Abstract
In this paper, we present HTAD: A Home Tasks Activities
Dataset. The dataset contains wrist-accelerometer and audio data from
people performing at-home tasks such as sweeping, brushing teeth, washing hands, or watching TV. These activities represent a subset of activities that are needed to be able to live independently. Being able to detect
activities with wearable devices in real-time is important for the realization of assistive technologies with applications in different domains such
as elderly care and mental health monitoring. Preliminary results show
that using machine learning with the presented dataset leads to promising results, but also there is still improvement potential. By making this
dataset public, researchers can test different machine learning algorithms
for activity recognition, especially, sensor data fusion methods
Dataset. The dataset contains wrist-accelerometer and audio data from
people performing at-home tasks such as sweeping, brushing teeth, washing hands, or watching TV. These activities represent a subset of activities that are needed to be able to live independently. Being able to detect
activities with wearable devices in real-time is important for the realization of assistive technologies with applications in different domains such
as elderly care and mental health monitoring. Preliminary results show
that using machine learning with the presented dataset leads to promising results, but also there is still improvement potential. By making this
dataset public, researchers can test different machine learning algorithms
for activity recognition, especially, sensor data fusion methods