Browsing by Author "Guinness, Robert"
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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
- ItemA hybrid smartphone indoor positioning solution for mobile LBS.(2012-12-13) Liu, Jingbin; Chen, Ruizhi; Pei, Ling; Guinness, Robert; Kuusniemi, HeidiSmartphone positioning is an enabling technology used to create new business in the navigation and mobile location based services LBS industries This paper presents a smartphone indoor positioning engine named HIPE that can be easily integrated with mobile LBS HIPE is a hybrid solution that fuses measurements of smartphone sensors with wireless signals The smartphone sensors are used to measure the user s motion dynamics information MDI which represent the spatial correlation of various locations Two algorithms based on hidden Markov model HMM problems the grid based filter and the Viterbi algorithm are used in this paper as the central processor for data fusion to resolve the position estimates and these algorithms are applicable for different applications e g real time navigation and location tracking respectively HIPE is more widely applicable for various motion scenarios than solutions proposed in previous studies because it uses no deterministic motion models which have been commonly used in previous works The experimental results showed that HIPE can provide adequate positioning accuracy and robustness for different scenarios of MDI combinations HIPE is a cost efficient solution and it can work flexibly with different smartphone platforms which may have different types of sensors available for the measurement of MDI data The reliability of the positioning solution was found to increase with increasing precision of the MDI data
- 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