Neural Networks for Indoor Human Activity Reconstructions

Tariq, Osama Bin; Lazarescu, Mihai T.; Lavagno, Luciano

doi:10.1109/jsen.2020.3006009

Cited by 11 publications

(5 citation statements)

References 56 publications

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“…In this section, we summarize some related works about human posture or activity recognition and localization applications in indoor environments. In the first stage of research on localization, researchers investigated active schemes that require the target to carry a wearable device, such as a motion sensor or beacon [30][31][32][33][34][35]. For instance, Wang et al [33] proposed a single person fall detection system, namely WiFall, which can be used to monitor elderly people with physical conditions when they stay at home alone.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Device-Free Localization Scheme for Simultaneous Estimation of Indoor Location and Posture Using FMCW Radars

Lee

Park

Kim

2022

Sensors

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Indoor device-free localization (DFL) systems are used in various Internet-of-Things applications based on human behavior recognition. However, the usage of camera-based intuitive DFL approaches is limited in dark environments and disaster situations. Moreover, camera-based DFL schemes exhibit certain privacy issues. Therefore, DFL schemes with radars are increasingly being investigated owing to their efficient functioning in dark environments and their ability to prevent privacy issues. This study proposes a deep learning-based DFL scheme for simultaneous estimation of indoor location and posture using 24-GHz frequency-modulated continuous-wave (FMCW) radars. The proposed scheme uses a parallel 1D convolutional neural network structure with a regression and a classification model for localization and posture estimation, respectively. The two-dimensional location information of the target is estimated for localization, and four different postures, namely standing, sitting, lying, and absence, are estimated simultaneously. We experimentally evaluated the proposed scheme and compared its performance with that of conventional schemes under identical conditions. The results indicate that the average localization error of the proposed scheme is 0.23 m, whereas that of the conventional scheme is approximately 0.65 m. The average posture estimation error of the proposed scheme is approximately 1.7%, whereas that of the conventional correlation, CSP, and SVM schemes are 54.8%, 42%, and 10%, respectively.

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Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Device-Free Localization Scheme for Simultaneous Estimation of Indoor Location and Posture Using FMCW Radars

Lee

Park

Kim

2022

Sensors

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“…Chen et al [36] proposed a deep long short-term memory (LSTM) algorithm to learn high-level representations of the extracted RSS features and conducted the experiments in two different scenarios to verify the effectiveness of the algorithm. Tariq et al [37] tested and analyzed several neural networks for localization and concluded that the LSTM could have a comparable performance with the lowest processing effort and fewest network parameters. Among these deep learning-based tracking algorithms, researchers concentrated on the spatial correlation locations of mobile users to improve the localization performance.…”

Section: Deepmentioning

confidence: 99%

Wi‐Fi Fingerprint‐Based Indoor Mobile User Localization Using Deep Learning

Bai

Sun

Meng

et al. 2021

Wireless Communications and Mobile Computing

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In recent years, deep learning has been used for Wi-Fi fingerprint-based localization to achieve a remarkable performance, which is expected to satisfy the increasing requirements of indoor location-based service (LBS). In this paper, we propose a Wi-Fi fingerprint-based indoor mobile user localization method that integrates a stacked improved sparse autoencoder (SISAE) and a recurrent neural network (RNN). We improve the sparse autoencoder by adding an activity penalty term in its loss function to control the neuron outputs in the hidden layer. The encoders of three improved sparse autoencoders are stacked to obtain high-level feature representations of received signal strength (RSS) vectors, and an SISAE is constructed for localization by adding a logistic regression layer as the output layer to the stacked encoders. Meanwhile, using the previous location coordinates computed by the trained SISAE as extra inputs, an RNN is employed to compute more accurate current location coordinates for mobile users. The experimental results demonstrate that the mean error of the proposed SISAE-RNN for mobile user localization can be reduced to 1.60 m.

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“…Capacitive sensing functions by detecting changes in the capacitive coupling between tracked targets and custom sensors embedded within the surrounding walls [ 25 ] or flooring [ 26 ]. In floor-based implementations, the presence of tracked targets feet acts as a capacitive plate, coupling with a floor-embedded sensor to form a capacitor.…”

Section: Introductionmentioning

confidence: 99%

Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks

Konings

Alam

Faulkner

et al. 2022

Sensors

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In recent publications, capacitive sensing floors have been shown to be able to localize individuals in an unobtrusive manner. This paper demonstrates that it might be possible to utilize the walking characteristics extracted from a capacitive floor to recognize subject and gender. Several neural network-based machine learning techniques are developed for recognizing the gender and identity of a target. These algorithms were trained and validated using a dataset constructed from the information captured from 23 subjects while walking, alone, on the sensing floor. A deep neural network comprising a Bi-directional Long Short-Term Memory (BLSTM) provided the most accurate identity performance, classifying individuals with an accuracy of 98.12% on the test data. On the other hand, a Convolutional Neural Network (CNN) was the most accurate for gender recognition, attaining an accuracy of 93.3%. The neural network-based algorithms are benchmarked against Support Vector Machine (SVM), which is a classifier used in many reported works for floor-based recognition tasks. The majority of the neural networks outperform SVM across all accuracy metrics.

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Neural Networks for Indoor Human Activity Reconstructions

Cited by 11 publications

References 56 publications

Deep Learning-Based Device-Free Localization Scheme for Simultaneous Estimation of Indoor Location and Posture Using FMCW Radars

Deep Learning-Based Device-Free Localization Scheme for Simultaneous Estimation of Indoor Location and Posture Using FMCW Radars

Wi‐Fi Fingerprint‐Based Indoor Mobile User Localization Using Deep Learning

Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks

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