ArticleSensors (Basel, Switzerland)2017
Sensor-Data Fusion for Multi-Person Indoor Location Estimation.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Analysis and Improvement of Indoor Positioning Accuracy for UWB Sensors.Sensors (Basel, Switzerland) · 2021Article
- UJAmI Location: A Fuzzy Indoor Location System for the Elderly.International journal of environmental research and public health · 2021Article
- A Practice of BLE RSSI Measurement for Indoor Positioning.Sensors (Basel, Switzerland) · 2021Article
- Towards a Simulation Framework for Smart Indoor Spaces.Sensors (Basel, Switzerland) · 2020Article
- An Indoor Positioning Approach Based on Fusion of Cameras and Infrared Sensors.Sensors (Basel, Switzerland) · 2019Article
- A BLE-Based Pedestrian Navigation System for Car Searching in Indoor Parking Garages.Sensors (Basel, Switzerland) · 2018Article
- Next Generation Wireless Technologies for Internet of Things.Sensors (Basel, Switzerland) · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.