Smartphones, smartwatches, fitness trackers, and ad-hoc wearable devices are being increasingly used to monitor human activities. Usually, machine-learning-based algorithms process data acquired by their sensors to classify human activities. The success of those algorithms mostly depends on the availability of training (labeled) data. In this letter we present a new smartphone accelerometer dataset designed for activity recognition. The dataset includes 7,013 activities performed by 30 subjects, mostly females, of ages ranging from 18 to 60 years. Activities are divided in 17 fine grained classes grouped in two coarse grained classes: 9 types of activities of daily living (ADL) and 8 types of falls. The dataset, benchmarked with two different classifiers, thanks to its unique features will be of interest to the scientific community.
['Daniela Micucci', 'Marco Mobilio', 'Paolo Napoletano']
UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphones