Affiliations: [a] Department of Digital Systems, University of Piraeus, Piraeus, Greece | [b] School of Electrical and Computer Engineering, National technical University of Athens, Piraeus, Greece
Correspondence:
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Corresponding author: Ilias Maglogiannis, Department of Digital Systems, University of Piraeus, Grigoriou Lampraki 126, PC 18532 Piraeus, Greece. Tel.: +30 210 414 2517; E-mail:[email protected]
Abstract: Human motion data captured from wearable devices such as
smart watches can be utilized for activity recognition and emergency event
detection, especially in the case of elderly or disabled people living
independently in their homes. The output of such sensors is streams of
physical activity data that require real-time recognition, especially in
emergency situations. This paper presents a novel application that utilizes
the low-cost Pebble Smart Watch together with an Android device (i.e. a
smart phone) and allows the efficient capturing, transmission, storage and
processing of such motion data. The paper includes the technical details of
the stream data capturing and processing methodology, along with a
comparison of the major algorithms used for the classification of physical
activity type (i.e. Mild, Moderate, Intense and Sleep). An initial evaluation
of the achieved accuracy in recognizing activity type, calculating the
energy consumption and detecting falls, is also included and the
corresponding results are discussed. The reported results are quite
promising and can enable the development of intelligent systems, capable of
analyzing human behavior and triggering alarms related to human activity in
addition to fall detection.
Keywords: Activity type recognition, fall detection, smart watch, ambient assisted living, accelerometer, motion data, streaming data
