SlideAugment: A Simple Data Processing Method to Enhance Human Activity Recognition Accuracy Based on WiFi

Li, Junyan; Yin, Kang; Tang, Chengpei

doi:10.3390/s21062181

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…Past research work on gesture recognition was divided into three main categories: sensor-based [ 2 ], vision-based [ 3 ], and Wi-Fi-based [ 4 , 5 ]. In sensor-based systems, limb motion features are captured by body-worn sensors.…”

Section: Introductionmentioning

confidence: 99%

UltrasonicGS: A Highly Robust Gesture and Sign Language Recognition Method Based on Ultrasonic Signals

Wang

Hao

Dang

et al. 2023

Sensors

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With the global spread of the novel coronavirus, avoiding human-to-human contact has become an effective way to cut off the spread of the virus. Therefore, contactless gesture recognition becomes an effective means to reduce the risk of contact infection in outbreak prevention and control. However, the recognition of everyday behavioral sign language of a certain population of deaf people presents a challenge to sensing technology. Ubiquitous acoustics offer new ideas on how to perceive everyday behavior. The advantages of a low sampling rate, slow propagation speed, and easy access to the equipment have led to the widespread use of acoustic signal-based gesture recognition sensing technology. Therefore, this paper proposed a contactless gesture and sign language behavior sensing method based on ultrasonic signals—UltrasonicGS. The method used Generative Adversarial Network (GAN)-based data augmentation techniques to expand the dataset without human intervention and improve the performance of the behavior recognition model. In addition, to solve the problem of inconsistent length and difficult alignment of input and output sequences of continuous gestures and sign language gestures, we added the Connectionist Temporal Classification (CTC) algorithm after the CRNN network. Additionally, the architecture can achieve better recognition of sign language behaviors of certain people, filling the gap of acoustic-based perception of Chinese sign language. We have conducted extensive experiments and evaluations of UltrasonicGS in a variety of real scenarios. The experimental results showed that UltrasonicGS achieved a combined recognition rate of 98.8% for 15 single gestures and an average correct recognition rate of 92.4% and 86.3% for six sets of continuous gestures and sign language gestures, respectively. As a result, our proposed method provided a low-cost and highly robust solution for avoiding human-to-human contact.

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Section: Introductionmentioning

confidence: 99%

UltrasonicGS: A Highly Robust Gesture and Sign Language Recognition Method Based on Ultrasonic Signals

Wang

Hao

Dang

et al. 2023

Sensors

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“…In nearest neighbor interpolation, the value at the nearest sample grid point is taken as a new value at the query points [203]. Furthermore, 50% overlapping was done to overcome the loss of information while segmenting for further processing [204]. Furthermore, both the analysis were performed by using four commonly used convolutional neural network (CNN) architectures: ResNet-50 [205], MobileNet-V2 [206], NASnetmobile [207], xception [208].…”

Section: Wi-gitation: Baseline Performancementioning

confidence: 99%

“…Thus, a matrix of dimension 270 channels × 12000 samples (only CSI amplitude) were obtained for each activity and each participant. Since the aim of this paper is to showcase exploratory analysis, minimum pre-processing steps including nearest neighbor interpolation to make samples consistent [231] and sliding window with 50% overlap to overcome the information loss, if any, during segmentation in further processing [232] were applied.…”

Section: Dataset Used and Analysis Protocolmentioning

confidence: 99%

Sensing the care: advancing unobtrusive sensing solutions to support informal caregivers of older adults with cognitive impairment

Sharma

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Wi-Gitation: Replica Wi-Fi CSI Dataset for Physical Agitation Activity Recognition

Sharma,

Klein Brinke,

Braakman Jansen

et al. 2023

Data

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Agitation is a commonly found behavioral condition in persons with advanced dementia. It requires continuous monitoring to gain insights into agitation levels to assist caregivers in delivering adequate care. The available monitoring techniques use cameras and wearables which are distressful and intrusive and are thus often rejected by older adults. To enable continuous monitoring in older adult care, unobtrusive Wi-Fi channel state information (CSI) can be leveraged to monitor physical activities related to agitation. However, to the best of our knowledge, there are no realistic CSI datasets available for facilitating the classification of physical activities demonstrated during agitation scenarios such as disturbed walking, repetitive sitting–getting up, tapping on a surface, hand wringing, rubbing on a surface, flipping objects, and kicking. Therefore, in this paper, we present a public dataset named Wi-Gitation. For Wi-Gitation, the Wi-Fi CSI data were collected with twenty-three healthy participants depicting the aforementioned agitation-related physical activities at two different locations in a one-bedroom apartment with multiple receivers placed at different distances (0.5–8 m) from the participants. The validation results on the Wi-Gitation dataset indicate higher accuracies (F1-Scores ≥0.95) when employing mixed-data analysis, where the training and testing data share the same distribution. Conversely, in scenarios where the training and testing data differ in distribution (i.e., leave-one-out), the accuracies experienced a notable decline (F1-Scores ≤0.21). This dataset can be used for fundamental research on CSI signals and in the evaluation of advanced algorithms developed for tackling domain invariance in CSI-based human activity recognition.

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SlideAugment: A Simple Data Processing Method to Enhance Human Activity Recognition Accuracy Based on WiFi

Cited by 4 publications

References 31 publications

UltrasonicGS: A Highly Robust Gesture and Sign Language Recognition Method Based on Ultrasonic Signals

UltrasonicGS: A Highly Robust Gesture and Sign Language Recognition Method Based on Ultrasonic Signals

Sensing the care: advancing unobtrusive sensing solutions to support informal caregivers of older adults with cognitive impairment

Wi-Gitation: Replica Wi-Fi CSI Dataset for Physical Agitation Activity Recognition

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