Sensor-Based Abnormal Human-Activity Detection Authors: Jie Yin, Qiang Yang, and Jeffrey Junfeng Pan Presenter: Raghu Rangan

Need to be able to track and monitor user activities Detect abnormal activities  Very useful in security (anti-terrorism)  Healthcare for the elderly Need to develop algorithm to track movements of individuals and determine if they are out of the norm Problem

Abnormal activities are events  Occur rarely  Have not been expected in advance Need to keep false positives/negatives down to a minimum Data is extremely scarce Abnormal Activity Detection

Several approaches to the abnormality problem  Computer vision area Using Markov models to detect out-of-norm behavior Problem: cameras are sensitive to lighting and area, plus privacy concerns  Wearable sensors Unintrusive and user can be monitored continuously Deployment and computational challenges Related Work

First approach: use easily understandable rules to describe human behavior  Provides mechanism to capture abnormal rules too (exceptional rules)  Complementary to probabilistic model based approach Second approach: use template-based plan recognition  Compile set of typical patterns using logical frameworks in AI planning and match patterns to observed actions Related Work: Activity Recognition

Other approaches:  Hidden Markov models, Dynamic Bayesian Networks Employ supervised learning to recognize normal activities Need large amount of training data  Problematic for abnormality detection Related Work: Activity Recognition

Similarity-based approach  Define pairwise distances between all data points and identify outliers by looking at distances  Advantage: no explicit distribution needed  Problem: how to define effective similarity measures when there is high uncertainty Model-based approach  Use predictive models, detect outliers as deviations from learned model  One model is one-class SVMs Related Work: Outlier Detection

More normal data than abnormal data Use cost-sensitive learning  “addresses the issue of classification in the presence of different misclassification costs”  Set false positive/negative costs differently to balance the total cost  Use receiver operating characteristic (ROC) curve to evaluate approach Related Work: Unbalanced Data

Two phase approach  First phase: build one-class SVM based on normal activities Filter out activities with a high probability of being normal Pass suspicious traces to secondary phase  Second phase: Perform Kernel Nonlinear Regression analysis to derive abnormal activity model Proposed Algorithm

Flow Diagram of Algorithm

One Class SVM

Iterative procedure to create abnormal activity models Once outlier is detected beyond threshold, KNLR performed to generate model Repeated for more outliers to generate better abnormal model Iterative Adaptation Procedure

Various adaptation techniques to generate models  Maximum likelihood linear regression (MLLR) Attempts to compute transformations to reduce mismatch between initial model and adaptation data Can only perform linear transformations  Kernel Nonlinear Regression (KNLR) Nonlinear generalization of MLLR Maps linear regression transformations to a high- dimensional feature space via nonlinear kernel map KNLR Adaptation

Attach sensor boards to various parts of the body Evaluate the performance of the algorithm by comparing it to others  OneSVM – one class SVM for abnormality detection  SVN+MLLR  SVN+KNLR (proposed method) Experimental Setup

Results: ROC ROC curve with 216 traces ROC curve with 108 traces

Area Under the Curve Table

Achieved a better tradeoff between detection rate and false alarm rate Potential problem of generating a lot of abnormal models  When abnormal activities become normal In the future  Detect abnormal activities from continuous traces Conclusion and Future Work

Questions/Comments/Discussion