scholarly journals Modeling Privacy in WiFi Fingerprinting Indoor Localization

2018 ◽  
pp. 329-346 ◽  
Author(s):  
Zheng Yang ◽  
Kimmo Järvinen
Keyword(s):  
Indoor Localization

Design of a Bell-Shaped Ultra Wideband Antenna for Indoor Localization System

2015 ◽  
Vol 5 (6) ◽  
pp. 323 ◽  
Author(s):  
Nadia Ghariani ◽  
Mohamed Salah Karoui ◽  
Mondher Chaoui ◽  
Mongi Lahiani ◽  
Hamadi Ghariani
Keyword(s):  
Indoor Localization ◽  
Ultra Wideband ◽  
Localization System ◽  
Wideband Antenna

IoT-TD: IoT Dataset for Multiple Model BLE-based Indoor Localization/Tracking

2021 ◽  
Author(s):  
Mohammad Salimibeni ◽  
Zohreh Hajiakhondi-Meybodi ◽  
Parvin Malekzadeh ◽  
Mohammadamin Atashi ◽  
Konstantinos N. Plataniotis ◽  
...  
Keyword(s):  
Indoor Localization ◽  
Multiple Model

Physical Activity Behavior of Skilled Nursing Facility Patients: A 21-day Longitudinal Cohort Study (Preprint)

2020 ◽  
Author(s):  
Wenhao Zhang ◽  
Ramin Ramezani ◽  
Zhuoer Xie ◽  
John Shen ◽  
David Elashoff ◽  
...  
Keyword(s):  
Physical Activity ◽  
Energy Intensity ◽  
Indoor Localization ◽  
Pearson Correlation ◽  
Patent Application ◽  
Skilled Nursing Facility ◽  
Nursing Facility ◽  
Skilled Nursing ◽  
Longitudinal Changes ◽  
Therapy Room

BACKGROUND The availability of low cost ubiquitous wearable sensors has enabled researchers, in recent years, to collect a large volume of data in various domains including healthcare. The goal has been to harness wearables to further investigate human activity, physiology and functional patterns. As such, on-body sensors have been primarily used in healthcare domain to help predict adverse outcomes such as hospitalizations or fall, thereby enabling clinicians to develop better intervention guidelines and personalized models of care to prevent harmful outcomes. In the previous studies [9,10] and the patent application [11], we introduced a generic framework (Sensing At-Risk Population) that draws on the classification of human movements using a 3-axial accelerometer and extraction of indoor localization using BLE beacons, in concert. This work is to address the longitudinal analyses of a particular cohort using the introduced framework in a skilled nursing facility. OBJECTIVE (a) To observe longitudinal changes of physical activity and indoor localization features of rehabilitation-dwelling patients, (b) to assess if such changes can be used at early stages during the rehabilitation period to discriminate between patients that will be re-hospitalized versus the ones that will be discharged to a community setting and (c) to investigate if the sensor based longitudinal changes can imitate patients changes captured by therapist assessments over the course of rehabilitation. METHODS Pearson correlation was used to compare occupational therapy (OT) and physical therapy (PT) assessments with sensor-based features. Generalized Linear Mixed Model was used to find associations between functional measures with sensor based features. RESULTS Energy intensity at therapy room was positively associated with transfer general (β=0.22;SE=0.08;p<.05). Similarly, sitting energy intensity showed positive association with transfer general (β=0.16;SE=0.07;p<.05). Laying down energy intensity was negatively associated with hygiene grooming (β=-0.27;SE=0.14;p<.05). The interaction of sitting energy intensity with time (β=-0.13;SE=.06;p<.05) was associated with toileting general. Dressing lower body was strongly correlated with overall energy intensity (r = 0.66), standing energy intensity (r = 0.61), and laying down energy intensity (r = 0.72) on the first clinical assessment session. CONCLUSIONS This study demonstrates that a combination of indoor localization and physical activity tracking produces a series of features, a subset of which can provide crucial information on the storyline of daily and longitudinal activity patterns of rehabilitation-dwelling patients.

scholarly journals The Technology of Crowd-Sourcing Landmarks-Assisted Smartphone in Indoor Localization

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 57036-57048 ◽  
Author(s):  
Xinxin Wang ◽  
Danyang Qin ◽  
Ruolin Guo ◽  
Min Zhao ◽  
Lin Ma ◽  
...  
Keyword(s):  
Indoor Localization ◽  
Crowd Sourcing

scholarly journals SurfCNN: A Descriptor Accelerated Convolutional Neural Network for Image-Based Indoor Localization

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 59750-59759
Author(s):  
Ahmed M. Elmoogy ◽  
Xiaodai Dong ◽  
Tao Lu ◽  
Robert Westendorp ◽  
Kishore Reddy Tarimala
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Indoor Localization

scholarly journals Dynamic Indoor Localization Using Maximum Likelihood Particle Filtering

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1090
Author(s):  
Wenxu Wang ◽  
Damián Marelli ◽  
Minyue Fu
Keyword(s):  
Maximum Likelihood ◽  
Particle Filtering ◽  
Indoor Localization ◽  
Real Data ◽  
High Accuracy ◽  
Classical Particle ◽  
Popular Approach ◽  
Filtering Method ◽  
Essential Idea ◽  
Number Of Particles

A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.

scholarly journals Towards Robust Multiple Blind Source Localization Using Source Separation and Beamforming

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 532
Author(s):  
Henglin Pu ◽  
Chao Cai ◽  
Menglan Hu ◽  
Tianping Deng ◽  
Rong Zheng ◽  
...  
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Indoor Localization ◽  
Weighting Function ◽  
Signal To Noise Ratio ◽  
Source Separation ◽  
Sound Source Localization ◽  
Angle Of Arrival ◽  
Sound Sources ◽  
Localization Algorithms

Multiple blind sound source localization is the key technology for a myriad of applications such as robotic navigation and indoor localization. However, existing solutions can only locate a few sound sources simultaneously due to the limitation imposed by the number of microphones in an array. To this end, this paper proposes a novel multiple blind sound source localization algorithms using Source seParation and BeamForming (SPBF). Our algorithm overcomes the limitations of existing solutions and can locate more blind sources than the number of microphones in an array. Specifically, we propose a novel microphone layout, enabling salient multiple source separation while still preserving their arrival time information. After then, we perform source localization via beamforming using each demixed source. Such a design allows minimizing mutual interference from different sound sources, thereby enabling finer AoA estimation. To further enhance localization performance, we design a new spectral weighting function that can enhance the signal-to-noise-ratio, allowing a relatively narrow beam and thus finer angle of arrival estimation. Simulation experiments under typical indoor situations demonstrate a maximum of only 4∘ even under up to 14 sources.

Smartphones: 3D Indoor Localization Using WiFi RTT

2020 ◽  
pp. 1-1
Author(s):  
Hongji Cao ◽  
Yunjia Wang ◽  
Jingxue Bi
Keyword(s):  
Indoor Localization
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