Human Activity Inference on Smartphones Using C ommunity Similarity Network (CSN)

Human Activity Inference on Smartphones Using Community Similarity Network (CSN) Ye Xu

Labeled Data from Mobile Users

the population diversity problemone size doesn’t fit for all

the population diversity problemis more data the answer?

community similarity networksaddressing the population diversity problem without more labeled data

Evaluation • Everyday Activities 41 persons, 1 to 3 weeks Accelerometer and Audio sensor data {walk, run, stationary, meeting, studying, exercising} • Transportation Mode 51 persons, 3 months GPS sensor data {bike, bus, car, walk}

Evaluation • Single-Model Personalized model only using data from user • Isolated-Model General model suing all available data • Naïve-Multi Training Traditional Multi-training method w.o. using similarity network

Insights From CSN • Real-world learning tasks are complex; A single model may not work on all times; • How to model the problem is more important than a good learning algorithm. • Personalization model is practical by reducing user label burden.

Other Directions?Personalization by Leveraging Social Networks

Other Directions?Reduce user burden by multi-instance modeling

Ye Xu, and Wei Ping. Multi-Instance Metric Learning. In ICDM’11. • Nicholas Lane, Ye Xu, Hong Lu, Shaohan Hu, TanzeemChoudhury and Andrew T. Campbell. Enabling Large-scale Human Activity Inference on Smartphones using Community Similarity Networks (CSN). In Ubicomp’11. • Nicholas Lane, Ye Xu, Hong Lu, Shane B. Eisenmany, TanzeemChoudhury and Andrew T. Campbell. Exploiting Social Networks for Large-scale Modeling of Human Behavior. In IEEE Pervasive Computing Magazine.

Summary • Ask not what the users can provide for you, but what you can provide for the users. • Ask not what the world can provide for us, but what we can do together to change the world.

Human Activity Inference on Smartphones Using C ommunity Similarity Network (CSN)