1
|
Veer K, Vig R. Identification and classification of upper limb motions using PCA. ACTA ACUST UNITED AC 2017; 63:191-196. [DOI: 10.1515/bmt-2016-0224] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Accepted: 01/30/2017] [Indexed: 11/15/2022]
Abstract
Abstract:
This paper describes the utility of principal component analysis (PCA) in classifying upper limb signals. PCA is a powerful tool for analyzing data of high dimension. Here, two different input strategies were explored. The first method uses upper arm dual-position-based myoelectric signal acquisition and the other solely uses PCA for classifying surface electromyogram (SEMG) signals. SEMG data from the biceps and the triceps brachii muscles and four independent muscle activities of the upper arm were measured in seven subjects (total dataset=56). The datasets used for the analysis are rotated by class-specific principal component matrices to decorrelate the measured data prior to feature extraction.
Collapse
Affiliation(s)
- Karan Veer
- Electronics and Communication Engineering Department , Panjab University , Chandigarh , India
| | - Renu Vig
- Electronics and Communication Engineering Department , Panjab University , Chandigarh , India
| |
Collapse
|