Physical Activity Recognition From Smartphone Accelerometer Data for User Context Awareness Sensing
- 23 May 2016
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Systems, Man, and Cybernetics: Systems
- Vol. 47 (12), 3142-3149
- https://doi.org/10.1109/tsmc.2016.2562509
Abstract
Physical activity recognition of everyday activities such as sitting, standing, laying, walking, and jogging was performed, through the use of smartphone accelerometer data. Activity classification was done on a remote server through the use of machine learning algorithms, data was received from the smartphone wirelessly. The smartphone was placed in the subject's trouser pocket while data was gathered. A large sample set was used to train the classifiers and then a test set was used to verify the algorithm accuracies. Ten different classifier algorithm configurations were evaluated to determine which performed best overall, as well as, which algorithms performed best for specific activity classes. Based on the results obtained, very accurate predictions could be made for offline activity recognition. The kNN and kStar algorithms both obtained an overall accuracy of 99.01%.Keywords
Funding Information
- Research Development Programme (AOX220)
- National Research Foundation (90908)
This publication has 15 references indexed in Scilit:
- A Study on Human Activity Recognition Using Accelerometer Data from SmartphonesProcedia Computer Science, 2014
- Activity classification using a single chest mounted tri-axial accelerometerMedical Engineering & Physics, 2011
- Identifying Types of Physical Activity With a Single Accelerometer: Evaluating Laboratory-trained Algorithms in Daily LifeIEEE Transactions on Biomedical Engineering, 2011
- Activity recognition using cell phone accelerometersACM SIGKDD Explorations Newsletter, 2011
- A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity MonitoringSensors, 2010
- Activity recognition from acceleration data based on discrete consine transform and SVMPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- Evaluation of a threshold-based tri-axial accelerometer fall detection algorithmGait & Posture, 2007
- Implementation of a Real-Time Human Movement Classifier Using a Triaxial Accelerometer for Ambulatory MonitoringIEEE Transactions on Information Technology in Biomedicine, 2006
- Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderlyIEEE Transactions on Biomedical Engineering, 2003
- Standing balance evaluation using a triaxial accelerometerGait & Posture, 2001