ArticleSensors (Basel, Switzerland)2023
From Lab to Real World: Assessing the Effectiveness of Human Activity Recognition and Optimization through Personalization.
Article in Sensors (Basel, Switzerland), 2023. 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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Who cites it
8 citing papers in PubMed.
- A Comparison of Machine Learning Approaches to Activity Classification Using IMU Data Collected in a Community-Based Setting.Sensors (Basel, Switzerland) · 2026Article
- Transfer and Reinforcement Learning as Support Paradigms for Human Activity Recognition in Indoor Environments: A Comprehensive Analysis of Trends, Impact and Future Directions.Sensors (Basel, Switzerland) · 2026Review
- A Context-Assisted, Semi-Automated Activity Recall Interface Allowing Uncertainty.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2025Article
- The Physical Activity Assessment Using Wearable Sensors (PAAWS) Dataset: Labeled Laboratory and Free-living Accelerometer Data.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2025Article
- Human Activity Recognition via Attention-Augmented TCN-BiGRU Fusion.Sensors (Basel, Switzerland) · 2025Article
- A Multi-Agent LLM Network for Suggesting and Correcting Human Activity and Posture Annotations.Proceedings of the ... ACM International Conference on Ubiquitous Computing . UbiComp (Conference) · 2025Article
- Unlocking the potential of smartphone and ambient sensors for ADL detection.Scientific reports · 2024Article
- Designing a just-in-time adaptive intervention with trigger detection and a generative chatbot: Smoking cessation use case.Digital healthArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human activity recognition (HAR) algorithms today are designed and evaluated on data collected in controlled settings, providing limited insights into their performance in real-world situations with noisy and missing sensor data and natural human activities. We present a real-world HAR open dataset compiled from a wristband equipped with a triaxial accelerometer. During data collection, participants had autonomy in their daily life activities, and the process remained unobserved and uncontrolled. A general convolutional neural network model was trained on this dataset, achieving a mean balanced accuracy (MBA) of 80%. Personalizing the general model through transfer learning can yield comparable and even superior results using fewer data, with the MBA improving to 85%. To emphasize the issue of insufficient real-world training data, we conducted training of the model using the public MHEALTH dataset, resulting in 100% MBA. However, upon evaluating the MHEALTH-trained model on our real-world dataset, the MBA drops to 62%. After personalizing the model with real-world data, an improvement of 17% in the MBA is achieved. This paper showcases the potential of transfer learning to make HAR models trained in different contexts (lab vs. real-world) and on different participants perform well for new individuals with limited real-world labeled data available.
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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.