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Electronics
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Recent Research in Positioning and Activity Recognition Systems
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Recent Research in Positioning and Activity Recognition Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 10 December 2024 | Viewed by 5681

Special Issue Editors

Prof. Dr. Youngok Kim

E-Mail Website
Guest Editor
Electronic Engineering Department, Kwangwoon University, Seoul 01897, Republic of Korea
Interests: sensor networks; ultra-wideband; machine learning; 2D/3D positioning systems
Special Issues, Collections and Topics in MDPI journals
Dr. Zhou Biao

E-Mail Website
Guest Editor
Internet of Things School, Jiangnan University, Wuxi 214122, China
Interests: advanced positioning and tracking algorithms; RFID-based passive positioning schemes; multiple positioning system fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Topic

(1) Positioning or activity recognizing technology based on Internet of Things;

(2) Positioning or activity recognizing technology based on wireless signals;

(3) UWB- or FMCW Radar-based positioning or activity recognizing technologies;

(4) Machine learning and artificial intelligence for positioning or activity recognition;

(5) Channel State Information-based positioning or activity recognition systems;

(6) Vision-based positioning or activity recognition systems;

(7) GPS-based hybrid positioning or tracking systems;

(8) Signal processing for positioning or activity recognition systems;

(9) Pedestrian dead reckoning systems;

(10) Magnetic field-based positioning systems.

Prof. Dr. Youngok Kim
Dr. Zhou Biao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • positioning
  • tracking
  • activity recognizing
  • sensors
  • machine learning
  • artificial neural networks

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Published Papers (5 papers)

