TY - GEN
T1 - Recognizing short duration hand movements from accelerometer data
AU - Krishnan, Narayanan C.
AU - Pradhan, Gaurav N.
AU - Panchanathan, Sethuraman
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Accelerometer
KW - Feature extraction
KW - Histograms
UR - http://www.scopus.com/inward/record.url?scp=70449623344&partnerID=8YFLogxK
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U2 - 10.1109/ICME.2009.5202848
DO - 10.1109/ICME.2009.5202848
M3 - Conference contribution
AN - SCOPUS:70449623344
SN - 9781424442911
T3 - Proceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009
SP - 1700
EP - 1703
BT - Proceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009
T2 - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009
Y2 - 28 June 2009 through 3 July 2009
ER -