Multi-person identification and localization using RFID and passive sensor technology

Ferdous, Shahina; Becker, Eric; Fegaras, Leonidas; Makedon, Fillia

doi:10.1145/2141622.2141700

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…Different methodologies and technologies were reviewed in [122] for indoor and outdoor localization, and a comprehensive study was presented in terms of scalability, cost, security, accuracy, and complexity. Many indoor localization and identification techniques were introduced in research to This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: B Localization and Identification Approaches Based On Rfid Systemmentioning

confidence: 99%

Systematic Review of Dynamic Multi-Object Identification and Localization: Techniques and Technologies

Ali¹,

Liu²,

He³

et al. 2021

IEEE Access

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Object identification and localization in indoor and outdoor environments are paramount issues in object-human interaction. Recent advancements in the data fusion capabilities of multi-sensor systems have paved the way for research on emerging object identification and positioning techniques. This review describes techniques and methods used in positioning technologies. State-of-the-art localization technologies are classified into range-based, range-free and AI-based categories. An in-depth analysis of localization approaches based on laser range finder, radio-frequency identification, ultra-wideband, inertial measurement unit, etc., are presented by providing a detailed comparison based on range, accuracy, measurement method, advantages, disadvantages, and their applications. Furthermore, we investigate stateof-the-art multimodal data fusion techniques that utilize probabilistic methods for the precise estimation of object identification in motion and its localization.INDEX TERMS Indoor and outdoor positioning systems; identification and localization; unstructured environment; multi-sensor system; multi-object; positioning and localization techniques.

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Section: B Localization and Identification Approaches Based On Rfid Systemmentioning

confidence: 99%

Systematic Review of Dynamic Multi-Object Identification and Localization: Techniques and Technologies

Ali¹,

Liu²,

He³

et al. 2021

IEEE Access

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“…Unfortunately, this method cannot recognize two occupants when their movement paths cross. [ 19 ] uses RFID tags and readers for the same person-counting task: the method maintains an uncertainty circle for each person, with a radius computed as the product of their speed of movement multiplied by the time lapsed; when a new event comes from a reader, the method assumes the person most likely to have moved in the vicinity of the reader based on their uncertainty circle and their direction of movement. A more recent paper by the same group [ 20 ] uses a much more expensive Kinect sensor to actually estimate the occupants’ locations.…”

Section: Related Workmentioning

confidence: 99%

Sensor-Data Fusion for Multi-Person Indoor Location Estimation

Mohebbi

Stroulia

Nikolaidis

2017

Sensors

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We consider the problem of estimating the location of people as they move and work in indoor environments. More specifically, we focus on the scenario where one of the persons of interest is unable or unwilling to carry a smartphone, or any other “wearable” device, which frequently arises in caregiver/cared-for situations. We consider the case of indoor spaces populated with anonymous binary sensors (Passive Infrared motion sensors) and eponymous wearable sensors (smartphones interacting with Estimote beacons), and we propose a solution to the resulting sensor-fusion problem. Using a data set with sensor readings collected from one-person and two-person sessions engaged in a variety of activities of daily living, we investigate the relative merits of relying solely on anonymous sensors, solely on eponymous sensors, or on their combination. We examine how the lack of synchronization across different sensing sources impacts the quality of location estimates, and discuss how it could be mitigated without resorting to device-level mechanisms. Finally, we examine the trade-off between the sensors’ coverage of the monitored space and the quality of the location estimates.

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“…For all other uses, contact the owner/author(s). Copyright is held by the author/owner(s Multimodal data is playing an increasingly significant role in healthcare, with sophisticated technologies involved in monitoring and diagnosis [2], [3], [4], the advent of smart homes [5] enabling accurate and effective remote monitoring and care allowing elderly to live independently and remain active members of society. The processing of great quantities of multimodal data is now regarded as a necessary complement to physicians' diagnoses, with computers able to process vast amounts of multimodal data, statistics and the medical literature in an efficient manner, greatly supporting health professionals [6].…”

Section: Introductionmentioning

confidence: 99%

Acm Mm Miirh 2013

Benois-Pineau

Briassouli

Hauptmann

2013

Proceedings of the 21st ACM International Conference on Multimedia

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Healthcare systems are depending on increasingly sophisticated and ubiquitous technology, while telehealth is rapidly gaining importance with the advent of low-cost and effective technological solutions in medicine. The increase in the worldwide elderly population and the burden this is inflicting upon the workforce, societies and economies are making remote care and independent living at home a necessity. MIIRH is the first workshop on multimedia analysis for remote care of and assisted living solutions which enable people that are incapacitated in some regard to continue living independently at home and remain active members of society. The topics addressed in MIIRH are extremely timely, as multitudes of cost-effective and high quality care solutions are already being developed and used, rendering the examination of new medical, healthcare paradigms an absolute necessity.

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Multi-person identification and localization using RFID and passive sensor technology

Cited by 4 publications

References 2 publications

Systematic Review of Dynamic Multi-Object Identification and Localization: Techniques and Technologies

Systematic Review of Dynamic Multi-Object Identification and Localization: Techniques and Technologies

Sensor-Data Fusion for Multi-Person Indoor Location Estimation

Acm Mm Miirh 2013

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