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Research

15 pages, 3389 KiB  
Article
Indoor Positioning Method by CNN-LSTM of Continuous Received Signal Strength Indicator
by Jae-hyuk Yoon, Hee-jin Kim, Dong-seok Lee and Soon-kak Kwon
Electronics 2024, 13(22), 4518; https://doi.org/10.3390/electronics13224518 - 18 Nov 2024
Viewed by 270
Abstract
This paper proposes an indoor positioning method based on Bluetooth Low Energy signals by Convolution Neural Network-Long Short-Term Memory (CNN-LSTM). The proposed method determines a receiver location based on distances from adjacent transmitters. The CNN-LSTM model estimates the distance from each transmitter using [...] Read more.
This paper proposes an indoor positioning method based on Bluetooth Low Energy signals by Convolution Neural Network-Long Short-Term Memory (CNN-LSTM). The proposed method determines a receiver location based on distances from adjacent transmitters. The CNN-LSTM model estimates the distance from each transmitter using continuous signal strengths. To train and validate the model, the signal strengths are collected in several locations within various indoor environments. The positioning technique is adaptively selected based on the highest signal strength to avoid the interfering problem due to an excessively strong signal. If the signal strength exceeds a certain threshold, the location is determined using the proximity technique, which utilizes only the strongest signal instead of triangulation. In the experimental results, the proposed method demonstrated an average error of about 2.90 m, which is 34.2% better than a triangulation-based positioning method that does not utilize neural networks. Full article
(This article belongs to the Special Issue Recent Research in Positioning and Activity Recognition Systems)
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18 pages, 5527 KiB  
Article
Leveraging Off-the-Shelf WiFi for Contactless Activity Monitoring
by Zixuan Zhu, Wei Liu, Hao Zhang and Jinhu Lu
Electronics 2024, 13(17), 3351; https://doi.org/10.3390/electronics13173351 - 23 Aug 2024
Viewed by 554
Abstract
Monitoring human activities, such as walking, falling, and jumping, provides valuable information for personalized health assistants. Existing solutions require the user to carry/wear certain smart devices to capture motion/audio data, use a high-definition camera to record video data, or deploy dedicated devices to [...] Read more.
Monitoring human activities, such as walking, falling, and jumping, provides valuable information for personalized health assistants. Existing solutions require the user to carry/wear certain smart devices to capture motion/audio data, use a high-definition camera to record video data, or deploy dedicated devices to collect wireless data. However, none of these solutions are widely adopted for reasons such as discomfort, privacy, and overheads. Therefore, an effective solution to provide non-intrusive, secure, and low-cost human activity monitoring is needed. In this study, we developed a contactless human activity monitoring system that utilizes channel state information (CSI) of the existing ubiquitous WiFi signals. Specifically, we deployed a low-cost commercial off-the-shelf (COTS) router as a transmitter and reused a desktop equipped with an Intel WiFi Link 5300 NIC as a receiver, allowing us to obtain CSI data that recorded human activities. To remove the outliers and ambient noise existing in raw CSI signals, an integrated filter consisting of Hampel, wavelet, and moving average filters was designed. Then, a new metric based on kurtosis and standard deviation was designed to obtain an optimal set of subcarriers that is sensitive to all target activities from the candidate 30 subcarriers. Finally, we selected a group of features, including time- and frequency-domain features, and trained a classification model to recognize different indoor human activities. Our experimental results demonstrate that the proposed system can achieve a mean accuracy of above 93%, even in the face of a long sensing distance. Full article
(This article belongs to the Special Issue Recent Research in Positioning and Activity Recognition Systems)
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27 pages, 25888 KiB  
Article
An Enhanced Hidden Markov Model for Map-Matching in Pedestrian Navigation
by Shengjie Ma, Pei Wang and Hyukjoon Lee
Electronics 2024, 13(9), 1685; https://doi.org/10.3390/electronics13091685 - 26 Apr 2024
Cited by 1 | Viewed by 1020
Abstract
Map-matching is a core functionality of pedestrian navigation applications. The localization errors of the global positioning systems (GPSs) in smartphones are one of the most critical factors that limit the large-scale deployment of pedestrian navigation applications, especially in dense urban areas where multiple [...] Read more.
Map-matching is a core functionality of pedestrian navigation applications. The localization errors of the global positioning systems (GPSs) in smartphones are one of the most critical factors that limit the large-scale deployment of pedestrian navigation applications, especially in dense urban areas where multiple road segments exist within the range of GPS errors, which can be increased by tall buildings neighboring each other. In this paper, we address two issues of practical importance for map-matching based on the Hidden Markov Model (HMM) in pedestrian navigation systems: large localization error in the initial phase of map-matching and HMM breaks in open field traversals. A heuristic method to determine the probability of initial states of the HMM based on a small number of GPS data received during the short warm-up period is proposed to improve the accuracy of initial map-matching. A simple but highly practical method based on a heuristic evaluation of near-future locations is proposed to prevent the malfunction of the Viterbi algorithm within the area of open fields. The results of field experiments indicate that the enhanced HMM constructed via the proposed methods achieves significantly higher map-matching accuracy compared to that of state of the art. Full article
(This article belongs to the Special Issue Recent Research in Positioning and Activity Recognition Systems)
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17 pages, 1182 KiB  
Article
Edge-Computing-Enabled Abnormal Activity Recognition for Visual Surveillance
by Musrrat Ali, Lakshay Goyal, Chandra Mani Sharma and Sanoj Kumar
Electronics 2024, 13(2), 251; https://doi.org/10.3390/electronics13020251 - 5 Jan 2024
Cited by 2 | Viewed by 1336
Abstract
Due to the ever increasing number of closed circuit television (CCTV) cameras worldwide, it is the need of the hour to automate the screening of video content. Still, the majority of video content is manually screened to detect some anomalous incidence or activity. [...] Read more.
Due to the ever increasing number of closed circuit television (CCTV) cameras worldwide, it is the need of the hour to automate the screening of video content. Still, the majority of video content is manually screened to detect some anomalous incidence or activity. Automatic abnormal event detection such as theft, burglary, or accidents may be helpful in many situations. However, there are significant difficulties in processing video data acquired by several cameras at a central location, such as bandwidth, latency, large computing resource needs, and so on. To address this issue, an edge-based visual surveillance technique has been implemented, in which video analytics are performed on the edge nodes to detect aberrant incidents in the video stream. Various deep learning models were trained to distinguish 13 different categories of aberrant incidences in video. A customized Bi-LSTM model outperforms existing cutting-edge approaches. This approach is used on edge nodes to process video locally. The user can receive analytics reports and notifications. The experimental findings suggest that the proposed system is appropriate for visual surveillance with increased accuracy and lower cost and processing resources. Full article
(This article belongs to the Special Issue Recent Research in Positioning and Activity Recognition Systems)
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17 pages, 10338 KiB  
Article
Diffuse Reflection Effects in Visible Light Positioning: Analysis, Modeling, and Evaluation
by Yuanpeng Zhang, Xiansheng Yang, Xiao Sun, Yaxin Wang, Tianbing Ma and Yuan Zhuang
Electronics 2023, 12(17), 3646; https://doi.org/10.3390/electronics12173646 - 29 Aug 2023
Cited by 1 | Viewed by 1676
Abstract
Currently, the Global Positioning System (GPS) is widely used, but its signal is attenuated by factors such as trees, walls, and ceilings, which severely degrade its positioning accuracy. To fill the gap, various indoor positioning techniques have attracted increasing attention in recent years. [...] Read more.
Currently, the Global Positioning System (GPS) is widely used, but its signal is attenuated by factors such as trees, walls, and ceilings, which severely degrade its positioning accuracy. To fill the gap, various indoor positioning techniques have attracted increasing attention in recent years. Visible light positioning (VLP) is a promising scheme for indoor positioning due to its high precision, high security, and low energy consumption; however, ubiquitous diffuse reflection affects the accuracy and robustness of VLP. During our testing, we found that diffuse reflection could increase the error in RSS values by 20~30%, severely affecting VLP accuracy; however, diffuse reflection is inevitable in real positioning environments. To solve this problem, this paper first establishes a wall diffuse reflection model and then implements a visible light positioning system based on an Internet of Things platform. Finally, this paper uses the system to verify the effectiveness of the diffuse reflection model. The experiments show that the proposed model effectively improves positioning accuracy by 36.7~61.3%. Full article
(This article belongs to the Special Issue Recent Research in Positioning and Activity Recognition Systems)
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