TY - GEN
T1 - Analysis of low resolution accelerometer data for continuous human activty recognition
AU - Krishnan, Narayanan C.
AU - Panchanathan, Sethuraman
PY - 2008/9/16
Y1 - 2008/9/16
N2 - The advent of wearable sensors like accelerometers has opened a plethora of opportunities to recognize human activities from other low resolution sensory streams. In this paper we formulate recognizing activities from accelerometer data as a classification problem. In addition to the statistical and spectral features extracted from the acceleration data, we propose to extract features that characterize the variations in the first order derivative of the acceleration signal. We evaluate the performance of different state of the art discriminative classifiers like, boosted decision stumps (AdaBoost), support vector machines (SVM) and Regularized Logistic Regression(RLogReg) under three different evaluation scenarios(namely Subject Independent, Subject Adaptive and Subject Dependent). We propose a novel computationally inexpensive methodology for incorporating smoothing classification temporally, that can be coupled with any classifier with minimal training for classifying continuous sequences. While a 3% increase in the classification accuracy was observed on adding the new features, the proposed technique for continuous recognition showed a 2.5 - 3% improvement in the performance.
AB - The advent of wearable sensors like accelerometers has opened a plethora of opportunities to recognize human activities from other low resolution sensory streams. In this paper we formulate recognizing activities from accelerometer data as a classification problem. In addition to the statistical and spectral features extracted from the acceleration data, we propose to extract features that characterize the variations in the first order derivative of the acceleration signal. We evaluate the performance of different state of the art discriminative classifiers like, boosted decision stumps (AdaBoost), support vector machines (SVM) and Regularized Logistic Regression(RLogReg) under three different evaluation scenarios(namely Subject Independent, Subject Adaptive and Subject Dependent). We propose a novel computationally inexpensive methodology for incorporating smoothing classification temporally, that can be coupled with any classifier with minimal training for classifying continuous sequences. While a 3% increase in the classification accuracy was observed on adding the new features, the proposed technique for continuous recognition showed a 2.5 - 3% improvement in the performance.
KW - Accelerometers
KW - AdaBoost
KW - Human activity recognition
KW - SVM
UR - http://www.scopus.com/inward/record.url?scp=51449085444&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=51449085444&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2008.4518365
DO - 10.1109/ICASSP.2008.4518365
M3 - Conference contribution
AN - SCOPUS:51449085444
SN - 1424414849
SN - 9781424414840
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3337
EP - 3340
BT - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
T2 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Y2 - 31 March 2008 through 4 April 2008
ER -