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