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

Labeled Data from Mobile Users

the population diversity problem one size doesn’t fit for all

the population diversity problem is more data the answer?

community similarity networks addressing 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, Tanzeem Choudhury 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, Tanzeem Choudhury 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.