RETRACTED ARTICLE: A new localization technique for node positioning in wireless sensor networks

Sivasakthiselvan, S.; Nagarajan, V.

doi:10.1007/s10586-018-2613-1

Cited by 24 publications

(10 citation statements)

References 13 publications

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“…Through the experimental study of muscle fatigue, the characteristic parameters analyze the characterization effect of muscle fatigue. Through experiments, ARC1 is compared with traditional frequency domain parameters MPF and MF to prove the feasibility and effectiveness of ARC1 in the field of evaluating muscle fatigue [ 26 ].…”

Section: Experiments and Discussionmentioning

confidence: 99%

Intelligent Scanning Detection System of Muscle Exercise Fatigue Based on Surface Electromyography

Wang

2022

Scanning

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In order to use the surface EMG signal to automatically detect the muscle fatigue state, a research method of the muscle exercise fatigue intelligent scanning detection system based on surface EMG was proposed, and the sEMG signal features of 10 subjects before and after fatigue were extracted. A time-varying parameter autoregressive model is established. By introducing the Legendre basis function, the parameter identification of the linear nonstationary process is transformed into the parameter identification of the linear time-invariant system. Combined with the correlation index, the optimal Legendre base function dimension of the time-varying system parameter estimation can be obtained, then the best model fitting effect can be obtained, and the time-invariant parameters are solved by the least square method. Using the rate of change of the first time-varying parameter (ARC1) of the autoregressive model before and after fatigue as an index to detect muscle fatigue sensitivity, a two-tailed t test was used to compare the mean power frequency (MPF) and the median frequency (MF) with the rate of change. The results showed that the change rates of ARC1, MPF, and MF before and after fatigue were 34.33 % ± 2.5 % , 68 % + 2.03 % , and 22.80 % + 2.19 % , which were 41% and 25%, respectively. The rate of change of ACR1 was significantly higher than that of MPF and MF ( P < 0.05 ). When detecting muscle fatigue by sEMG signal, it has the advantages of short time and high sensitivity. It can be used for online real-time analysis of muscle fatigue, providing a potential analysis tool for limb muscle strain, rehabilitation, and ergonomics assessment.

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Section: Experiments and Discussionmentioning

confidence: 99%

Intelligent Scanning Detection System of Muscle Exercise Fatigue Based on Surface Electromyography

Wang

2022

Scanning

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“…The study of this system also has implications for exploring the use of wireless sensor networks in addition to the entertainment field, motion recognition, and precise motion recognition [15]. [16,17]. The energy consumption model of WSN communication used in this paper includes the case of energy consumption of transmit data module and receive data module.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Using Wireless Sensor Network to Correct Posture in Sports Training Based on Hidden Markov Matching Algorithm

Cui

2021

Journal of Sensors

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This paper combines the research of wireless sensor networks and sports training and proposes a wireless sensor network-based intelligent sports training system. According to the requirements of the system, this design uses the wireless sensor network system as the platform for development and the ZigBee module for wireless communication. The advantage of this system is to transmit the obtained information to the ZigBee coordinator module, and after the processing of information and the resultant decision, a nonwearable unmonitored motion training model based on visual sensing is proposed. The motion terminal collects video data streams of user motion actions and extracts features to establish HMM motion recognition algorithm to achieve recognition of motion actions, automatic counting, and intelligent scoring functions. The template matching algorithm based on dynamic time regularization and weighted Euclidean distance realizes a universal real-time motion recognition algorithm with high standard and low latency and can guide the user’s motion action based on similarity calculation. The intelligent sports training system is designed and developed to maintain a high-quality human-computer interaction experience with a real-time feedback client and uploads sports data to a cloud server via the HTTP protocol, which supports real-time sports proximity query and training plan development on the website. After practical application tests, the intelligent sports training system based on the wireless sensor network proposed in this paper is stable and reliable and adds fun and competitiveness to boring sports. The research of this paper has some reference value for the application of wireless sensor networks and the research of the motion recognition algorithm.

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“…As the estimate position is close to the real position, the localization algorithm is as good. As in [63], the requirement on the resolution of the positional accuracy depends on applications. In this section, we discuss the performance of our proposed algorithm and compare it with the original DV-Hop algorithm-literatures [57,64,65] algorithms.…”

Section: Simulation and Resultsmentioning

confidence: 99%

Online Sequential DV-Hop Localization Algorithm for Wireless Sensor Networks

Messous

Liouane

2020

Mobile Information Systems

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One of the main issues of wireless sensor networks is localization. Besides, it is important to track and analyze the sensed information. The technique of localization can calculate node position with the help of a set of designed nodes, denoted as anchors. The set density of these anchors may be incremented or decremented because of many reasons such as maintenance, lifetime, and breakdown. The well-known Distance Vector Hop (DV-Hop) algorithm is a suitable solution for localizing nodes having few neighbor anchors. However, existing DV-Hop-based localization methods have not considered the problem of anchor breakdown which may happen during the localization process. In order to avoid this issue, an Online Sequential DV-Hop algorithm is proposed in this paper to sequentially calculate positions of nodes and improve accuracy of node localization for multihop wireless sensor networks. The algorithm deals with the variation of the number of available anchors in the network. We note that DV-Hop algorithm is used in this article to process localization of nodes by a new optimized method for the estimation of the average distance of hops between nodes. Our proposed localization method is based on an online sequential computation. Compared with the original DV-Hop and other localization methods from the literature, simulation results prove that the proposed algorithm greatly minimizes the average of localization error of sensor nodes.

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RETRACTED ARTICLE: A new localization technique for node positioning in wireless sensor networks

Cited by 24 publications

References 13 publications

Intelligent Scanning Detection System of Muscle Exercise Fatigue Based on Surface Electromyography

Intelligent Scanning Detection System of Muscle Exercise Fatigue Based on Surface Electromyography

[Retracted] Using Wireless Sensor Network to Correct Posture in Sports Training Based on Hidden Markov Matching Algorithm

Online Sequential DV-Hop Localization Algorithm for Wireless Sensor Networks

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