Browsing by Author "Chen, Yuwei"
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- ItemHuman behavior cognition using smartphone sensors.(2013-01-25) Pei, Ling; Guinness, Robert; Chen, Ruizhi; Liu, Jingbin; Kuusniemi, Heidi; Chen, Yuwei; Chen, Liang; Kaistinen, JyrkiThis research focuses on sensing context modeling human behavior and developing a new architecture for a cognitive phone platform We combine the latest positioning technologies and phone sensors to capture human movements in natural environments and use the movements to study human behavior Contexts in this research are abstracted as a Context Pyramid which includes six levels Raw Sensor Data Physical Parameter Features Patterns Simple Contextual Descriptors Activity Level Descriptors and Rich Context To achieve implementation of the Context Pyramid on a cognitive phone three key technologies are utilized ubiquitous positioning motion recognition and human behavior modeling Preliminary tests indicate that we have successfully achieved the Activity Level Descriptors level with our LoMoCo Location Motion Context model Location accuracy of the proposed solution is up to 1 9 meters in corridor environments and 3 5 meters in open spaces Test results also indicate that the motion states are recognized with an accuracy rate up to 92 9 using a Least Square Support Vector Machine LS SVM classifier
- ItemiParking: an intelligent indoor location-based smartphone parking service.(2012-12-03) Liu, Jingbin; Chen, Ruizhi; Chen, Yuwei; Pei, Ling; Chen, LiangIndoor positioning technologies have been widely studied with a number of solutions being proposed yet substantial applications and services are still fairly primitive Taking advantage of the emerging concept of the connected car the popularity of smartphones and mobile Internet and precise indoor locations this study presents the development of a novel intelligent parking service called iParking With the iParking service multiple parties such as users parking facilities and service providers are connected through Internet in a distributed architecture The client software is a light weight application running on a smartphone and it works essentially based on a precise indoor positioning solution which fuses Wireless Local Area Network WLAN signals and the measurements of the built in sensors of the smartphones The positioning accuracy availability and reliability of the proposed positioning solution are adequate for facilitating the novel parking service An iParking prototype has been developed and demonstrated in a real parking environment at a shopping mall The demonstration showed how the iParking service could improve the parking experience and increase the efficiency of parking facilities The iParking is a novel service in terms of cost and energy efficient solution
- ItemUsing LS-SVM based motion recognition for smartphone indoor wireless positioning.(2012-07-10) Pei, Ling; Liu, Jingbin; Guinness, Robert; Chen, Yuwei; Kuusniemi, Heidi; Chen, RuizhiThe paper presents an indoor navigation solution by combining physical motion recognition with wireless positioning Twenty seven simple features are extracted from the built in accelerometers and magnetometers in a smartphone Eight common motion states used during indoor navigation are detected by a Least Square Support Vector Machines LS SVM classification algorithm e g static standing with hand swinging normal walking while holding the phone in hand normal walking with hand swinging fast walking U turning going up stairs and going down stairs The results indicate that the motion states are recognized with an accuracy of up to 95 53 for the test cases employed in this study A motion recognition assisted wireless positioning approach is applied to determine the position of a mobile user Field tests show a 1 22 m mean error in Static Tests and a 3 53 m in Stop Go Tests