A WiFi-Based Smart Home Fall Detection System Using Recurrent Neural Network

Ding, Jianyang; Wang, Yong

doi:10.1109/tce.2020.3021398

Cited by 67 publications

(31 citation statements)

References 37 publications

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“…Detection accuracy was increased by implementing different classification algorithms such as artificial neural network (ANN), K-nearest neighbors (KNN), quadratic support vector machine (QSVM), or ensemble bagged tree. Fall detection systems have been proposed that implement novel classification algorithms, e.g., adversarial data argumentation [ 27 ], recurrent neural networks [ 28 ], among others, which positively impact accuracy rates. However, this area has been under intensive research, while the sensing has been vaguely explored.…”

Section: General Structure Of a Wifi-based Fall Detection Systemmentioning

confidence: 99%

Influence of the Antenna Orientation on WiFi-Based Fall Detection Systems

Cardenas

Gutiérrez

Aguilar-Ponce

2021

Sensors

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The growing elderly population living independently demands remote systems for health monitoring. Falls are considered recurring fatal events and therefore have become a global health problem. Fall detection systems based on WiFi radio frequency signals still have limitations due to the difficulty of differentiating the features of a fall from other similar activities. Additionally, the antenna orientation has not been taking into account as an influencing factor of classification performance. Therefore, we present in this paper an analysis of the classification performance in relation to the antenna orientation and the effects related to polarization and radiation pattern. Furthermore, the implementation of a device-free fall detection platform to collect empirical data on falls is shown. The platform measures the Doppler spectrum of a probe signal to extract the Doppler signatures generated by human movement and whose features can be used to identify falling events. The system explores two antenna polarization: horizontal and vertical. The accuracy reached by horizontal polarization is 92% with a false negative rate of 8%. Vertical polarization achieved 50% accuracy and false negatives rate.

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Section: General Structure Of a Wifi-based Fall Detection Systemmentioning

confidence: 99%

Influence of the Antenna Orientation on WiFi-Based Fall Detection Systems

Cardenas

Gutiérrez

Aguilar-Ponce

2021

Sensors

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“…But the approach has considered only one person in conducting activities. Wu et al 60 focused to improve the accuracy of the model with the help of Wi‐Fi‐based human movement recognition. The conventional method like pattern‐based is compared with ML approach named model‐based solution.…”

Section: Review On Ml‐based Fall Detection Techniquementioning

confidence: 99%

A systematic review on machine learning for fall detection system

Rastogi

Singh

2021

Computational Intelligence

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Fall is a major threat to the health and life of the elders. A Fall Detection System (FDS) assist the elders by identifying the fall and save their life. Machine Learning-(ML) based FDS has turned into a major research area due to its capability to assist the elders automatically. The efficiency of a FDS depends on its strength to identify the fall from nonfall accurately. The initial fall detection scheme depends on the threshold-based classification to classify the fall from the Activity of Daily Living (ADL) but this classification method has failed to reduce the false alarm rate, which raises a question on the efficiency of the technique. This review work identifies the problems in threshold-based classification from existing works and finds the need for an efficient ML-based classification technique to accurately identify the fall. Then, presents a comprehensive literature review on various ML-based classification in fall detection. Moreover, the scrutiny investigates the shortcomings associated with the ML-based techniques for future research. This study finds that present ML-based FDS has not addressed problems like data preprocessing and data dimensionality reduction techniques even though ML-based techniques are far superior to threshold-based techniques.The study concludes that Self-Adaptive-based FDS, as

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“…However, the commercial application of deep learning algorithm is limited because of the long training time. To solve the problem, Ding et al [28] proposed a method to automatically identify the fall state by RNN. In the method, the collected data is uploaded to the proxy server which processes the data and identifies the fall state, and the client application obtains the processing result of the algorithm from the proxy server.…”

Section: Fall Detection Csi-based Human Activity Recognitionmentioning

confidence: 99%

Small CSI Samples-Based Activity Recognition: A Deep Learning Approach Using Multidimensional Features

Tian

Chen

et al. 2021

Security and Communication Networks

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With the emergence of tools for extracting CSI data from commercial WiFi devices, CSI-based device-free activity recognition technology has developed rapidly and has been widely used in security monitoring, smart home, medical monitoring, and other fields. However, the existing CSI-based activity recognition algorithms need a large number of training samples to obtain the ideal recognition accuracy. To solve the problem, an attention-based bidirectional LSTM method using multidimensional features (called MF-ABLSTM method) is proposed. In this method, the signal preprocessing and continuous wavelet transform algorithms are used to construct time-frequency matrix, the sample entropy is used to characterize the statistical feature of CSI amplitudes, the energy difference at a fixed time interval is used to characterize the time-domain feature of activities, and the energy distribution of different frequency components is used to characterize the frequency-domain feature of activities. By expanding the training samples with the proposed tensor prediction algorithm, the accurate activity recognition can be realized with only a few samples. A large number of experiments verify the good performance of MF-ABLSTM method.

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A WiFi-Based Smart Home Fall Detection System Using Recurrent Neural Network

Cited by 67 publications

References 37 publications

Influence of the Antenna Orientation on WiFi-Based Fall Detection Systems

Influence of the Antenna Orientation on WiFi-Based Fall Detection Systems

A systematic review on machine learning for fall detection system

Small CSI Samples-Based Activity Recognition: A Deep Learning Approach Using Multidimensional Features

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