ArticleBiomedical engineering online2015
Identifying typical physical activity on smartphone with varying positions and orientations.
Article in Biomedical engineering online, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Human Activity Recognition Dataset for Pedestrians with Mobility Disabilities.Scientific data · 2026Article
- LightGBM-Based Human Action Recognition Using Sensors.Sensors (Basel, Switzerland) · 2025Article
- X-CHAR: A Concept-based Explainable Complex Human Activity Recognition Model.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2023Article
- Ensemble of RNN Classifiers for Activity Detection Using a Smartphone and Supporting Nodes.Sensors (Basel, Switzerland) · 2022Article
- Real-time prediction of smoking activity using machine learning based multi-class classification model.Multimedia tools and applications · 2022Article
- The Moderating Effect of COVID-19 Risk Perception on the Relationship Between Empathy and COVID-19 Volunteer Behavior: A Cross-Sectional Study in Jiangsu, China.Frontiers in public health · 2022Article
- A systematic review of smartphone-based human activity recognition methods for health research.NPJ digital medicine · 2021Review
- Scoping Review of Healthcare Literature on Mobile, Wearable, and Textile Sensing Technology for Continuous Monitoring.Journal of healthcare informatics research · 2021Article
- Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition.Sensors (Basel, Switzerland) · 2020Article
- Human activity monitoring system based on wearable sEMG and accelerometer wireless sensor nodes.Biomedical engineering online · 2018Article
- Classifier Level Fusion of Accelerometer and sEMG Signals for Automatic Fitness Activity Diarization.Sensors (Basel, Switzerland) · 2018Article
- A Wearable Context-Aware ECG Monitoring System Integrated with Built-in Kinematic Sensors of the Smartphone.Sensors (Basel, Switzerland) · 2015Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTraditional activity recognition solutions are not widely applicable due to a high cost and inconvenience to use with numerous sensors. This paper aims to automatically recognize physical activity with the help of the built-in sensors of the widespread smartphone without any limitation of firm attachment to the human body.
methodsBy introducing a method to judge whether the phone is in a pocket, we investigated the data collected from six positions of seven subjects, chose five signals that are insensitive to orientation for activity classification. Decision trees (J48), Naive Bayes and Sequential minimal optimization (SMO) were employed to recognize five activities: static, walking, running, walking upstairs and walking downstairs.
resultsThe experimental results based on 8,097 activity data demonstrated that the J48 classifier produced the best performance with an average recognition accuracy of 89.6% during the three classifiers, and thus would serve as the optimal online classifier.
conclusionsThe utilization of the built-in sensors of the smartphone to recognize typical physical activities without any limitation of firm attachment is feasible.
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