterns has become of utmost interest within the research field of Human Activity Recognition (HAR) systems. In this context, by leveraging the numerous sensors natively embedded in smartwatches, the HAR functionalities that can be implemented in a watch via software and in a very cost-efficient way cover a wide variety of applications, ranging from fitness trackers to gesture detectors aimed at disabled individuals (e.g., for sending alarms), promoting behavioral activation or healthy lifestyle habits. In this regard, for the development of artificial intelligence algorithms capable of effectively 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 collection of files containing the signals captured by a Shimmer 3 sensor node, which includes an accelerometer, a gyroscope, a magnetometer and a barometer, during the execution of different typical hand movements. For that purpose, 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 involving hand mobility. Participants wore the sensor node on their dominant hand throughout the experiments.