sensor-based abnormal human-activity detection authors: jie yin, qiang yang, and jeffrey junfeng pan...
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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
• 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
• 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