Adaptive Activity and Context Recognition Using Multimodal Sensors in Smart Devices

Abstract

Abstract. The continuous development of new technologies has led to the creation of a wide range of personal devices embedded with an ever increasing number of miniature sensors. With accelerometers and technologies such as Bluetooth and Wi-Fi, today's smartphones have the potential to monitor and record a complete history of their owners' movements as well as the context in which they occur. In this article, we focus on four complementary aspects related to the understanding of human behaviour. First, the use of smartwatches in combination with smartphones in order to detect di↵erent activities and associated physiological patterns. Next, the use of a scalable and energy-e cient data structure that can represent the detected signal shapes. Then, the use of a supervised classifier (i.e. Support Vector Machine) in parallel with a quantitative survey involving a dozen participants to achieve a deeper understanding of the influence of each collected metric and its use in detecting user activities and contexts. Finally, the use of novel representations to visualize the activities and social interactions of all the users, allowing the creation of quick and easy-to-understand comparisons. The tools used in this article are freely available online under a MIT licence.

Characterizing user mobility using mobile sensing systems

“…One future work, which has already been carried out through a few publications, 6,7 is to go deeper into each of the aspects mentioned in the article.…”

“…As described in Faye et al, 7 it is therefore critical that we make every effort to save energy. By optimizing data transmission, recording frequency, and the devices themselves (e.g.…”

“…Other metrics collected by the phone include contextual data such as microphone readings (level of ambient noise) and network data. Further information about the specifications of this system can be found in Faye et al 7 …”

“…Indeed, the veracity of the Android activity recognition API has been largely verified by the literature and supported by previous studies. 7 Use-case Figure 10 shows four examples of profiles in the form of a radar chart (or Kiviat diagram, as described in Chang et al 18 ) based on data collected on four participants having different characteristics in our dataset. The minimum and maximum axes for each characteristic are determined according to our dataset.…”

Usage of Smartphone Data to Derive an Indicator for Collaborative Mobility between Individuals

“…In this context, the studies conducted as part of this paper are based on SWIPE, an open-source platform for sensing, recording and processing human dynamics using smart devices [40,41]. Figure 2 gives an overview of the SWIPE architecture, which consists of two main parts: a local sensing system composed of one or several smartphones and an online analytics platform where the data from multiple users and devices is aggregated and analyzed.…”