ReviewSensors (Basel, Switzerland)2024
Machine Learning Techniques for Sensor-Based Human Activity Recognition with Data Heterogeneity-A Review.
Review in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Deployment-Oriented Evaluation of Lightweight IMU-Based Human Activity Recognition: On-Device Efficiency and Streaming Feasibility.Sensors (Basel, Switzerland) · 2026Article
- AviaTAD-LGH: A Multi-Task Spatio-Temporal Action Detector with Lightweight Gradient Harmonization for Real-Time Avian Behavior Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Advances in flexible wearable pressure/strain sensors for motion monitoring in orthopaedic sports medicine.Journal of orthopaedic translation · 2026Review
- A Selective RAG-Enhanced Hybrid ML-LLM Framework for Efficient and Explainable Fatigue Prediction Using Wearable Sensor Data.Bioengineering (Basel, Switzerland) · 2026Article
- Multimodal healthcare system for human activity recognition using multiple features and advanced ensemble classifier.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous computing, analyzing behaviors through multi-dimensional observations. Despite research progress, HAR confronts challenges, particularly in data distribution assumptions. Most studies assume uniform data distributions across datasets, contrasting with the varied nature of practical sensor data in human activities. Addressing data heterogeneity issues can improve performance, reduce computational costs, and aid in developing personalized, adaptive models with fewer annotated data. This review investigates how machine learning addresses data heterogeneity in HAR by categorizing data heterogeneity types, applying corresponding suitable machine learning methods, summarizing available datasets, and discussing future challenges.
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What OpenQuestion holds
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.