Gait and Respiration-Based User Identification Using Wi-Fi Signal

Wang, Xiaoyang; Li, Fan; Xie, Yadong; Yang, Song; Wang, Yu

doi:10.1109/jiot.2021.3097892

Cited by 15 publications

(4 citation statements)

References 39 publications

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“…The accuracy of our proposed method is higher than the other three methods. Our method is 3:7% higher than the traditional VMD scheme, 6:7% higher than the literature [16], and 4:3 % higher than the literature [17], respectively.…”

Section: Person Identification Accuracymentioning

confidence: 69%

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RF-Gait: Gait-Based Person Identification with COTS RFID

Jiang

Wang

et al. 2022

Wireless Communications and Mobile Computing

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Recently, person identification has been a prerequisite in many applications of the Internet of Things. As a new biometric identification technology, gait recognition has a wide application prospect with the advantages of long-distance recognition and difficulty to forge. However, the existing gait recognition methods have some problems, such as complex algorithm calculation, high user participation, and large equipment overhead. In this paper, we propose RF-Gait, a method that identifies a person through unobtrusive gait perception with COTS RFID. The key insight is that wireless signal fluctuation can be exploited to distinguish each person’s unique gait behavior. To this end, we first collect and preprocess the gait-induced data composed of multiple RFID tags. Furthermore, multivariate variational mode decomposition is utilized to extract the intrinsic features in the spatial multichannels cooperatively. By developing a support vector machine model, we identify a person via the intrinsic walking pattern. Finally, extensive experiments show that our method can identify a person with an average accuracy of 96.3% from a group of twenty persons in a complex indoor environment.

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Section: Person Identification Accuracymentioning

confidence: 69%

“…Furthermore, we compare RF-Gait with variational mode decomposition and two state-of-art gait-based person identification methods [16,17] which are empirical mode decomposition-based methods in Figure 11. The accuracy of our proposed method is higher than the other three methods.…”

Section: Person Identification Accuracymentioning

confidence: 99%

RF-Gait: Gait-Based Person Identification with COTS RFID

Jiang

Wang

et al. 2022

Wireless Communications and Mobile Computing

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“…However, Wi-Fi sensing is still a developing area of research and there are still many challenges to overcome. The two main identified problems are related to performance [8,14,15] and user convenience [16][17][18][19]. The accuracy ranges from 90-80%, often reducing as the number of participants increases-90% was only for a small number of participants, that is, around 11 people.…”

Section: Introductionmentioning

confidence: 99%

“…HumanFi [16] asks the user to collect 40 samples of walking back and forth in a straight line; Lin et al [18] asked for 140 h of data collection of 20 different types of human activity. Wang et al [17] explored the combination of gait and breathing; however, they required 18 samples from each participant standing for 15 s per sample. In the studies that have been conducted, background interference was not considered, and it is currently not known how the accuracy might improve in a controlled environment.…”

Section: Introductionmentioning

confidence: 99%

Classifying Participant Standing and Sitting Postures Using Channel State Information

Custance,

Khan,

Parkinson

2023

Electronics

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Recently, channel state information (CSI) has been identified as beneficial in a wide range of applications, ranging from human activity recognition (HAR) to patient monitoring. However, these focused studies have resulted in data that are limited in scope. In this paper, we investigate the use of CSI data obtained from an ESP32 microcontroller to identify participants from sitting and standing postures in a many-to-one classification. The test is carried out in a controlled isolated environment to establish whether a pre-trained model can distinguish between participants. A total of 15 participants were recruited and asked to sit and stand between the transmitter (Tx) and the receiver (Rx), while their CSI data were recorded. Various pre-processing algorithms and techniques have been incorporated and tested on different classification algorithms, which have gone through parameter selection to enable a consistent testing template. Performance metrics such as the confusion matrix, accuracy, and elapsed time were captured. After extensive evaluation and testing of different classification models, it has been established that the hybrid LSTM-1DCNN model has an average accuracy of 84.29% and 74.13% for sitting and standing postures, respectively, in our dataset. The models were compared with the BedroomPi dataset and it was found that LSTM-1DCNN was the best model in terms of performance. It is also the most efficient model with respect to the time elapsed to sit and stand.

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A driver authentication system integrated to stress-level determination for driving safety

Esener

2023

Soft Comput

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Gait and Respiration-Based User Identification Using Wi-Fi Signal

Cited by 15 publications

References 39 publications

RF-Gait: Gait-Based Person Identification with COTS RFID

RF-Gait: Gait-Based Person Identification with COTS RFID

Classifying Participant Standing and Sitting Postures Using Channel State Information

A driver authentication system integrated to stress-level determination for driving safety

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