RF-Based Fall Monitoring Using Convolutional Neural Networks

Tian, Yonglong; Lee, Guang-He; He, Hao; Hsu, Chen-Yang; Katabi, Dina

doi:10.1145/3264947

Cited by 135 publications

(63 citation statements)

References 34 publications

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“…It currently achieves the best accuracy on this task. We use Aryokee [33] as a representative of the state-of-the-art in RF-based action recognition. To our knowledge, this is the only past RF-based action recognition system that performs action detection in addition to classification.…”

Section: Comparison Of Different Modelsmentioning

confidence: 99%

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Making the Invisible Visible: Action Recognition Through Walls and Occlusions

Li¹,

Fan²,

Zhao³

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Self Cite

120

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Understanding people's actions and interactions typically depends on seeing them. Automating the process of action recognition from visual data has been the topic of much research in the computer vision community. But what if it is too dark, or if the person is occluded or behind a wall? In this paper, we introduce a neural network model that can detect human actions through walls and occlusions, and in poor lighting conditions. Our model takes radio frequency (RF) signals as input, generates 3D human skeletons as an intermediate representation, and recognizes actions and interactions of multiple people over time. By translating the input to an intermediate skeleton-based representation, our model can learn from both vision-based and RF-based datasets, and allow the two tasks to help each other. We show that our model achieves comparable accuracy to vision-based action recognition systems in visible scenarios, yet continues to work accurately when people are not visible, hence addressing scenarios that are beyond the limit of today's vision-based action recognition.

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Section: Comparison Of Different Modelsmentioning

confidence: 99%

“…If one can interpret such radio reflections, one can perform action recognition through walls and occlusions. Indeed, some research on wireless systems has attempted to leverage this property for action recognition [33,39,19,1,37]. However, existing radio-based action recognition systems lag significantly behind vision-based systems.…”

Section: Introductionmentioning

confidence: 99%

Making the Invisible Visible: Action Recognition Through Walls and Occlusions

Li¹,

Fan²,

Zhao³

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Self Cite

120

View full text Add to dashboard Cite

show abstract

“…In the end, they used a deep belief network to build a model based on the low and high-level features. Yonglong et al [23] proposed a radio frequency-based fall monitoring system based on CNN. They introduce Aryokee, which is based on radio frequency to detect fall using CNN.…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting Video Surveillance Using VGG19 Convolutional Neural Networks

Butt¹,

Letchmunan²,

Hafinaz³

et al. 2020

IJACSA

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The meteoric growth of data over the internet from the last few years has created a challenge of mining and extracting useful patterns from a large dataset. In recent years, the growth of digital libraries and video databases makes it more challenging and important to extract useful information from raw data to prevent and detect the crimes from the database automatically. Street crime snatching and theft detection is the major challenge in video mining. The main target is to select features/objects which usually occurs at the time of snatching. The number of moving targets imitates the performance, speed and amount of motion in the anomalous video. The dataset used in this paper is Snatch 101; the videos in the dataset are further divided into frames. The frames are labelled and segmented for training. We applied the VGG19 Convolutional Neural Network architecture algorithm and extracted the features of objects and compared them with original video features and objects. The main contribution of our research is to create frames from the videos and then label the objects. The objects are selected from frames where we can detect anomalous activities. The proposed system is never used before for crime prediction, and it is computationally efficient and effective as compared to state-of-the-art systems. The proposed system outperformed with 81 % accuracy as compared to stateof-the-art systems.

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“…The dynamic algorithm keeps on updating and improving on usage. Tian et al [27] used a CNN along with a state machine to determine the state sequences for fall detection and duration based on radio frequency signals. WiFall [28] is a WiFi sensing-based fall detection system, which utilizes frequency information to detect sharp falls with the help of SVM classifier.…”

Section: Introductionmentioning

confidence: 99%

“…WiFi sensing-based monitoring systems only require a WiFi router or access point and one or more WiFi enabled devices. WiFi sensing with CSI measurements have been used in various applications, such as human presence/localization [20][21][22], activity recognition [23][24][25], fall classification and detection [26][27][28][29], gesture recognition [30][31][32], and user identification [33][34][35].Recent work has leveraged WiFi sensing for human presence detection and localization. Qian et al[20] used a WiFi-based Multiple Inputs and Multiple Outputs (MIMO) system and CSI measurements to detect presence of humans with dynamic movement speeds utilizing a Support Vector Machine (SVM), resulting in a true positive rate greater than 93%.…”

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confidence: 99%

WiFreeze: Multiresolution Scalograms for Freezing of Gait Detection in Parkinson’s Leveraging 5G Spectrum with Deep Learning

et al. 2019

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Freezing of Gait (FOG) is an episodic absence of forward movement in Parkinson's Disease (PD) patients and represents an onset of disabilities. FOG hinders daily activities and increases fall risk. There is high demand for automating the process of FOG detection due to its impact on health and well being of individuals. This work presents WiFreeze, a noninvasive, line of sight, and lighting agnostic WiFi-based sensing system, which exploits ambient 5G spectrum for detection and classification of FOG. The core idea is to utilize the amplitude variations of wireless Channel State Information (CSI) to differentiate between FOG and activities of daily life. A total of 225 events with 45 FOG cases are captured from 15 patients with the help of 30 subcarriers and classification is performed with a deep neural network. Multiresolution scalograms are proposed for time-frequency signatures of human activities, due to their ability to capture and detect transients in CSI signals caused by transitions in human movements. A very deep Convolutional Neural Network (CNN), VGG-8K, with 8K neurons each, in fully connected layers is engineered and proposed for transfer learning with multiresolution scalogram features for detection of FOG. The proposed WiFreeze system outperforms all existing wearable and vision-based systems as well as deep CNN architectures with the highest accuracy of 99.7% for FOG detection. Furthermore, the proposed system provides the highest classification accuracies of 94.3% for voluntary stop and 97.6% for walking slow activities, with improvements of 9% and 23%, respectively, over the best performing state-of-the-art deep CNN architecture.China [3]. Every 1 in 37 people in the United Kingdom are diagnosed with PD at some stage of their life [1]. PD is caused by the neuronal loss and damage in the motor area of the brain, particularly the centers of the limbic, visceromotor, and somatomotor systems [4]. Most PD patients experience difficulty in walking, which indicates an onset of gait, balance, and other disabilities [5]. Half of the PD patients experience Freezing of Gate (FOG) symptoms, which represent an episodic absence of forward feet movement, despite of the intention for forward progress [6]. The frequency as well as the duration of FOG episodes increases with the progression of PD. FOG and falls are both interconnected phenomena, and FOG is known to hinder daily activities and increase fall risk [7], resulting in severe consequences for older adults.Conventional clinical methods to validate FOG occurrences and their severity are based on self-report questionnaires [8] and doctors' reports based on direct observation. Due to the impact of FOG on population in general and falls in older adults in particular, there is a high demand for automating the process of detection and recognition of FOG events. Currently, various methods using wearable devices and vision-based systems have been exploited for FOG detection. A number of systems have been proposed with wearable sensors or cameras for FOG detectio...

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RF-Based Fall Monitoring Using Convolutional Neural Networks

Cited by 135 publications

References 34 publications

Making the Invisible Visible: Action Recognition Through Walls and Occlusions

Making the Invisible Visible: Action Recognition Through Walls and Occlusions

Detecting Video Surveillance Using VGG19 Convolutional Neural Networks

WiFreeze: Multiresolution Scalograms for Freezing of Gait Detection in Parkinson’s Leveraging 5G Spectrum with Deep Learning

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