MAMS: A Mobile Application to Detect Abnormal Patterns of Activity

1. MAMS: A Mobile Application to Detect Abnormal Patterns of Activity Omar Abdul Baki Ying Zhang Martin Griss Hsiuping Lin CyLab Mobility Research Center Mobility Research Center Carnegie Mellon Silicon Valley

2. Agenda • Introduction to Anomaly Detection • Related Work • MAMS • Experiments • Results and Analysis • Conclusion and Future Work

3. What is an anomalous incident? • In the context of anomaly detection, it is an activity which isn’t part of an individual’s regular routine.

4. Anomalous Activity Detection in the Real World • Detecting anomalous incidents amongst seniors in real time • To notify caretakers sooner when something serious occurs. • To reduce the stay-at-home costs for seniors. • Securing Mobile Devices • Providing an added level of security into Mobile Devices • Making sense of changes in daily patterns of activity • Uncovering changes in quality of life amongst seniors • To detect the onset of certain medical conditions (i.e Alzheimer's)

5. Problems • Most implementations use supervised learning approaches • May not detect events outside the learned scope • Some learned activities are user dependent and don’t generalize well to other users. • Enumerating and training a thorough set of learned activities is difficult and time consuming • Such implementations are impractical since they would require an active end-user for training. • Most implementations fail to account for location of events • May generate false alerts for events which are location dependent

6. Related Work • Unsupervised, Dynamic Identification of Physiological and Activity Context in Wearable Computing, Krause et. al., 2000. • Towards Recognizing Abstract Activities: An Unsupervised Approach, Hein A., Kirste T., 2008. • Unsupervised Clustering of Free-Living Human Activities using Ambulatory Accelerometry, Nguyen et al., 2007.

7. MAMS Overview • Developed on Nokia N95 platform using Mobile Python. • MAMS uses 3 features to classify atomic activities. • Location • Based on REDPIN - indoor WIFI-based positioning system. • Movement • Variance in Accelerometer axes readings • Posture • Mean readings in Accelerometer axes readings User Interface Learning Logic/ Abnormality Detector Application Logic MAMS Cluster System Sensor Sampler/Aggregator Accelerometer Sensor Interface WI-FI Sensor Interface

8. Clustering Algorithm • Data points corresponding to the same atomic activities form clusters. • New data points clustered based on a Euclidean distance measure. • Clustering is continuous and incremental.

9. Experiments • 1. Normal Event Log Collection • 5 subjects to carry a mobile device for 3 days while performing daily routine. • Subjects expected not to perform any irregular activities. • 2. Abnormal Event Log Collection • Subjects perform a predefined set of abnormal activities. • 3. Compute the system’s precision and recall when calibrated for optimum performance. Number of labeled abnormal activies correctly classified by MAMS Precision = Total number of labeled abnormal activities classified by MAMS Number of labeled abnormal activies correctly classified by MAMS Recall = Total number of labeled abnormal activities

10. Experiments

11. Results

12. Results 90% precision, 40% recall

13. Results

14. Conclusions • MAMS is an anomalous activity detector based on a KNN unsupervised clustering algorithm • MAMS supports continuous and incremental learning • MAMS produces user-specific activity models • MAMS distinguishes well between anomalous activities and normal ones

15. Future Work • Analyze performances of several alternate online unsupervised learning methods • Test MAMS performance in target environment. • Re-valuate the performance of the location feature in larger experiment • Evaluate the performance of the classifier with new features (ie. time of day)