ArticleData in brief2022
Dataset of acceleration signals recorded while performing activities of daily living.
Article in Data in brief, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- UMATBrush: A dataset of inertial signals of toothbrushing activities.Data in brief · 2025Article
- Generalised machine learning models outperform personalised models for cognitive load classification in real-life settings.Frontiers in digital health · 2025Article
- Multimodal data for behavioural authentication in Internet of Things environments.Data in brief · 2024Article
- UMAHand: A dataset of inertial signals of typical hand activities.Data in brief · 2024Article
- Human Action Recognition in Smart Living Services and Applications: Context Awareness, Data Availability, Personalization, and Privacy.Sensors (Basel, Switzerland) · 2023Review
- Assessing Impact of Sensors and Feature Selection in Smart-Insole-Based Human Activity Recognition.Methods and protocols · 2022Article
- Dataset of acceleration signals recorded while performing activities of daily living.Data in brief · 2022Article
- Privacy-Preserving Human Action Recognition with a Many-Objective Evolutionary Algorithm.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.