Recognizing short duration hand movements from accelerometer data

Authors:
Narayanan C. Krishnan;Gaurav N. Pradhan;Sethuraman Panchanathan
Affiliations:
Center for Cognitive Ubiquitous Computing, Department of Computer Science and Engineering, Arizona State University, Tempe, AZ;Center for Cognitive Ubiquitous Computing, Department of Computer Science and Engineering, Arizona State University, Tempe, AZ;Center for Cognitive Ubiquitous Computing, Department of Computer Science and Engineering, Arizona State University, Tempe, AZ
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

Citing 6
Cited 0

Analyzing features for activity recognition

Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies
Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers

IEEE Transactions on Pattern Analysis and Machine Intelligence
Gesture spotting with body-worn inertial sensors to detect user activities

Pattern Recognition
Recognition of hand movements using wearable accelerometers

Journal of Ambient Intelligence and Smart Environments
Toward recognition of short and non-repetitive activities from wearable sensors

AmI'07 Proceedings of the 2007 European conference on Ambient intelligence
A practical approach to recognizing physical activities

PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Processing of accelerometer data for recognizing short duration hand movements is a challenging problem. This paper focuses on characterization of acceleration data corresponding to hand movements (lift to mouth, scoop, stir, pour, unscrew cap) using aggregate statistical features and histograms computed from raw acceleration and derivative of the acceleration data. Data collected from an accelerometer placed on the wrist of subjects was used to perform the analysis. Supplementing the statistical features with raw acceleration histograms had a very marginal effect on the classification performance. However, the addition of derivative histograms resulted in a considerable improvement in the classification accuracy by nearly 8%. The effect of bin size of the derivative histograms was also conducted. It was observed that having a small number of bins decreased the classification accuracy by 3%. We thus show that adding features that capture the distribution of the changes in the acceleration data improve the classification performance.