Activity Recognition : Techniques and Approaches

1. Activity Recognition : Techniques and Approaches PrafullaNathDawadi Cpts 540 Artificial Intelligence

2. Contents • Introduction • Sensor • Techniques • Application • Conclusion

3. Introduction • What is Activity Recognition? • Recognizing daily activities of human • Sequential, Interleaved, Concurrent • What type of activity to recognize? • Activities of Daily Living(ADL) • Hygiene, Dressing , Eating etc • Instrumental ADL • Shopping, Preparing meal ,Managing Medication • Basic Activities • walking , sitting, eating etc

4. Challenge • Recognizing Concurrent Activities Human performs concurrent activities at a time • Recognizing interleavedActivities Humans perform activities in a interleaved setting • Ambiguity of Interpretation Activities are ambiguous in nature to interpret when individual steps are taken under consideration eg: a freeze can be opened to cook or eat • Multiple Resident Activities performed by individual resident either can be parallel or concurrent.

5. Sensor • How to do that ? • Use different type of sensor • Object Sensor • Environmental Sensor • Wearable Sensor • Example : • State change Sensor • Video Sensor • Motion Sensor • Accelerometer • Actigraph • Iphone, PDA device • Some of them are invasive while some are obtrusive.

6. Technique • Use video camera • Process the video • Steps • Input a video sequence. • Extract low level feature • Generate higher level features from these low level one • Interpret/Learn what kind of activities were performed over the higher level features. • Low Level feature : Blobs, Edge • Learning: Hidden Markov Model • Issues • Privacy Invasive • Computationally Intensive • Solution • Use non-invasive Sensor

7. Technique • Use Non-Invasive Sensor • Motion Sensor, Wearable Sensor • Record the data /Process it real time • Use Machine Learning Algorithm to recognize the activity • Algorithm • Naïve Bayes • Hidden Markov Model • Conditional Random Field • Emerging Pattern ……………………………

8. Hidden Markov Model Consider, each hidden state as activity observable state as a sensor x — statesy — possible observationsa — state transition probabilitiesb — output probabilities Train it using sensor data

9. Technique • Use wearable device • Accelerometer, Actigraph with Object sensor such as RFID tag, Shake sensor • Focus on recognizing basic activities such as walking, running • Algorithm • Supervised Learning Algorithm • J48, Neural Network • Extract x,y,z feature • Calculate Mean, Variance etc • Do preprocessing and input to the algorithm

10. Wearable Sensor • Issues • Where an accelerometer must be placed? • At wrist : Detect activity such as walking • At hip : Detect activity such as running • How many accelerometer will give you reasonable accuracy? • Accelerometer at five different body position can detect 21 different set of activities • Poor performance when there is little motion or with less physical characteristics movement • Is it to possible to have real time detection of activities? • Use three accelerometer and a fast preprocessing techn

11. Application • Assist Older Adult in Independent living: • Help older adult live independently in home. • Prompting System: • Remind resident to take medicine providing appropriate audio/video cues. • Remote Health Monitoring: • Health of the older adult in smart home can be monitored via health sensor such as blood pressure monitor, blood sugar level monitor etc. • Functional Assessment Technique: • Continuously track the activity behavior, find if they are having any behavioral changes. • Energy Conservation: • Help saving energy by turning off energy source in places where there are no any activities.

12. Conclusion • Growing research in activity recognition due to its large application • Different technique and approaches • Concentrated over non-invasive sensor • Growing popularity over research community