ArticleSensors (Basel, Switzerland)2021
HARTH: A Human Activity Recognition Dataset for Machine Learning.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Movement Behaviors in Youth on the Autism Spectrum: The HUNT Study, Norway.Journal of autism and developmental disorders · 2026Article
- Robust Assessment of Free-Living Physical Behaviors and Activity Intensity Using Dual-Wearable Multitask Learning: Development and Evaluation Study From the Multicenter WEALTH Project.JMIR mHealth and uHealth · 2026Article
- Strength of associations of psoriatic arthritis and physical activity with body composition: the population-based Trøndelag Health study.Clinical rheumatology · 2026Article
- Data Resource Profile: Device-measured physical activity and sleep in the HUNT4 study, Norway.International journal of epidemiology · 2026Article
- Cohort Profile Update: The Finnish Gestational Diabetes (FinnGeDi) study.International journal of epidemiology · 2026Article
- Neural Network-Based Granular Activity Recognition from Accelerometers: Assessing Generalizability Across Diverse Mobility Profiles.Sensors (Basel, Switzerland) · 2026Article
- Distribution of device-measured 24-h movement behaviors in older adults: cross-sectional findings from the HUNT4 study.Scientific reports · 2026Article
- Towards Practical, Best Practice Video Annotation to Support Human Activity Recognition.Annotation of real-world data for artificial intelligence systems : 9th international workshop, ARDUOUS 2025, Bologna Italy, October 25-26, 2025, proceedings. International Workshop on Annotation of Real-World Data for Artificial Intell... · 2026Article
- Article
- Automated identification of Shankha Prakshalana yoga poses with machine learning techniques.Scientific reports · 2025Article
- Assessment of gait speed in older adults using a short-distance walking test with smartphone motion sensors and machine learning.Scientific reports · 2025Article
- Validation and comparative study of the Motus system for accurately identifying movement behaviours using different sampling frequencies.Scientific reports · 2025Article
- Rehearsal-Free Continual Learning for Emerging Unsafe Behavior Recognition in Construction Industry.Sensors (Basel, Switzerland) · 2025Article
- Device measured physical activity before pregnancy and the risk of adverse pregnancy outcomes in the HUNT study: a population-based cohort study.BMC pregnancy and childbirth · 2025Article
- Volume and Intensity of Walking and Risk of Chronic Low Back Pain.JAMA network open · 2025Article
- Associations of sport and exercise participation in adolescence with body composition and device-measured physical activity in adulthood: longitudinal data from the Norwegian HUNT study.The international journal of behavioral nutrition and physical activity · 2025Article
- Federated Learning for IoMT-Enhanced Human Activity Recognition with Hybrid LSTM-GRU Networks.Sensors (Basel, Switzerland) · 2025Article
- The performance of a machine learning model in predicting accelerometer-derived walking speed.Heliyon · 2025Article
- Enhancing healthcare data privacy and interoperability with federated learning.PeerJ. Computer science · 2025Article
- Contribution of walking, running and cycling to moderate-to-vigorous and total physical activity in adolescents and adults across and within seasons: cross-sectional data from the Norwegian HUNT Study.BMJ open sport & exercise medicine · 2025Article
Corrections and comments
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Existing accelerometer-based human activity recognition (HAR) benchmark datasets that were recorded during free living suffer from non-fixed sensor placement, the usage of only one sensor, and unreliable annotations. We make two contributions in this work. First, we present the publicly available Human Activity Recognition Trondheim dataset (HARTH). Twenty-two participants were recorded for 90 to 120 min during their regular working hours using two three-axial accelerometers, attached to the thigh and lower back, and a chest-mounted camera. Experts annotated the data independently using the camera's video signal and achieved high inter-rater agreement (Fleiss' Kappa =0.96). They labeled twelve activities. The second contribution of this paper is the training of seven different baseline machine learning models for HAR on our dataset. We used a support vector machine, k-nearest neighbor, random forest, extreme gradient boost, convolutional neural network, bidirectional long short-term memory, and convolutional neural network with multi-resolution blocks. The support vector machine achieved the best results with an F1-score of 0.81 (standard deviation: ±0.18), recall of 0.85±0.13, and precision of 0.79±0.22 in a leave-one-subject-out cross-validation. Our highly professional recordings and annotations provide a promising benchmark dataset for researchers to develop innovative machine learning approaches for precise HAR in free living.
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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.