Several research studies have investigated the human activity recognition (HAR) domain to detect and recognise patterns of daily human activities. However, the accurate and automatic assessment of activities of daily living (ADLs) through machine learning algorithms is still a challenge, especially due to limited availability of realistic datasets to train and test such algorithms. The dataset contains data from 52 participants in total (26 women, and 26 men). The data for these participants was collected in two phases: 33 participants initially, and 19 further participants later on. Participants performed up to 5 repetitions of 24 different ADLs. Firstly, we provide an annotated description of the dataset collected by wearing a wrist-worn measurement device, Empatica E4. Secondly, we describe the methodology of the data collection and the real context in which participants performed the selected activities. Finally, we present some examples of recent and relevant target applications where our dataset can be used, namely lifelogging, behavioural analysis and measurement device evaluation. The authors consider the dissemination of this dataset can highly benefit the research community, and specially those involved in the recognition of ADLs, and/or in the removal of cues that reveal identity.
Keywords: Accelerometry data; Active and assisted living; Human action recognition; Wearable device; Wrist-worn device.
© 2022 The Authors.