Joint Activity Recognition and Indoor Localization With WiFi Fingerprints

Wang, Fei; Feng, Jianwei; Zhao, Yangyang; Zhang, Xiaobin; Zhang, Shiyuan; Han, Jinsong

doi:10.1109/access.2019.2923743

Cited by 129 publications

(100 citation statements)

References 45 publications

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“…With success of mmWave FMCW signals for human sensing, commercial WiFi signals, especially CSI measurements from commercial 802.11n chipsets at low frequency (2.4 GHz) bands, were trained via supervised learning or cross-modal deep learning for human sensing tasks such as device-free localization, activity recognition, fall detection, personal identification, emotion sensing, and skeleton tracking [52]- [62]. Most recently, [60] used annotations from camera images to train fine-grained CSI measurements over 30 subcarriers and 5 frames from 3 transmitting and 3 receiving antennas. The cross-modal deep learning approach showed the great potential of commercial WiFi signals for sensing applications.…”

Section: ) Human Sensingmentioning

confidence: 99%

Fingerprinting-Based Indoor Localization With Commercial MMWave WiFi: A Deep Learning Approach

et al. 2020

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Existing fingerprint-based indoor localization uses either fine-grained channel state information (CSI) from the physical layer or coarse-grained received signal strength indicator (RSSI) measurements. In this paper, we propose to use a mid-grained intermediate-level channel measurement-spatial beam signal-to-noise ratios (SNRs) that are inherently available and defined in the IEEE 802.11ad/ay standardsto construct the fingerprinting database. These intermediate channel measurements are further utilized by a deep learning approach for multiple purposes: 1) location-only classification; 2) simultaneous locationand-orientation classification; and 3) direct coordinate estimation. Furthermore, the effectiveness of the framework is thoroughly validated by an in-house experimental platform consisting of 3 access points using commercial-off-the-shelf millimeter-wave WiFi routers. The results show a 100% accuracy if the location is only interested, about 99% for simultaneous location-and-orientations classification, and an averaged root mean-square error (RMSE) of 11.1 cm and an average median error of 9.5 cm for direct coordinate estimate, greater than 2-fold improvements over the RMSE of 28.7 cm and median error of 23.6 cm for RSSI-like single SNR-based localization.

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Section: ) Human Sensingmentioning

confidence: 99%

Fingerprinting-Based Indoor Localization With Commercial MMWave WiFi: A Deep Learning Approach

et al. 2020

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“…Accuracy is reported to be high (90% and up) when using CSI and excluding RSSI. Wang et al [16] applied a residual neural network (ResNet [5]) consisting of multiple ResNet layers to a dual-task CNN for both activity recognition and indoor localization for 6 activities at 16 indoor locations. An accuracy was reported of 88% and 95% was reported for activity recognition and indoor localization, respectively.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, research often looks into applying remote sensing in this field. It has been shown that indoor localization [6,16,18], measuring physiological signals [11,13,17,20], human identification [2,12], and general human activity recognition/gesture detection [1,7,21,22] are achievable by using CSI. The performances of such systems is comparable to the existing wearable wireless sensor systems.…”

Section: Introductionmentioning

confidence: 99%

“…Deep learning is applied more often due to increasingly more powerful hardware. Especially convolutional neural networks (CNNs) have proven to be effective in interpreting CSI [1,6,12,16,19,22]. A CNN takes an input for which the spatial and structural information is important (usually an image) and filters it through multiple convolutional or supportive layers (pooling or dropout) in order to extract features and ultimately classify or predict this information based on these features.…”

Section: Introductionmentioning

confidence: 99%

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Scaling Activity Recognition Using Channel State Information Through Convolutional Neural Networks and Transfer Learning

Brinke

Meratnia

2019

Proceedings of the First International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of T

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Unobtrusive sensing is receiving much attention in recent years, as it is less obtrusive and more privacy-aware compared to other monitoring technologies. Human activity recognition is one of the fields in which unobtrusive sensing is heavily researched" as this is especially important in health care. In this regard, investigating WiFi signals, and more specifically 802.11n channel state information, is one of the more prominent research fields. However, there is a challenge in scaling it up. Transfer learning is rarely applied, and when applied, it is done on filtered/modified data or extracted features. This paper focuses on two aspects. First, convolutional networks are used across multiple participants, days and activities and analysis is done based on these results. Secondly, it looks into the possibility of applying transfer learning based on raw channel state information over multiple participants and activities over multiple days. Results show channel state information is accurate for single participants (F 1-score of 0.90), but sensitive to different participants and fluctuating WiFi signals over days (F 1-score of 0.25-0.35). Furthermore, results show both clustering and transfer learning can be applied to increase the performance to 0.80 when using minimal resources and retraining. CCS CONCEPTS • Computer systems organization → Sensor networks; • Humancentered computing → Ubiquitous and mobile computing; • Computing methodologies → Cross-validation.

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“…Compared with RSSI, CSI has a certain multipath resolution and can sense weak fluctuations of signals on the propagation path. CSI also has a higher sensitivity, a wider sensing range, and stronger sensing reliability [7][8][9][10]. Of course, CSI can also be used in more fine-grained detection, such as gesture recognition using CSI and sleep monitoring using CSI [11,12].…”

Section: Introductionmentioning

confidence: 99%

Discrete Hopfield neural network based indoor Wi-Fi localization using CSI

Dang

Tang

Hao

et al. 2020

J Wireless Com Network

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The fingerprint indoor localization method based on channel state information (CSI) has gained widespread attention. However, this method fails to provide a better localization effect and higher localization accuracy due to poor fingerprint accuracy, unsatisfactory classification and matching effect, and vulnerability to environmental impacts. In order to solve the problem, this paper proposes a CSI fingerprint indoor localization method based on the Discrete Hopfield Neural Network (DHNN). The method mainly consists of off-line and on-line phases. At the off-line phase, a low-pass filter is applied to conduct a preliminary processing on the fingerprint information of each reference point, and then, phase difference is adopted to correct the fingerprint data of all reference points. In this way, the quality of fingerprint data is improved, hence avoiding problems such as indoor environmental changes and multipath effect of signals, etc. in which impact the fingerprint data. Finally, the characteristic fingerprint database is established after acquiring relatively accurate fingerprint data. At the on-line phase, to maintain the consistency of data, the data of each reference point in the fingerprint database is set as an attractor. Meanwhile, the localization information of the test point is processed to make convergence judgment through DHNN. Eventually, the localization result is obtained. The experimental results show that the localization accuracy with a median error of 1.6 m can be achieved through the proposed method in the experimental environment. Compared with similar methods, it has a higher stability which can significantly reduce the cost of manpower and time.

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Joint Activity Recognition and Indoor Localization With WiFi Fingerprints

Cited by 129 publications

References 45 publications

Fingerprinting-Based Indoor Localization With Commercial MMWave WiFi: A Deep Learning Approach

Fingerprinting-Based Indoor Localization With Commercial MMWave WiFi: A Deep Learning Approach

Scaling Activity Recognition Using Channel State Information Through Convolutional Neural Networks and Transfer Learning

Discrete Hopfield neural network based indoor Wi-Fi localization using CSI

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