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A
Two-Level Approach to Characterizing Human Activities from Wearable Sensor
Data
Interdisciplinary
Centre for Security, Reliability and Trust (SnT) University
of Luxembourg 4
rue Alphonse Weicker, L-2721 Luxembourg, Luxembourg Abstract The rapid emergence of new technologies in recent
decades has opened up a world of opportunities for a better understanding of
human mobility and behavior. It is now possible to recognize human movements,
physical activity and the environments in which they take place. And this can
be done with high precision, thanks to miniature sensors integrated into our
everyday devices. In this paper, we explore different methodologies for
recognizing and characterizing physical activities performed by people
wearing new smart devices. Whether it’s smartglasses, smartwatches or smartphones, we show that
each of these specialized wearables has a role to play in interpreting and
monitoring moments in a user’s life. In particular, we propose an approach
that splits the concept of physical activity into two sub-categories that we
call micro- and macro-activities. Micro- and macro-activities are supposed to
have functional relationship with each other and should therefore help to
better understand activities on a larger scale. Then, for each of these
levels, we show different methods of collecting, interpreting and evaluating
data from different sensor sources. Based on a sensing system we have developed
using smart devices, we build two data sets before analyzing how to recognize
such activities. Finally, we show different interactions and combinations
between these scales and demonstrate that they have the potential to lead to
new classes of applications, involving authentication or user profiling. Keywords: Activity Recognition, Wearable &
Mobile Computing, Sensing Systems, Data Analytics +: Corresponding author: Sébastien Faye Journal of Wireless Mobile
Networks, Ubiquitous Computing, and Dependable Applications (JoWUA) |