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  1. Falls are the second leading cause of accidental or unintentional injuries/deaths worldwide. Accurate pose estimation using commodity mobile devices will help early detection and injury assessment of falls, which are essential for the first aid of elderly falls. By following the definition of fall, we propose a P ervasive P ose Est imation scheme for fall detection ( P \( ^2 \) Est ), which measures changes in tilt angle and height of the human body. For the tilt measurement, P \( ^2 \) Est leverages the pointing of the mobile device, e.g., the smartphone, when unlocking to associate the Device coordinate system with the World coordinate system. For the height measurement, P \( ^2 \) Est exploits the fact that the person’s height remains unchanged while walking to calibrate the pressure difference between the device and the floor. We have prototyped and tested P \( ^2 \) Est in various situations and environments. Our extensive experimental results have demonstrated that P \( ^2 \) Est can track the body orientation irrespective of which pocket the phone is placed in. More importantly, it enables the phone’s barometer to detect falls in various environments with decimeter-level accuracy. 
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    The ability to sense ambient temperature pervasively, albeit crucial for many applications, is not yet available, causing problems such as degraded indoor thermal comfort and unexpected/premature shutoffs of mobile devices. To enable pervasive sensing of ambient temperature, we propose use of mobile device batteries as thermometers based on (i) the fact that people always carry their battery-powered smart phones, and (ii) our empirical finding that the temperature of mobile devices' batteries is highly correlated with that of their operating environment. Specifically, we design and implement Batteries-as-Thermometers (BaT), a temperature sensing service based on the information of mobile device batteries, expanding the ability to sense the device's ambient temperature without requiring additional sensors or taking up the limited on-device space. We have evaluated BaT on 6 Android smartphones using 19 laboratory experiments and 36 real-life field-tests, showing an average of 1.25°C error in sensing the ambient temperature. 
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