Indoor/Outdoor Pedestrian Navigation with an Embedded GPS/RFID/Self-contained Sensor System

Kourogi, Masakatsu; Sakata, Nobuchika; Okuma, Takashi; Kurata, Takeshi

doi:10.1007/11941354_136

Cited by 84 publications

(40 citation statements)

References 3 publications

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“…These trajectories could be collected by social networks (Cho et al, 2011), sensor networks (Ji and Zha, 2004), RFIDs (Kourogi et al, 2006), WI-FI (Song et al, 2006), simulators (Mousavi et al, 2007), internet of things (Macagnano et al, 2014), and cellular networks (Si et al, 2010). Among all of these kinds of trajectories, our work is focused on the trajectories collected by GPS sensors.…”

Section: Related Workmentioning

confidence: 99%

Geometry of interest (GOI): spatio-temporal destination extraction and partitioning in GPS trajectory data

Mousavi

Harwood

Karunasekera

et al. 2016

J Ambient Intell Human Comput

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Nowadays large amounts of GPS trajectory data is being continuously collected by GPS-enabled devices such as vehicles navigation systems and mobile phones. GPS trajectory data is useful for applications such as traffic management, location forecasting, and itinerary planning. Such applications often need to extract the time-stamped Sequence of Visited Locations (SVLs) of the mobile objects. The nearest neighbor query (NNQ) is the most applied method for labeling the visited locations based on the IDs of the POIs in the process of SVL generation. NNQ in some scenarios is not accurate enough. To improve the quality of the extracted SVLs, instead of using NNQ, we label the visited locations as the IDs of the POIs which geometrically intersect with the GPS observations. Intersection operator requires the accurate geometry of the points of interest which we refer to them as the Geometries

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Section: Related Workmentioning

confidence: 99%

Geometry of interest (GOI): spatio-temporal destination extraction and partitioning in GPS trajectory data

Mousavi

Harwood

Karunasekera

et al. 2016

J Ambient Intell Human Comput

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“…However, in indoor environments GPS signal is also not very reliable, thus fuelling research in indoor INS. For example, Kourogi et al in their classical work integrated self-contained dead-reckoning sensors with GPS and RFID tagged beacons to adjust for positioning errors [8]. A similar but more recent approach can be found in [9], and a good review of indoor INS solutions in [10].…”

Section: Related Workmentioning

confidence: 99%

Real-time sensor data integration in vertical transport systems: Are conventional mobile devices capable of coping with industrial measurement?

Francik

Brujic-Okretic

Menegazzo

et al. 2016

2016 IEEE Sensors Applications Symposium (SAS)

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Abstract-In this project, mobile connectivity and an innovative approach to sensor data gathering and integration have been employed to automate maintenance inspection, performance monitoring and ride quality measurement in vertical transportation systems. An Inertial Navigation System (INS) has been proposed, implemented and tested to track lift car movement profile. The inherent characteristics of vertical motion have been used to minimize errors and obtain higher accuracy in the integration results. The measurement of a correlation between kinematic profiles constructed from lift-car tracking data compared to its nominal values provides key information on the lift condition at any time. A frequency analysis was applied to processing vibrations and noise data, effectively adding another dimension to the lift ride quality measurement. This approach enabled lift performance profiles to be compiled automatically and transmitted in real time, which significantly rationalized and improved the process of maintenance inspection and monitoring. An advanced prototype, AdInspect, has been produced, with the full set of described features. Industry partners are currently evaluating it.

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“…Kourogi et al (2006) integrated wearable inertial sensors, a GPS function, and an RFID tag system. Woodman and Harle (2008) also integrated wearable inertial sensors and map information.…”

Section: Locating People By Using a Combination Of Sensorsmentioning

confidence: 99%

Pedestrian identification by associating wearable and environmental sensors based on phase dependent correlation of human walking

Ikeda

Ishiguro

Miyashita

et al. 2013

J Ambient Intell Human Comput

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Providing personal and location-dependent services is one of the promising services in public spaces like a shopping mall. So far, sensors in the environment have reliably detected the current positions of humans, but it is difficult to identify people using these sensors. On the other hand, wearable devices can send their personal identity information, but precise position estimation remains problematic. In this paper, we propose a method of associating wearable accelerometers and foot tracking results using laser range finders in the environment. First we propose an association method based on signal correlation between biped foot and accelerometer. However, in crowded situations, sometimes only one foot of a pedestrian is observed because of occlusion. To cope with the problem, we propose a new evaluation function that focuses on the phase dependent correlation of cyclic walking behavior. Example results of tracking individuals in the environment confirm the effectiveness of this method.

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Indoor/Outdoor Pedestrian Navigation with an Embedded GPS/RFID/Self-contained Sensor System

Cited by 84 publications

References 3 publications

Geometry of interest (GOI): spatio-temporal destination extraction and partitioning in GPS trajectory data

Geometry of interest (GOI): spatio-temporal destination extraction and partitioning in GPS trajectory data

Real-time sensor data integration in vertical transport systems: Are conventional mobile devices capable of coping with industrial measurement?

Pedestrian identification by associating wearable and environmental sensors based on phase dependent correlation of human walking

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