ArticleSensors (Basel, Switzerland)2020
A Public Domain Dataset for Real-Life Human Activity Recognition Using Smartphone Sensors.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- STAR: A Privacy-Preserving, Energy-Efficient Edge AI Framework for Human Activity Recognition via Wi-Fi CSI in Mobile and Pervasive Computing Environments.Sensors (Basel, Switzerland) · 2026Article
- Methods for classifying physical activities using accelerometer data: a scoping review.NPJ digital medicine · 2026Article
- An open-source, externally validated neural network algorithm to recognize daily-life gait of older adults based on a lower-back sensor.Medical & biological engineering & computing · 2026Article
- Human Activity Recognition Dataset for Pedestrians with Mobility Disabilities.Scientific data · 2026Article
- An enhanced dual inception-attention-BiGRU-attention model integrating wavelet transform for wearable sensor-based human activity recognition.Scientific reports · 2025Article
- Spike train analysis in rehabilitation movement classification using deep learning approach.Scientific reports · 2025Article
- Sensor-based evaluation of intermittent fasting regimes: a machine learning and statistical approach.International journal of obesity (2005) · 2025Article
- A Comprehensive Dataset for Activity of Daily Living (ADL) Research Compiled by Unifying and Processing Multiple Data Sources.Journal of personalized medicine · 2025Article
- The application of suitable sports games for junior high school students based on deep learning and artificial intelligence.Scientific reports · 2025Article
- Data Reconstruction Methods in Multi-Feature Fusion CNN Model for Enhanced Human Activity Recognition.Sensors (Basel, Switzerland) · 2025Article
- Time-Series Representation Feature Refinement with a Learnable Masking Augmentation Framework in Contrastive Learning.Sensors (Basel, Switzerland) · 2024Article
- A benchmark for domain adaptation and generalization in smartphone-based human activity recognition.Scientific data · 2024Article
- Achieving More with Less: A Lightweight Deep Learning Solution for Advanced Human Activity Recognition (HAR).Sensors (Basel, Switzerland) · 2024Article
- Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization.Sensors (Basel, Switzerland) · 2024Article
- Unlocking the potential of smartphone and ambient sensors for ADL detection.Scientific reports · 2024Article
- A Wearable Inertial Sensor Approach for Locomotion and Localization Recognition on Physical Activity.Sensors (Basel, Switzerland) · 2024Article
- A hybrid TCN-GRU model for classifying human activities using smartphone inertial signals.PloS one · 2024Article
- Smartphone-based activity tracking for spine patients: Current technology and future opportunities.World neurosurgery: X · 2024Review
- A New Index for the Quantitative Evaluation of Surgical Invasiveness Based on Perioperative Patients' Behavior Patterns: Machine Learning Approach Using Triaxial Acceleration.JMIR perioperative medicine · 2023Article
- Deep Learning in the Recognition of Activities of Daily Living Using Smartwatch Data.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
Abstract
In recent years, human activity recognition has become a hot topic inside the scientific community. The reason to be under the spotlight is its direct application in multiple domains, like healthcare or fitness. Additionally, the current worldwide use of smartphones makes it particularly easy to get this kind of data from people in a non-intrusive and cheaper way, without the need for other wearables. In this paper, we introduce our orientation-independent, placement-independent and subject-independent human activity recognition dataset. The information in this dataset is the measurements from the accelerometer, gyroscope, magnetometer, and GPS of the smartphone. Additionally, each measure is associated with one of the four possible registered activities: inactive, active, walking and driving. This work also proposes asupport vector machine (SVM) model to perform some preliminary experiments on the dataset. Considering that this dataset was taken from smartphones in their actual use, unlike other datasets, the development of a good model on such data is an open problem and a challenge for researchers. By doing so, we would be able to close the gap between the model and a real-life application.
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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.