Characterization of a multi-user indoor positioning system based on low cost depth vision (Kinect) for monitoring human activity in a smart home

Sevrin, Loic; Noury, Norbert; Abouchi, Nacer; Jumel, Fabrice; Massot, Bertrand; Saraydaryan, Jacques

doi:10.1109/embc.2015.7319515

Cited by 23 publications

(9 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The importance of indoor positioning is reflected by the increasing requirements of location-based services (LBSs). Under such a background, global researchers have proposed a lot of indoor positioning methods based on different technologies, such as wireless fidelity (Wi-Fi) [1,2], Bluetooth [3], radio frequency identification (RFID) [4], computer vision [5], ultra-wideband (UWB) [6], infrared [7], and inertial navigation systems (INSs) [8], micro-electro-mechanical systems (MEMSs) [9], visible light [10,11], geomagnetic fields [12,13], and pseudolites [14,15], among others.…”

Section: Introductionmentioning

confidence: 99%

A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification

Wang

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

In recent years, many new technologies have been used in indoor positioning. In 2016, IEEE 802.11-2016 created a Wi-Fi fine timing measurement (FTM) protocol, making Wi-Fi ranging more robust and accurate, and providing meter-level positioning accuracy. However, the accuracy of positioning methods based on the new ranging technology is influenced by non-line-of-sight (NLOS) errors. To enhance the accuracy, a positioning method with LOS (line-of-sight)/NLOS identification is proposed in this paper. A Gaussian model has been established to identify NLOS signals. After identifying and discarding NLOS signals, the least square (LS) algorithm is used to calculate the location. The results of the numerical experiments indicate that our algorithm can identify and discard NLOS signals with a precision of 83.01% and a recall of 74.97%. Moreover, compared with the traditional algorithms, by all ranging results, the proposed method features more accurate and stable results for indoor positioning.

show abstract

Section: Introductionmentioning

confidence: 99%

A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification

Wang

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Nowadays, there are a large number of applications for ILBS in market. And researchers have carried out a large number of studies on indoor positioning technologies and developed corresponding indoor positioning systems, such as the infrared [ 1 ], ultrasonic [ 2 ] or sound [ 3 ], radio frequency identity [ 4 , 5 ] (RFID), ZigBee [ 6 ], Wi-Fi [ 7 , 8 ], Bluetooth [ 9 ], microelectromechanical systems (MEMS) sensors [ 10 ], ultra-wideband [ 11 ] (UWB), geomagnetic field [ 12 , 13 ], visible light [ 14 ], computer vision [ 15 ], pseudolites [ 16 ] (PL), etc. However, due to the complexity of the indoor environment, it is usually difficult to provide a satisfactory level of accuracy in most applications.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Weighted KNN Positioning Method Based on Omnidirectional Fingerprint Database and Twice Affinity Propagation Clustering

Wang

et al. 2018

Sensors

View full text Add to dashboard Cite

The human body has a great influence on Wi-Fi signal power. A fixed K value leads to localization errors for the K-nearest neighbor (KNN) algorithm. To address these problems, we present an adaptive weighted KNN positioning method based on an omnidirectional fingerprint database (ODFD) and twice affinity propagation clustering. Firstly, an OFPD is proposed to alleviate body’s sheltering impact on signal, which includes position, orientation and the sequence of mean received signal strength (RSS) at each reference point (RP). Secondly, affinity propagation clustering (APC) algorithm is introduced on the offline stage based on the fusion of signal-domain distance and position-domain distance. Finally, adaptive weighted KNN algorithm based on APC is proposed for estimating user’s position during online stage. K initial RPs can be obtained by KNN, then they are clustered by APC algorithm based on their position-domain distances. The most probable sub-cluster is reserved by the comparison of RPs’ number and signal-domain distance between sub-cluster center and the online RSS readings. The weighted average coordinates in the remaining sub-cluster can be estimated. We have implemented the proposed method with the mean error of 2.2 m, the root mean square error of 1.5 m. Experimental results show that our proposed method outperforms traditional fingerprinting methods.

show abstract

“…Different works have been developed in computer vision to track people positioning. Some of these, such as OpenPTrack [11] or the systems presented by Saputra et al [12], Sevrin et al [13], or Nakano et al [14], consider a multicamera solution based on the use of RGB-D sensors. Others, like the works published by Mohedano et al [8] or Elhayek et al [15], follow an approach using common RGB cameras.…”

Section: Overview Of Related Workmentioning

confidence: 99%

Integration of Computer Vision and Wireless Networks to Provide Indoor Positioning

Domingo

Gómez-García-Bermejo

Zalama

et al. 2019

Sensors

View full text Add to dashboard Cite

This work presents an integrated Indoor Positioning System which makes use of WiFi signals and RGB cameras, such as surveillance cameras, to track and identify people navigating in complex indoor environments. Previous works have often been based on WiFi, but accuracy is limited. Other works use computer vision, but the problem of identifying concrete persons relies on such techniques as face recognition, which are not useful if there are many unknown people, or where the robustness decreases when individuals are seen from different points of view. The solution presented in this paper is based on an accurate combination of smartphones along with RGB cameras, such as those used in surveillance infrastructures. WiFi signals from smartphones allow the persons present in the environment to be identified uniquely, while the data coming from the cameras allow the precision of location to be improved. The system is nonintrusive, and biometric data about subjects is not required. In this paper, the proposed method is fully described and experiments performed to test the system are detailed along with the results obtained.

show abstract

Characterization of a multi-user indoor positioning system based on low cost depth vision (Kinect) for monitoring human activity in a smart home

Cited by 23 publications

References 9 publications

A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification

A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification

An Adaptive Weighted KNN Positioning Method Based on Omnidirectional Fingerprint Database and Twice Affinity Propagation Clustering

Integration of Computer Vision and Wireless Networks to Provide Indoor Positioning

Contact Info

Product

Resources

About