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Abstract Users of quantified self applications habitually log and track personal information, such as mood. Attempts to automate the procedure of logging mood have been made, but applications themselves rarely provide insights into the user’s mental well-being. In this paper we explore data from two small scale studies related to mobile device usage and mood tracking. We analyse associations between user’s mood throughout the day and the use of smartphone applications from different categories. Our analysis provides insights into the user’s behaviour based on their device usage. These insights mean that QS applications can independently use simple analysis tools to provide similar insights for the user.
Abstract Our world is increasingly interconnected via a wide variety of computers, IoT, wearable and mobile devices. The information provided collectively through these devices offers insightful information on our everyday lives, daily patterns, mood, behaviour, and surrounding environment. Our workshop brings together researchers interested in collecting and augmenting context to understand device specific behaviour and routines, human behaviour and mood, and changes in the environment. The outcomes of this workshop are new tools, methodologies, and potential collaborations for sensing the outlying world as well as ourselves.
Recent advancements in vehicular technology have meant that integrated wireless devices such as Wi-Fi access points or bluetooth are deployed in vehicles at an increasingly dense scale. These vehicular network edge devices, while enabling in car wireless connectivity and infotainment services, can also be exploited as sensors to improve environmental and behavioural awareness that in turn can provide better and more personalised driver feedback and improve road safety. We present WiBot! a network-edge based behaviour recognition and gesture based personal assistant system for cars. WiBot leverages the vehicular network edge to detect distracted behaviour based on unusual head turns and arm movements during driving situations by monitoring radio frequency fluctuation patterns in real-time. Additionally, WiBot can recognise known gestures from natural arm movements while driving and use such gestures for passenger-car interaction. A key element of WiBot design is its impulsive windowing approach that allows start and end of gestures to be accurately identified in a continuous stream of data. We validate the system in a realistic driving environment by conducting a non-choreographed continuous recognition study with 40 participants at BMW Group Research, New Technologies and Innovation centre. By combining impulsive windowing with a unique selection of features from peaks and subcarrier analysis of RF CSI phase information, the system is able to achieve 94.5% accuracy for head-vs. arm movement separation. We can further confidently differentiate relevant gestures from random arm and head movements, head turns and idle movement with 90.5% accuracy.