Human Activity Recognition Using Smartphones With WiFi Signals

Lin, Guiping; Jiang, Weiwei; Xu, Sicong; Zhou, Xiaobo; Guo, Xing; Zhu, Yujun; He, Xin

doi:10.1109/thms.2022.3188726

Cited by 26 publications

(5 citation statements)

References 38 publications

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“…The system achieved high accuracy in recognizing different scales of motions, demonstrating its usefulness in gesture recognition. Comparisons were made with three machine learning models, including the convolutional neural network (CNNs), decision tree, and long short-term memory [16]. Sakorn Mekruksavanich et al introduced a versatile framework for human activity recognition using WiFi-based sensing and CSI data.…”

Section: Rf Sensing Methodsmentioning

confidence: 99%

Enhancing Human Activity Recognition with LoRa Wireless RF Signal Preprocessing and Deep Learning

Nie,

Zou,

Cui

et al. 2024

Electronics

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This paper introduces a novel approach for enhancing human activity recognition through the integration of LoRa wireless RF signal preprocessing and deep learning. We tackle the challenge of extracting features from intricate LoRa signals by scrutinizing the unique propagation process of linearly modulated LoRa signals—a critical aspect for effective feature extraction. Our preprocessing technique involves converting intricate data into real numbers, utilizing Short-Time Fourier Transform (STFT) to generate spectrograms, and incorporating differential signal processing (DSP) techniques to augment activity recognition accuracy. Additionally, we employ frequency-to-image conversion for the purpose of intuitive interpretation. In comprehensive experiments covering activity classification, identity recognition, room identification, and presence detection, our carefully selected deep learning models exhibit outstanding accuracy. Notably, ConvNext attains 96.7% accuracy in activity classification, 97.9% in identity recognition, and 97.3% in room identification. The Vision TF model excels with 98.5% accuracy in presence detection. Through leveraging LoRa signal characteristics and sophisticated preprocessing techniques, our transformative approach significantly enhances feature extraction, ensuring heightened accuracy and reliability in human activity recognition.

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Section: Rf Sensing Methodsmentioning

confidence: 99%

Enhancing Human Activity Recognition with LoRa Wireless RF Signal Preprocessing and Deep Learning

Nie,

Zou,

Cui

et al. 2024

Electronics

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“…It is worth noting that data segmentation is very important for processing the collected CSI human activity data. There are many articles that have innovatively proposed data sample segmentation methods [ 35 ]. Signal statistical characteristics of amplitude and phase can be used as the segmentation standard.…”

Section: Data Pre-preparementioning

confidence: 99%

A Novel Lightweight Human Activity Recognition Method Via L-CTCN

Ding,

Li,

et al. 2023

Sensors

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Wi-Fi-based human activity recognition has attracted significant attention. Deep learning methods are widely used to achieve feature representation and activity sensing. While more learnable parameters in the neural networks model lead to richer feature extraction, it results in significant resource consumption, rendering the model unsuitable for lightweight Internet of Things (IoT) devices. Furthermore, the sensing performance heavily relies on the quality and quantity of data, which is a time-consuming and labor-intensive task. Therefore, there is a need to explore methods that reduce the dependence on the quality and quantity of the dataset while ensuring recognition performance and decreasing model complexity to adapt to ubiquitous lightweight IoT devices. In this paper, we propose a novel Lightweight-Complex Temporal Convolution Network (L-CTCN) for human activity recognition. Specifically, this approach effectively combines complex convolution with a Temporal Convolution Network (TCN). Complex convolution can extract richer information from limited raw complex data, reducing the reliance on the quality and quantity of training samples. Based on the designed TCN framework with 1D convolution and residual blocks, the proposed model can achieve lightweight human activity recognition. Extensive experiments verify the effectiveness of the proposed method. We can achieve an average recognition accuracy of 96.6% with only 0.17 M parameter size. This method performs well under conditions of low sampling rates and a low number of subcarriers and samples.

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“…Motion of humans and objects during HAR and HAP are sensed by tracking and analyzing the changes in the reflected signal caused by variation in wave propagation paths due to the movement. The data gathered using Wi-Fi transmission show high quality and are used in machine learning algorithms [46][47][48]. Data are organized in the form of channel state information (CSI) matrix, which holds information on properties of the communication signal such as phase shifts, power decay, etc.…”

Section: Wi-fi Devicesmentioning

confidence: 99%

Predicting Human Activity – State of the Art

Awong¹,

Zielińska²

2023

PAR

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Predicting human actions is a very actual research field. Artificial intelligence methods are commonly used here. They enable early recognition and classification of human activities. Such knowledge is extremely needed in the work on robots and other interactive systems that communicate and cooperate with people. This ensures early reactions of such devices and proper planning of their future actions. However, due to the complexity of human actions, predicting them is a difficult task. In this article, we review state-of-the-art methods and summarize recent advances in predicting human activity. We focus in particular on four approaches using machine learning methods, namely methods using: artificial neural networks, support vector machines, probabilistic models and decision trees. We discuss the advantages and disadvantages of these approaches, as well as current challenges related to predicting human activity. In addition, we describe the types of sensors and data sets commonly used in research on predicting and recognizing human actions. We analyze the quality of the methods used, based on the prediction accuracy reported in scientific articles. We describe the importance of the data type and the parameters of machine learning models. Finally, we summarize the latest research trends. The article is intended to help in choosing the right method of predicting human activity, along with an indication of the tools and resources necessary to effectively achieve this goal.

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Human Activity Recognition Using Smartphones With WiFi Signals

Cited by 26 publications

References 38 publications

Enhancing Human Activity Recognition with LoRa Wireless RF Signal Preprocessing and Deep Learning

Enhancing Human Activity Recognition with LoRa Wireless RF Signal Preprocessing and Deep Learning

A Novel Lightweight Human Activity Recognition Method Via L-CTCN

Predicting Human Activity – State of the Art

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