ArticleData in brief2020
Activities of daily living with motion: A dataset with accelerometer, magnetometer and gyroscope data from mobile devices.
Article in Data in brief, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 17 citations in OpenAlex.
- A dataset of paired smartphone and BioPlux accelerometer measurements for vertical jump analysis.Scientific data · 2026Article
- A Novel Cooperative AI-Based Fall Risk Prediction Model for Older Adults.Sensors (Basel, Switzerland) · 2025Article
- A Comprehensive Dataset for Activity of Daily Living (ADL) Research Compiled by Unifying and Processing Multiple Data Sources.Journal of personalized medicine · 2025Article
- Real Steps or Not: Auto-Walker Detection in Move-to-Earn Applications.Sensors (Basel, Switzerland) · 2025Article
- Enhancing human activity recognition with machine learning: insights from smartphone accelerometer and magnetometer data.PeerJ. Computer science · 2025Article
- SDHAR-HOME: A Sensor Dataset for Human Activity Recognition at Home.Sensors (Basel, Switzerland) · 2022Article
- Daily motionless activities: A dataset with accelerometer, magnetometer, gyroscope, environment, and GPS data.Scientific data · 2022Article
- Indoor and outdoor environmental data: A dataset with acoustic data acquired by the microphone embedded on mobile devices.Data in brief · 2021Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The dataset presented in this paper is related to the performance of five Activities of Daily Living (ADL) with motion, such as walking, running, standing, walking upstairs, and walking downstairs. These activities were performed with a mobile device in a waistband, containing the data acquired from accelerometer, magnetometer, and gyroscope sensors. These data include the motion data, which allow the characterization of the different types of movement. The data acquisition was performed in open environments by 25 individuals (15 man, and 10 woman) in the Covilhã, and Fundão municipalities (Portugal). The data related to the different sensors was acquired with a sampling rate of 100 Hz by the accelerometer, 50 Hz by the magnetometer, and 100 Hz by the gyroscope sensors. It includes the captures related to a minimum of 2000 captures for each ADL, which corresponds to 2.8 h (approximately) for each ADL. In total, this dataset includes 13.9 h (approximately) of captures. These data can be reused for the implementation of data processing techniques, and artificial intelligence methods.
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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.