Toward Low-Cost Passive Motion Tracking With One Pair of Commodity Wi-Fi Devices

Guo, Wei; Lei, Jing

doi:10.1109/jispin.2023.3287508

Cited by 6 publications

(4 citation statements)

References 42 publications

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“…Numerous research works have been conducted to address various aspects of the Internet of Things (IoT), encompassing energy harvesting, device-to-device communication, energy efficiency, resource allocation, edge computing, security, privacy, and applications across different domains. Scholars have explored stochastic geometry analysis to optimize energy harvesting, proposed energy-efficient systems, investigated untapped potential in spectrum utilization, developed reinforcement learning-based frameworks, and explored the integration of edge computing and artificial intelligence [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. Additionally, studies have focused on addressing security concerns in these areas, including privacy-preserving data sharing, explainable AI, and motion tracking.…”

Section: Introductionmentioning

confidence: 99%

Unleashing the Power of IoT: A Comprehensive Review of IoT Applications and Future Prospects in Healthcare, Agriculture, Smart Homes, Smart Cities, and Industry 4.0

Chataut,

Phoummalayvane,

Akl

2023

Sensors

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The Internet of Things (IoT) technology and devices represent an exciting field in computer science that is rapidly emerging worldwide. The demand for automation and efficiency has also been a contributing factor to the advancements in this technology. The proliferation of IoT devices coincides with advancements in wireless networking technologies, driven by the enhanced connectivity of the internet. Today, nearly any everyday object can be connected to the network, reflecting the growing demand for automation and efficiency. This paper reviews the emergence of IoT devices, analyzed their common applications, and explored the future prospects in this promising field of computer science. The examined applications encompass healthcare, agriculture, and smart cities. Although IoT technology exhibits similar deployment trends, this paper will explore different fields to discern the subtle nuances that exist among them. To comprehend the future of IoT, it is essential to comprehend the driving forces behind its advancements in various industries. By gaining a better understanding of the emergence of IoT devices, readers will develop insights into the factors that have propelled their growth and the conditions that led to technological advancements. Given the rapid pace at which IoT technology is advancing, this paper provides researchers with a deeper understanding of the factors that have brought us to this point and the ongoing efforts that are actively shaping the future of IoT. By offering a comprehensive analysis of the current landscape and potential future developments, this paper serves as a valuable resource to researchers seeking to contribute to and navigate the ever-evolving IoT ecosystem.

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Section: Introductionmentioning

confidence: 99%

Unleashing the Power of IoT: A Comprehensive Review of IoT Applications and Future Prospects in Healthcare, Agriculture, Smart Homes, Smart Cities, and Industry 4.0

Chataut,

Phoummalayvane,

Akl

2023

Sensors

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“…In the past, there has been a lot of research on recognizing motions, which can be categorized into research on wearable sensors and ambient sensors. Various wearable sensors have been studied, such as inertial sensors [2,6], myoelectric sensors [7][8][9][10][11], bending sensors [12,13], and ambient sensors [14,15]. Using sensors for motion recognition generally involves steps including sensor data collection, preprocessing and feature extraction, and classification recognition.…”

Section: Related Workmentioning

confidence: 99%

Recognizing Complex Activities by Combining Sequences of Basic Motions

Lu,

Hsu,

Jing

2024

Electronics

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For daily motion recognition, each researcher builds their own method to recognize their own specific target actions. However, for other types of target motions, they cannot use their method to recognize other kinds of motions because the features of their target motions that they extracted cannot be extracted from other kinds of motions. Therefore, we wanted to develop a general method that can be used in most kinds of motions. From our observations, we found that a meaningful motion is combined with some basic motions. Therefore, we could recognize basic motions and then combine them to recognize a target motion. First, we simply defined the basic motions according to the sensor’s basic sensing directions. Second, we used k-nearest neighbors (KNN) and dynamic time warping (DTW) to recognize different categories of basic motions. Then, we gave each basic motion a specific number to represent it, and finally, used continuous dynamic programming (CDP) to recognize a target motion by the sequence of basic motions we collected. In our experiment on our basic motions, the accuracy of all of the basic motions is higher than 80%, so the recognition of basic motions is reliable. Then, we performed an experiment for recognizing the target motions. The results of recognizing the target motions were not good, the average accuracy being only 65.9%, and we still have to improve our system. However, we also compared our system with recognizing motions by using another general recognition method, KNN. And the average accuracy of using KNN to recognize motions was 53.4%. As this result shows, our method still obtains better results in recognizing different kinds of motions than using KNN.

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“…However, GPS-based positioning encounters disruptions in underground or enclosed spaces and exhibits an error of approximately tens of meters, making it unsuitable for indoor positioning [7,8]. To achieve precise indoor positioning, it is necessary to utilize wireless signals with high accuracy [9].…”

Section: Introductionmentioning

confidence: 99%

Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator

Yoon,

Kim,

Kwon

2024

Applied Sciences

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In this paper, we propose a proximity-based adaptive positioning algorithm to address the challenge of positioning errors in indoor localization based on RSSI (received signal strength indicator). When positioning by trilateration, if a receiver is close to one AP, the signals of other APs become rapidly unstable, so positioning accuracy is reduced. Therefore, this paper proposes an algorithm to identify the proximity state with AP and adaptively determine the positioning technique based on this state. The proposed algorithm consists of three steps: RSSI error correction, adaptive location estimation, and post-processing. The RSSI error correction step corrects unstable RSSI. The adaptive location estimation step utilizes a modified proximity technique when identified as close to an AP, employing trilateration otherwise. Finally, in the post-processing step, an efficient filtering algorithm is applied. For the static state experiment, the accuracy of the proposed algorithm is found to be improved by about 28% compared to the method measured using only the trilateration technique applying the RSSI error correction step and post-processing step. The proposed algorithm improved the positioning accuracy of the entire area by improving accuracy in regions with weak signals without additional devices.

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Toward Low-Cost Passive Motion Tracking With One Pair of Commodity Wi-Fi Devices

Cited by 6 publications

References 42 publications

Unleashing the Power of IoT: A Comprehensive Review of IoT Applications and Future Prospects in Healthcare, Agriculture, Smart Homes, Smart Cities, and Industry 4.0

Unleashing the Power of IoT: A Comprehensive Review of IoT Applications and Future Prospects in Healthcare, Agriculture, Smart Homes, Smart Cities, and Industry 4.0

Recognizing Complex Activities by Combining Sequences of Basic Motions

Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator

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