Accelerometer-based gesture recognition using dynamic time warping and sparse representation

In this paper, we propose a new accelerometer-based gesture recognition system. In this system, the start and end of the data collection process is automatically determined by acceleration waveform. In pretreatment phase, we propose a waveform compensation algorithm to solve the problems caused by t...

Full description

Saved in:
Bibliographic Details
Published inMultimedia tools and applications Vol. 75; no. 14; pp. 8637 - 8655
Main Authors Wang, Haiying, Li, Zhengshan
Format Journal Article
LanguageEnglish
Published New York Springer US 01.07.2016
Springer Nature B.V
Subjects
Accelerometers
Accuracy
Algorithms
Cellular telephones
Classification
Computer Communication Networks
Computer Science
Coordinate transformations
Customization
Data collection
Data Structures and Information Theory
Decision trees
Dictionaries
Dynamical systems
Dynamics
Fuzzy logic
Human-computer interaction
Methods
Multimedia Information Systems
Neural networks
R&D
Recognition
Representations
Research & development
Sensors
Sparsity
Special Purpose and Application-Based Systems
User interface
Waveforms
Accelerometer
Sparse representation
Gesture recognition
Matching pursuit
Online AccessGet full text

Cover

More Information
Summary:In this paper, we propose a new accelerometer-based gesture recognition system. In this system, the start and end of the data collection process is automatically determined by acceleration waveform. In pretreatment phase, we propose a waveform compensation algorithm to solve the problems caused by the amplitude range of the accelerometer and use the coordinate transformation theory to alleviate the angle offset. In training phase, we use dynamic time warping (DTW) and affinity propagation (AP) to extract clusters and exemplars. We implement sparse representation for gesture recognition and propose a modified variable sparsity adaptive matching pursuit (MVSAMP) algorithm for signal reconstruction. This algorithm is more adapted to the characteristics of gesture recognition. In the classification stage, a method of weighted residuals is applied to improve the resolution of the best classification. To test the system’s performance, a dictionary of 10 gestures is defined and a database consists of 3800 samples is created from 14 participants. Test results have shown that the proposed system achieves a good performance in a variety of experiments on Android platform.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-015-2775-2