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dc.contributor.authorChing, Yu-Taien_US
dc.contributor.authorHe, Guan-Weien_US
dc.contributor.authorCheng, Chang-Chiehen_US
dc.contributor.authorYang, Yu-Jinen_US
dc.date.accessioned2018-08-21T05:57:06Z-
dc.date.available2018-08-21T05:57:06Z-
dc.date.issued2017-01-01en_US
dc.identifier.urihttp://dx.doi.org/10.1145/3123024.3123096en_US
dc.identifier.urihttp://hdl.handle.net/11536/147051-
dc.description.abstractWe implemented a wired sensors system that supports activities identification. The system consists of Raspberry Pi, MPU6050 (accelerometers and gyrometers), and TCA9548 (1 to 8 multiplexer). Our experimental results show that when 6 MPU6050 attached to the right arm, right wrist. chest, waist, right thigh, and right ankle, the activities of standing, sitting, lying, walking, running, going upstairs, going downstairs. drinking water, and dumbbells activities could be identified with high accuracy. The system can connect up to 128 sensors, but under a practical sampling rate, the number of sensors should not be greater than 15. The system shall be used for finding the optimal locations for a multi -sensor wearable system (for examples, clothes or shoes).en_US
dc.language.isoen_USen_US
dc.subjectHuman Activity Recognitionen_US
dc.subjectWearable sensorsen_US
dc.subjectphysical activitiesen_US
dc.titleFull Model for Sensors Placement and Activities Recognitionen_US
dc.typeProceedings Paperen_US
dc.identifier.doi10.1145/3123024.3123096en_US
dc.identifier.journalPROCEEDINGS OF THE 2017 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING AND PROCEEDINGS OF THE 2017 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS (UBICOMP/ISWC '17 ADJUNCT)en_US
dc.citation.spage17en_US
dc.citation.epage20en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000426932500005en_US
Appears in Collections:Conferences Paper