Activity recognition using optical sensors on mobile phones
Each mobile phone with a built-in CMOS sensor can inherently be seen as sophisticated optical sensor being able to analyze its environment in terms of visual events and its own mobility. Due to mass production their price decreases steadily, although their processing capacity increases. Mobile phones are usually attached to people, who are driven by mobility. We define activities arising from this mobility as internal activities in contrast to external activities, that are caused by visual events. Both activities can be recognized by measuring the sensor's optical flow. We present a method to identify internal activities based on optical flow measurements and probabilistic reasoning. We implement a lifelogging application, running on a Linux-based mobile phone, that can detect internal activities such as moving left-hand, right-hand or walking with a recognition rate of 80\%. While standing still external activities are recognized using object detection.
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