1. Using Mobile Phones to Determine Transportation Modes Sasank Reddy, Min Mun, Jeff Burke, D. Estrin, M. Hansen, M. Srivastava TOSN 2010

2. Determining Transportation Modes Possible transportation modes: – Stationary, walking, running, biking, motorized movement Applications: – Physical activity monitoring – Personal Impact and/or Exposure Monitoring – Transportation and Mobility-based Recruitment (distributed data gathering)

3. Related Work Pedometer (e.g., Omron) Mobile phones based pedometers FitBit/Philips Tracmor (calorie burnt), BodyMedia (GSR…) SenseWear (accel, audio, barometer) Cell-tower assisted detection – Signal strength of multiple cell-towers nearby (but it’s not available under current smartphones) – Wi-Fi/cell-tower : coarse grained classification (stationary, walking, motorized) This work: GPS + accel in smartphones: still, walk, run, bike, vehicle

4. Design Principles Design principles: – Using only a mobile phone (less cpu/memory footprint, energy efficient) Outdoor movement is monitored w/ GMS cell tower (which triggers GPS outdoors) – Not susceptible to position/orientation Feature selection must be good enough – Able to work for a variety of users w/o additional training Classifier should be generic enough – Not using external spatial data (or user’s movement history data) Even without such data it should be accurate enough Design a new classifier w/ mobile phone (GPS, Accel)

5. Design Space Sensor selection – Bluetooth – WiFi and GSM – Accelerometer and GPS

6. Feature Selection Window size: 1 second – Too small: may not effective (esp: accel sampling freq) – Too large: introduces noise (spanning multiple activity) Types of features: – Mobility: accelerometer Magnitude of force vector Mean, variance, energy DFT (discrete Fourier transform): energy cofficients between 1-10 hz (Bao and Intille 2004) – Appropriate for detecting pedestrian based motion – Speed: GPS Invalid points? Analyzing accuracy, dilution of precision, change of speed, etc.. Correlation-based feature selection – (CFS vs. PCA..); CPS eliminates irrelevant/redundant attributes.. – Accel: variance along with DFT energy coefficients between 1-3Hz – GPS: speed Classifier consideration: – Instance classifiers: DT, KMC, Naïve Bayes (NB), Nearest Neighbor (NN), SVM, etc. – HMM-based classifier: continuous vs. discrete

7. Classifier

9. Phone Position and Accuracy

10. User Variation