Human Activity Recognition Using Self-Powered Sensors Based on Multilayer Bidirectional Long Short-Term Memory Networks

Su, Jian; Liao, Zhenlong; Sheng, Zhengguo; Liu, Alex X.; Singh, Dilbag; Lee, Heung-No

doi:10.1109/jsen.2022.3195274

Cited by 11 publications

(5 citation statements)

References 33 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Channel state information (CSI) provides information about the amplitude and phase of the transmitted signals so that we can be aware of changes in Wi-Fi signals, including signal scattering, ambient attenuation, environmental fading (including multipath fading and shadow fading), and power decays because of distance in each transmission path during propagation [ 21 ]. Using CSI in this context instead of RSSI has many advantages, including more sustainability, reduced environmental influences, and increased transmission of information [ 3 , 22 ]. In addition, OFDM technology can be used in Wi-Fi devices, and the bandwidth can be divided between several orthogonal subcarriers by using the IEEE 802.11 n/ac standard.…”

Section: System Methodsmentioning

confidence: 99%

CSI-Based Human Activity Recognition Using Multi-Input Multi-Output Autoencoder and Fine-Tuning

Chahoushi

Nabati

Asvadi

et al. 2023

Sensors

View full text Add to dashboard Cite

Wi-Fi-based human activity recognition (HAR) has gained considerable attention recently due to its ease of use and the availability of its infrastructures and sensors. Channel state information (CSI) captures how Wi-Fi signals are transmitted through the environment. Using channel state information of the received signals transmitted from Wi-Fi access points, human activity can be recognized with more accuracy compared with the received signal strength indicator (RSSI). However, in many scenarios and applications, there is a serious limit in the volume of training data because of cost, time, or resource constraints. In this study, multiple deep learning models have been trained for HAR to achieve an acceptable accuracy level while using less training data compared to other machine learning techniques. To do so, a pretrained encoder which is trained using only a limited number of data samples, is utilized for feature extraction. Then, by using fine-tuning, this encoder is utilized in the classifier, which is trained by a fraction of the rest of the data, and the training is continued alongside the rest of the classifier’s layers. Simulation results show that by using only 50% of the training data, there is a 20% improvement compared with the case where the encoder is not used. We also showed that by using an untrainable encoder, an accuracy improvement of 11% using 50% of the training data is achievable with a lower complexity level.

show abstract

Section: System Methodsmentioning

confidence: 99%

CSI-Based Human Activity Recognition Using Multi-Input Multi-Output Autoencoder and Fine-Tuning

Chahoushi

Nabati

Asvadi

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Compared with the above methods, several studies [22][23][24][25] have shown that WiFibased human action recognition has been favored by many researchers in recent years due to many advantages such as wide coverage, strong privacy protection, low price, the ability to detect non-visual distance, and no need to wear or touch. The WiFi-based human action recognition mainly relies on two signals: (1) the received signal strength indicator (RSSI); and (2) channel state information (CSI).…”

Section: Introductionmentioning

confidence: 99%

SM-TCNNET: A High-Performance Method for Detecting Human Activity Using WiFi Signals

Gao

Zhu

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Human activity recognition (HAR) is an important research area with a wide range of application scenarios, such as smart homes, healthcare, abnormal behavior detection, etc. Wearable sensors, computer vision, radar, and other technologies are commonly used to detect human activity. However, they are severely limited by issues such as cost, lighting, context, and privacy. Therefore, this paper explores a high-performance method of using channel state information (CSI) to identify human activities, which is a deep learning-based spatial module-temporal convolutional network (SM-TCNNET) model. The model consists of a spatial feature extraction module and a temporal convolutional network (TCN) that can extract the spatiotemporal features in CSI signals well. In this paper, extensive experiments are conducted on the self-picked dataset and the public dataset (StanWiFi), and the results show that the accuracy reaches 99.93% and 99.80%, respectively. Compared with the existing methods, the recognition accuracy of the SM-TCNNET model proposed in this paper is improved by 1.8%.

show abstract

“…The paper [8] proposed a fall detection system WiFall to achieve an accuracy of 87% of users' fall detection. The paper [9] proposes an activity recognition model based on a three-layer bidirectional long-and short-term memory network fused with attention to classify and recognize six activities in the open dataset in the paper [10] , and the experimental results show that the recognition rate can reach 96%. The paper [11] proposed an attentional temporal convolutional network by extracting continuous CSI time-domain features, and verified its advanced recognition rate in multiple datasets.…”

Section: Introductionmentioning

confidence: 99%

Human activity recognition based on CSI in multiple scenarios

Zhang

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

View full text Add to dashboard Cite

Due to the popularity of commercial WiFi devices prompting the use of WiFi devices to achieve indoor human activity recognition(HAR) has become possible, HAR plays an important role in human-computer interaction, virtual reality and meta-universe, human walking, running and other activities will have an impact on the propagation of WiFi signals, by analyzing the information of the affected channel state, WiFi-based HAR can be achieved. In this paper, human activity states of three environments are classified, firstly by Hampel filtering to remove the anomaly noise, after that by DWT to further reduce the noise interference, then by extracting the statistical features in the time-frequency domain, and finally by random forest classification to complete the classification recognition. Through experimental verification, the method proposed in this paper can achieve 92% and 91% recognition rate in laboratory scene and corridor scene, which is better than the traditional algorithm recognition rate. It can also achieve 89% accurate recognition rate in non-line-of-sight(NLOS) environment, which also confirms the robustness of the proposed algorithm to the environment.

show abstract

Human Activity Recognition Using Self-Powered Sensors Based on Multilayer Bidirectional Long Short-Term Memory Networks

Cited by 11 publications

References 33 publications

CSI-Based Human Activity Recognition Using Multi-Input Multi-Output Autoencoder and Fine-Tuning

CSI-Based Human Activity Recognition Using Multi-Input Multi-Output Autoencoder and Fine-Tuning

SM-TCNNET: A High-Performance Method for Detecting Human Activity Using WiFi Signals

Human activity recognition based on CSI in multiple scenarios

Contact Info

Product

Resources

About