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.
