ArticleSensors (Basel, Switzerland)2021
Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer 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 18 papers.
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Who cites it
18 citing papers in PubMed.
- 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
- Application of Machine Learning Approach to Classify Human Activity Level Based on Lifelog Data.Sensors (Basel, Switzerland) · 2026Article
- A survey of machine learning for estimating workload: considering unknown tasks.Frontiers in robotics and AI · 2026Review
- Towards User-Generalizable Wearable-Sensor-Based Human Activity Recognition: A Multi-Task Contrastive Learning Approach.Sensors (Basel, Switzerland) · 2025Article
- Machine Learning Techniques for Sensor-Based Human Activity Recognition with Data Heterogeneity-A Review.Sensors (Basel, Switzerland) · 2024Review
- Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review.Sensors (Basel, Switzerland) · 2023Review
- Real-Time Human Activity Recognition with IMU and Encoder Sensors in Wearable Exoskeleton Robot via Deep Learning Networks.Sensors (Basel, Switzerland) · 2022Article
- A Comprehensive "Real-World Constraints"-Aware Requirements Engineering Related Assessment and a Critical State-of-the-Art Review of the Monitoring of Humans in Bed.Sensors (Basel, Switzerland) · 2022Review
- Personalised Gait Recognition for People with Neurological Conditions.Sensors (Basel, Switzerland) · 2022Article
- Human Activity Recognition Data Analysis: History, Evolutions, and New Trends.Sensors (Basel, Switzerland) · 2022Review
- Mathematical Criteria for a Priori Performance Estimation of Activities of Daily Living Recognition.Sensors (Basel, Switzerland) · 2022Article
- Online Activity Recognition Combining Dynamic Segmentation and Emergent Modeling.Sensors (Basel, Switzerland) · 2022Article
- Emotion recognition based on multi-modal physiological signals and transfer learning.Frontiers in neuroscience · 2022Article
- Machine Learning for Healthcare Wearable Devices: The Big Picture.Journal of healthcare engineering · 2022Review
- Feature Fusion of a Deep-Learning Algorithm into Wearable Sensor Devices for Human Activity Recognition.Sensors (Basel, Switzerland) · 2021Article
- An Information Gain-Based Model and an Attention-Based RNN for Wearable Human Activity Recognition.Entropy (Basel, Switzerland) · 2021Article
- From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning.Sensors (Basel, Switzerland) · 2021Article
- LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Human activity recognition (HAR) based on the wearable device has attracted more attention from researchers with sensor technology development in recent years. However, personalized HAR requires high accuracy of recognition, while maintaining the model's generalization capability is a major challenge in this field. This paper designed a compact wireless wearable sensor node, which combines an air pressure sensor and inertial measurement unit (IMU) to provide multi-modal information for HAR model training. To solve personalized recognition of user activities, we propose a new transfer learning algorithm, which is a joint probability domain adaptive method with improved pseudo-labels (IPL-JPDA). This method adds the improved pseudo-label strategy to the JPDA algorithm to avoid cumulative errors due to inaccurate initial pseudo-labels. In order to verify our equipment and method, we use the newly designed sensor node to collect seven daily activities of 7 subjects. Nine different HAR models are trained by traditional machine learning and transfer learning methods. The experimental results show that the multi-modal data improve the accuracy of the HAR system. The IPL-JPDA algorithm proposed in this paper has the best performance among five HAR models, and the average recognition accuracy of different subjects is 93.2%.
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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.