Falls among older people remain a very important public healthcare issue. Every year over 11 million falls are registered in the U.S. alone. This paper presents a practical real time fall detection system running on a smartwatch (F2D). A decision module takes into account the rebound after the fall and the residual movement of the user, matching a detected fall pattern to an actual fall. The final decision of a fall event is taken based on the location of the user. To the best of our knowledge, this is the first fall detection system which works on an independent smartwatch, being less stigmatizing for the end user. The fall detection algorithm has been tested by Fondation Suisse pour les Téléthèses (FST), the project partner who is responsible for the commercialization of our system. By analyzing real data of activities of daily life of elderly people, we are confident that F2D meets the demands of a reliable and easily extensible system. This paper highlights the innovative algorithm which takes into account the residual movement and the location of the user to increase the fall detection accuracy. By testing with real data we have a fall detection system ready to be deployed on the market.

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Gestures are a fast and efficient mean to transmit information. They are used in a large number of situations where speaking is not as effective or even not possible, such as to indicate precisely a point of interest or to warn about a danger in a noisy environment. Furthermore, gestures can also be used for intuitive human-computer interfaces where specific tasks would otherwise require navigating through graphical interface menus. Consequently, solutions to provide reliable and accurate gesture recognition have been investigated extensively in the past years. In this paper, we propose a gesture recognition system to detect user interest with a sensor-embedded mobile phone. Specifically, this system uses hidden Markov models to recognize pointing gestures. Once such a gesture has been recognized, it is straightforward to identify the point of interest based on the user location and the phone orientation. In a subject-independent scenario, we obtained a recognition accuracy above 91 % with the accelerometer when discriminating between pointing gestures and similar gestures that are common with a mobile phone (e.g.looking at the screen). When using the gyroscope in addition to the accelerometer, the accuracy raised above 98%.

Interacting with smartphones generally requires direct input from the user. We investigated a novel way based on the user's behaviour to interact directly with a phone. In this paper, we present Move Y ourStory, a mobile application that generates a movie composed of small video clips selected according to the user's position and his current behaviour when the user is moving.Towards this end, we have implemented an activity recognition module that is able to recognise current activities, like walking, bicycling or travelling in a vehicle using the accelerometer and the GPS embedded in a smartphone. Moreover, we added different walking intensity levels to the recognition algorithm, as well as the possibility of using the application in any position. A user study was done to validate our algorithm. Overall, we achieved 96.7% recognition accuracy for walking activities and 87.5% for the bicycling activity.