SenSay: A Context-Aware Mobile Phone
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Gait analyzer based on a cell phone with a single three-axis accelerometer
Proceedings of the 8th conference on Human-computer interaction with mobile devices and services
Proceedings of the 6th ACM conference on Embedded network sensor systems
An activity recognition system for mobile phones
Mobile Networks and Applications
Human Activity Recognizer for Mobile Devices with Multiple Sensors
UIC-ATC '09 Proceedings of the 2009 Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing
Bathroom activity monitoring based on sound
PERVASIVE'05 Proceedings of the Third international conference on Pervasive Computing
Object-based activity recognition with heterogeneous sensors on wrist
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
Indoor-outdoor activity recognition by a smartphone
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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We propose an in-home living activity recognition method using only off-the-shelf sensors, namely, an accelerometer and a microphone, which are commonly applied in mobile phones. The proposed method firstly estimates a user's movement condition roughly by acceleration sensing. Secondly, it classifies the working condition in detail by acoustic sensing when it estimates the condition to be working by acceleration sensing. We developed a prototype system to recognize the user's living activity in real time and conducted two experiments to confirm the feasibility of the proposed method. As a result of the first experiment, three movement conditions; quiet, walking, and working, are classified with more than 95% accuracy by acceleration sensing. And it classified working into seven conditions with 85.9% accuracy by acoustic sensing. Moreover, the result of the second experiment shows that it is effective to adopt instance-based recognition according to the assumed application.