Classification of physical activities based on sparse representation
- Resource Type
- Conference
- Authors
- Liu, Shaopeng; Gao, Robert X.; John, Dinesh; Staudenmayer, John; Freedson, Patty S.
- Source
- 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE. :6200-6203 Aug, 2012
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Signal Processing and Analysis
Training
Feature extraction
Classification algorithms
Vectors
Sparse matrices
Accuracy
Legged locomotion
- Language
- ISSN
- 1557-170X
1094-687X
1558-4615
This paper presents a new classification method for physical activity assessment, based on sparse representation. This method bypasses the need for feature extraction and selection that is typically involved for activity classification, and classifies activities using raw sensor signals directly. Higher discriminative power than that from the conventional k-nearest neighbor algorithm has been demonstrated through experiments performed on 105 subjects.