Human Activity Inference on Smartphones Using C ommunity Similarity Network (CSN)
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Human Activity Inference on Smartphones Using Community Similarity Network (CSN)
Ye Xu
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Labeled Data from Mobile Users
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the population diversity problemone size doesn’t fit for all
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the population diversity problemone size doesn’t fit for all
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the population diversity problemone size doesn’t fit for all
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the population diversity problemis more data the answer?
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community similarity networksaddressing the population diversity problem without more labeled data
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Evaluation
• Everyday Activities41 persons, 1 to 3 weeksAccelerometer and Audio sensor data{walk, run, stationary, meeting, studying, exercising}• Transportation Mode 51 persons, 3 monthsGPS sensor data{bike, bus, car, walk}
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Evaluation• Single-ModelPersonalized model only using data from user• Isolated-ModelGeneral model suing all available data• Naïve-Multi TrainingTraditional Multi-training method w.o. using similarity network
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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.
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Other Directions?Personalization by Leveraging Social Networks
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Other Directions?Reduce user burden by multi-instance modeling
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• 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.
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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.
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