The Analysis And Application Of Daily Exercise Through Sensor Signal For Mobile Intelligent Terminal

With the development of the sensor technology, more and more researches prove thatsensor signals of energy can present human’s kinematic characteristic. In recent years, therehave been a lot of researches based on acceleration sensor signal. However, because of thespecialized hardware, the complexity of system configuration and the rigid using condition,this technology hasn’t been used widely yet.On the other side, because of the development of3G network and smartphone, theconcept of mobile internet deeply affect our daily life. Both the accumulation speed of thenumber of users and the speed of development of the mobile internet technology itself aresubstantial leading Inertnet. In the market of smartphone, Apple’s IOS, Google’s Android andMicrosoft’s WP7has a fast growing in recently. A notable point is that the acceleration sensorhas now been an essential device within the smartphone. This provides possible for theresearch of the energy signal based on the smartphone.This thesis focused on the study of energy-signal on smartphone. Our works include:1. We propose a gravity filter solution for acceleration signal used for smartphonedevices and a motion recognition method for the signal. Since the smartphone device has thenetwork communication module, we give a falling monitoring algorithm and a falling eventprocessing method with the location information. This system is more precise for evaluatingthe quantity of exercise for daily life and more scientific for processing the falling event.2. We complete a daily physical activity analyzing software on Android OS. Thissoftware can measure the step number, distance, calories and speed of user’s movement byhour, day or month, with falling monitor, sport tips offering and some other interestingfunctions.3. We propose an optimization method for implementing daily physical activityanalyzing software on Android OS. With the method of power saving, reducing thecomplexity of the algorithm, sampling rate adaptation and UI optimization, this software iswell compatible with Android OS.4. We implement our motion analyzing method on Windows Phone7platform.