Location Based Indoor and Outdoor Lightweight Activity Recognition System

In intelligent environments one of the most relevant information that can be gathered about users is their location. Their position can be easily captured without the need for a large infrastructure through devices such as smartphones or smartwatches that we easily carry around in our daily life, providing new opportunities and services in the field of pervasive computing and sensing. Location data can be very useful to infer additional information in some cases such as elderly or sick care, where inferring additional information such as the activities or types of activities they perform can provide daily indicators about their behavior and habits. To do so, we present a system able to infer user activities in indoor and outdoor environments using Global Positioning System (GPS) data together with open data sources such as OpenStreetMaps (OSM) to analyse the user’s daily activities, requiring a minimal infrastructure

Scalable and privacy-respectful interactive discovery of place semantics from human mobility traces

Mobility diaries of a large number of people are needed for assessing transportation infrastructure and for spatial development planning. Acquisition of personal mobility diaries through population surveys is a costly and error-prone endeavour. We examine an alternative approach to obtaining similar information from episodic digital traces of people’s presence in various locations, which appear when people use their mobile devices for making phone calls, accessing the internet, or posting georeferenced contents (texts, photos, or videos) in social media. Having episodic traces of a person over a long time period, it is possible to detect significant (repeatedly visited) personal places and identify them as home, work, or place of social activities based on temporal patterns of a person’s presence in these places. Such analysis, however, can lead to compromising personal privacy. We have investigated the feasibility of deriving place meanings and reconstructing personal mobility diaries while preserving the privacy of individuals whose data are analysed. We have devised a visual analytics approach and a set of supporting tools making such privacy-preserving analysis possible. The approach was tested in two case studies with publicly available data: simulated tracks from the VAST Challenge 2014 and real traces built from georeferenced Twitter posts

Visuäly : Visualizing the Condition of Älynysse Bus

We present Visuäly, a set of visualization techniques that enables users to explore historical data of bus conditions. We design and evaluate the prototype, then compare it with the existing system. It is meant to give another perspective and alternative to the system and to increase the value of the existing system environment. We will focus on the visualization technique itself. First, we performed a preliminary survey to assess the existing system. Then, we did four design iterations, each consisted of a moderated remote usability testing session. In addition, we held a survey to compare both systems. We found that participants understand and enjoy interacting with the proposed solution, and also prefer it to the existing system