Authors:
Minh-Son Dao
1
;
Duc-Tien Dang-Nguyen
2
;
Michael Riegler
3
and
Cathal Gurrin
2
Affiliations:
1
Universiti Teknologi Brunei, Brunei Darussalam
;
2
Dublin City University, Ireland
;
3
Simula Research Laboratory, Norway
Keyword(s):
Lifelog, Human Activity Recognition, Smartphones, Embedded Sensors, Smart-City, Heterogeneous Sensory Data Analytics.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Learning of Action Patterns
;
Pattern Recognition
;
Sensors and Early Vision
;
Software Engineering
Abstract:
This paper introduces a new idea for sensor data analytics, named PHASOR, that can recognize and stream
individual human activities online. The proposed sensor concept can be utilized to solve some emerging
problems in smartcity domain such as health care, urban mobility, or security by creating a lifelog of human
activities. PHASOR is created from three ‘components’: ID, model, and Sensor. The first component is to
identify which sensor is used to monitor which object (e.g., group of users, individual users, type of smartphone).
The second component decides suitable classifiers for human activities recognition. The last one
includes two types: (1) physical sensors that utilize embedded sensors in smartphones to recognize human
activities, (2) human factors that uses human interaction to personally increase the accuracy of the detection.
The advantage of PHASOR is the error signal is inversely proportional to its lifetime, which is well-suited
for lifelogging applications. The propos
ed concept is evaluated and compared to de-facto datasets as well as
state-of-the-art of Human Activity Recognition (HAR) using smartphones, confirming that applying PHASOR
can improves the accuracy of HAR.
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