An evaluation method of channel state information fingerprinting for single gateway indoor localization

Berruet, Brieuc; Baala, Oumaya; Caminada, Alexandre; Guillet, Valéry

doi:10.1016/j.jnca.2020.102591

Cited by 13 publications

(9 citation statements)

References 39 publications

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“…WiFi-based motion tracking relies on RSSI or the more detailed CSI to realize activity detection [ 13 , 27 , 28 , 29 , 30 , 31 ], gesture recognition [ 32 , 33 , 34 ] and tracking [ 35 , 36 , 37 , 38 , 39 , 40 ]. WiFit [ 41 ] enables people to practice three kinds of freehand exercises with the body on the line-of-sight between Wi-Fi transceivers.…”

Section: Related Workmentioning

confidence: 99%

Recognizing and Counting Freehand Exercises Using Ubiquitous Cellular Signals

Teng

Hong

et al. 2021

Sensors

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Freehand exercises help improve physical fitness without any requirements for devices or places. Existing fitness assistant systems are typically restricted to wearable devices or exercising at specific positions, compromising the ubiquitous availability of freehand exercises. In this paper, we develop MobiFit, a contactless freehand exercise assistant using just one cellular signal receiver placed on the ground. MobiFit passively monitors the ubiquitous cellular signals sent by the base station, which frees users from the space constraints and deployment overheads and provides accurate repetition counting, exercise type recognition and workout quality assessment without any attachments to the human body. The design of MobiFit faces new challenges of the uncertainties not only on cellular signal payloads but also on signal propagations because the sender (base station) is beyond the control of MobiFit and located far away. To tackle these challenges, we conducted experimental studies to observe the received cellular signal sequence during freehand exercises. Based on the observations, we constructed the analytic model of the received signals. Guided by the insights derived from the analytic model, MobiFit segments out every repetition and rest interval from one exercise session through spectrogram analysis and extracts low-frequency features from each repetition for type recognition. Extensive experiments were conducted in both indoor and outdoor environments, which collected 22,960 exercise repetitions performed by ten volunteers over six months. The results confirm that MobiFit achieves high counting accuracy of 98.6%, high recognition accuracy of 94.1% and low repetition duration estimation error within 0.3 s. Besides, the experiments show that MobiFit works both indoors and outdoors and supports multiple users exercising together.

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Section: Related Workmentioning

confidence: 99%

Recognizing and Counting Freehand Exercises Using Ubiquitous Cellular Signals

Teng

Hong

et al. 2021

Sensors

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“…IPS differ in technology, signal measurement, and localization techniques tailored to suit the environment and particular use-case requirements [ 8 ]. Radiofrequency-based technologies [ 11 ], specifically Wi-Fi [ 12 , 13 , 14 , 15 , 16 ], Radio Frequency Identification (RFID) [ 17 , 18 ], and Bluetooth Low Energy (BLE) [ 19 , 20 , 21 ], are typically preferred considering flexibility to setup and integration with IoT. Recently, studies on Wi-FI have been optimizing line of sight (LOS) and non-line of sight (NLOS) [ 22 , 23 ] approaches, including combination with vision [ 24 ] for localization.…”

Section: Introductionmentioning

confidence: 99%

Relabeling for Indoor Localization Using Stationary Beacons in Nursing Care Facilities

Garcia,

Inoue

2024

Sensors

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In this study, we propose an augmentation method for machine learning based on relabeling data in caregiving and nursing staff indoor localization with Bluetooth Low Energy (BLE) technology. Indoor localization is used to monitor staff-to-patient assistance in caregiving and to gain insights into workload management. However, improving accuracy is challenging when there is a limited amount of data available for training. In this paper, we propose a data augmentation method to reuse the Received Signal Strength (RSS) from different beacons by relabeling to the locations with less samples, resolving data imbalance. Standard deviation and Kullback–Leibler divergence between minority and majority classes are used to measure signal pattern to find matching beacons to relabel. By matching beacons between classes, two variations of relabeling are implemented, specifically full and partial matching. The performance is evaluated using the real-world dataset we collected for five days in a nursing care facility installed with 25 BLE beacons. A Random Forest model is utilized for location recognition, and performance is compared using the weighted F1-score to account for class imbalance. By increasing the beacon data with our proposed relabeling method for data augmentation, we achieve a higher minority class F1-score compared to augmentation with Random Sampling, Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). Our proposed method utilizes collected beacon data by leveraging majority class samples. Full matching demonstrated a 6 to 8% improvement from the original baseline overall weighted F1-score.

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“…Peng et al 47 developed a multiway kernel entropy independent component analysis algorithm to formulate more accurate model for process monitoring so as to enhance the monitoring performance based on KECA. Berruet et al 48 put forward an evaluation method using KECA to guide the design of future fingerprinting solutions based on channel state information. However, the abovementioned KECA-based feature extraction procedure requires a large number of components (i.e.…”

Section: Introductionmentioning

confidence: 99%

Acoustic emission and moving window-improved kernel entropy component analysis for structural condition monitoring of hoisting machinery under various working conditions

Yang

2021

Structural Health Monitoring

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Acoustic emission (AE) has been widely used to the nondestructive evaluation (NDE) and structural health monitoring (SHM) of hoisting machinery recently. Kernel entropy component analysis (KECA) is generally applied to extract the AE features based on its excellent nonlinear ability. However, traditional KECA specifically requires a considerable number of components (e.g. eigenvalues and eigenvectors) to excellently describe the original data, which leads to a reduction in the effect of approximate dimensionality reduction of high-dimensional data, thus causing readily unacceptable condition monitoring result. To overcome this weakness, a novel method named moving window-improved kernel entropy component analysis (MW-IKECA) is proposed in this study for structural condition monitoring of hoisting machinery, which is aimed at extracting more AE feature information and improving the condition identification accuracy. Firstly, a twiddle factor is introduced in the KECA model for the purpose of breaking the restriction that the projection axes originate only from the feature vectors and maximizing the independence between the components. Meanwhile, the moving window local strategy is incorporated into the proposed IKECA to extract more rich and effectiveness AE feature information at different scales. Finally, the Cauchy–Schwarz (CS) statistic is utilized to calculate the similarity between probability density functions and maintain the angular structure of the MW-IKECA feature space for the task of improving the monitoring accuracy and shortening the monitoring time-delay of MW-IKECA. Results of the experimental and practical engineering application validate the effectiveness and superiority of the proposed method in AE-based crane SHM under different working conditions compared with the traditional KECA and some combinatorial methods.

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An evaluation method of channel state information fingerprinting for single gateway indoor localization

Cited by 13 publications

References 39 publications

Recognizing and Counting Freehand Exercises Using Ubiquitous Cellular Signals

Recognizing and Counting Freehand Exercises Using Ubiquitous Cellular Signals

Relabeling for Indoor Localization Using Stationary Beacons in Nursing Care Facilities

Acoustic emission and moving window-improved kernel entropy component analysis for structural condition monitoring of hoisting machinery under various working conditions

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