Human Activity Recognition with Wearable Sensors Minh Nguyen, Liyue Fan, Cyrus Shahabi Integrated Media Systems Center University of Southern California Introduction Classifier Method §  Motivation: + The exceptional development of wearable sensors/devices + Human interaction with the devices as part of daily living + Human activity data analysis + Useful healthcare services §  Devices’ Accelerometers & Machine Learning algorithms to recognize locomotion type §  Providing users with human performance status Decision Tree [2] Architecture + Decision Tree (C4.5, ID3) + Root 1: Input (extracted features) + Node 1, 2, 3, 4, 5, 6: Based on the value of the features, the nodes decide which activity is labelled Communication Integration Storage Data Collection Human Activity Data Signals Data Preprocessing Feature Extraction Classifier Recognition Result Data Segmentation Mean, Standard Deviation, Energy, etc. Decision Tree, NB, kNN, etc. Walk, sit, stand, lie, etc. K-nearest Neighbors + Calculating distances between points + Finding k nearest feature points of feature points § Data Signals [1] Sensor Naïve Bayesian Acceleration 3-axis accelerometer Environmental Attribute Light, temperature, noise, location Physiological Signal Heart rate, respiration rate, galvanic skin response + Activity A - Features Xi + Independence assumption between features Support Vector Machine + SVM finds the hyperplane that maximizes the margin between the data points Other Methods + Neural Networks, HMM, Fuzzy Basis Function Conclusion & Future Work n Evaluation for each classification method: Activity Confusion/Overall Accuracy n Application for Healthcare Informatics Related Research §  [1] O. D. Lara and M. A. Labrador. A survey on human activity recognition using wearable sensors. IEEE Communications Surveys & Tutorials 15(3), pp.1192-1209. 2013; 2012.DOI: 10.1109/SURV.2012.110112. 00192. §  [2] J. Parkka, J. Parkka, M. Ermes, M. Ermes, P. Korpipaa, J. Mantyjarvi, J. Peltola and I. Korhonen. Activity classification using realistic data from wearable sensors. IEEE Transactions on Information Technology in Biomedicine 10(1), pp. 119-128. 2006. . DOI: 10.1109/TITB.2005.856863. IMSC Retreat 2015