Optimization-Based Domain Adaptation towards Person-Adaptive Classification Models

The emergence of inexpensive and unobtrusive physiological sensors has widened their application to newer and innovative areas including proactive health monitoring, smart environments and novel human-computer interfaces. The inherent variability in physiological signals across subjects poses a great challenge to traditional machine learning algorithms which are used to develop generalized classification frameworks. In this paper, we propose an optimization-based domain adaptation (ODA) methodology which can provide reliable classification on a given test subject, using the available data from other subjects. The proposed ODA method selects instances from the source domain (data available from other subjects) based on a novel optimization formulation, to ensure that the selected instances are similar in distribution to the target domain (test subject data) in both marginal and conditional probability distributions. We validated the proposed framework on Surface Electromyogram (SEMG) signals collected from 8 people during a fatigue-causing repetitive gripping activity, to detect different stages of fatigue. Comprehensive experiments on our SEMG data set demonstrated that the proposed method improves the classification accuracy by 19% to 21% over traditional classification models, and by 12% to 18% over existing state-of-the-art domain adaptation methodologies.

[1]  Ivor W. Tsang,et al.  Domain Transfer SVM for video concept detection , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Hal Daumé,et al.  Frustratingly Easy Domain Adaptation , 2007, ACL.

[3]  Steffen Bickel,et al.  Discriminative Learning Under Covariate Shift , 2009, J. Mach. Learn. Res..

[4]  Deepak S. Turaga,et al.  Cross domain distribution adaptation via kernel mapping , 2009, KDD.

[5]  Apostolos Georgakis,et al.  Fatigue analysis of the surface EMG signal in isometric constant force contractions using the averaged instantaneous frequency , 2003, IEEE Transactions on Biomedical Engineering.

[6]  S Karlsson,et al.  Criterion validation of surface EMG variables as fatigue indicators using peak torque: a study of repetitive maximum isokinetic knee extensions. , 2000, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[7]  Ingo Steinwart,et al.  On the Influence of the Kernel on the Consistency of Support Vector Machines , 2002, J. Mach. Learn. Res..

[8]  D K Smith,et al.  Numerical Optimization , 2001, J. Oper. Res. Soc..

[9]  Qiang Yang,et al.  Transfer Learning via Dimensionality Reduction , 2008, AAAI.

[10]  Ivor W. Tsang,et al.  Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.

[11]  Graham Clarke,et al.  A user-independent real-time emotion recognition system for software agents in domestic environments , 2007, Eng. Appl. Artif. Intell..

[12]  James J. Jiang A Literature Survey on Domain Adaptation of Statistical Classifiers , 2007 .

[13]  Bernhard Schölkopf,et al.  Correcting Sample Selection Bias by Unlabeled Data , 2006, NIPS.

[14]  John Blitzer,et al.  Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification , 2007, ACL.

[15]  Elisabeth André,et al.  Emotion recognition based on physiological changes in music listening , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Motoaki Kawanabe,et al.  Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation , 2007, NIPS.

[17]  Ivor W. Tsang,et al.  Domain adaptation from multiple sources via auxiliary classifiers , 2009, ICML '09.

[18]  Dinesh Kant Kumar,et al.  Wavelet analysis of surface electromyography , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[19]  Hans-Peter Kriegel,et al.  Integrating structured biological data by Kernel Maximum Mean Discrepancy , 2006, ISMB.

[20]  H. Shimodaira,et al.  Improving predictive inference under covariate shift by weighting the log-likelihood function , 2000 .

[21]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[22]  Jiawei Han,et al.  Knowledge transfer via multiple model local structure mapping , 2008, KDD.