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Smart Lifelogging: Recognizing Human Activities using PHASOR


Minh-Son Dao, Duc Tien Dang Nguyen, Michael Riegler, Cathal Gurrin

Publication Type: 
Refereed Conference Meeting Proceeding
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 smart- phone). 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 proposed 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.
Conference Name: 
6th International Conference on Pattern Recognition Applications and Methods
Proceedings of 6th International Conference on Pattern Recognition Applications and Methods
Digital Object Identifer (DOI): 
Publication Date: 
Conference Location: 
Research Group: 
Dublin City University (DCU)
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