A Millimetre-Wave Radar-Based Fall Detection Method Using Line Kernel Convolutional Neural Network

Wang, Bo; Guo, Liang; Zhang, Hao; Guo, Yong‐Xin

doi:10.1109/jsen.2020.3006918

Cited by 54 publications

(25 citation statements)

References 10 publications

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“…Therefore, it is necessary to remove low-frequency and high-frequency noise for obtaining good detection. Wang et al [30] used a single stage canceller to filter out lowfrequency noise, but high-frequency noise and clutter will still exist and would affect the detection accuracy. In [31], the stationary and non-stationary clutters were removed by employing the singular value decomposition (SVD) algorithm when the signal-to-noise ratio (SNR) is low.…”

Section: Data Preprocessingmentioning

confidence: 99%

A convolution neural network approach for fall detection based on adaptive channel selection of UWB radar signals

Wang

Yin

et al. 2022

Neural Comput & Applic

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According to the World Health Organization and other authorities, falls are one of the main causes of accidental injuries among the elderly population. Therefore, it is essential to detect and predict the fall activities of older persons in indoor environments such as homes, nursing, senior residential centers, and care facilities. Due to non-contact and signal confidentiality characteristics, radar equipment is widely used in indoor care, detection, and rescue. This paper proposes an adaptive channel selection algorithm to separate the activity signals from the background using an ultra-wideband radar and to generalize fused features of frequency- and time-domain images which will be sent to a lightweight convolutional neural network to detect and recognize fall activities. The experimental results show that the method is able to distinguish three types of fall activities (i.e., stand to fall, bow to fall, and squat to fall) and obtain a high recognition accuracy up to 95.7%.

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Section: Data Preprocessingmentioning

confidence: 99%

A convolution neural network approach for fall detection based on adaptive channel selection of UWB radar signals

Wang

Yin

et al. 2022

Neural Comput & Applic

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“…The method achieved better accuracy, precision, sensitivity, and specificity, which is considered to be the best performance than other methods. Su et al 65 proposed a threshold-based FDS. The wavelet transform is used to identify the fall from other activities.…”

Section: Radar-based Fall Detectionmentioning

confidence: 99%

“…Su et al 65 proposed a threshold‐based FDS. The wavelet transform is used to identify the fall from other activities.…”

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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“…This work is based on the AI on edge paradigm. Commercialization of single-chip mmwave radar sensors in the last decade has created opportunities in various fields such as healthcare [4], high resolution radar imaging [5], human activity recognition [6], and many more. Camera sensors can also be used for accurate human activity recognition [7].…”

Section: Introductionmentioning

confidence: 99%

mmFiT: Contactless Fitness Tracker Using mmWave Radar and Edge Computing Enabled Deep Learning

Tiwari¹,

Bajaj²,

Gupta³

2023

Preprint

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Internet of things (IoT) is transforming the way we imagine healthcare with ubiquitous connectivity, faster response and deeper personalized insights using large amounts of data. Fitness trackers provide useful insights to maintain balance of a healthy lifestyle. Nowadays, fitness trackers are available as wearable devices which creates a sense of unease in exercise and may cause skin irritation. In this paper, we present mmFiT, an edge computing enabled, contactless, real-time fitness tracker using a single mmwave radar point cloud data. It has the inherent advantage of user privacy preservation while tracking indoor fitness activities. Experimental results show that the system can classify various exercises with real-time accuracy of 95.53\% and is also capable of counting repetitions of exercises. This implementation is computationally inexpensive, and therefore, the system can be deployed in an IoT connected edge device for real-time operations. This system will be an ideal fit in a smart home or smart gymnasium setting.

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A Millimetre-Wave Radar-Based Fall Detection Method Using Line Kernel Convolutional Neural Network

Cited by 54 publications

References 10 publications

A convolution neural network approach for fall detection based on adaptive channel selection of UWB radar signals

A convolution neural network approach for fall detection based on adaptive channel selection of UWB radar signals

A systematic review on machine learning for fall detection system

mmFiT: Contactless Fitness Tracker Using mmWave Radar and Edge Computing Enabled Deep Learning

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