Fall Detection With UWB Radars and CNN-LSTM Architecture

Maître, Julien; Bouchard, Kévin; Gaboury, Sébastien

doi:10.1109/jbhi.2020.3027967

Cited by 64 publications

(25 citation statements)

References 40 publications

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“…Commonly used deep learning methods include convolutional neural networks [ 24 ], recurrent neural networks [ 25 ], and long short-term memory neural networks [ 26 ]. For example, Maitre et al [ 27 ] proposed a fall behavior recognition algorithm based on a hybrid CNN and LSTM model. The model adopts a two-layer structure.…”

Section: Related Workmentioning

confidence: 99%

Fall Detection for Shipboard Seafarers Based on Optimized BlazePose and LSTM

Liu

et al. 2022

Sensors

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Aiming to avoid personal injury caused by the failure of timely medical assistance following a fall by seafarer members working on ships, research on the detection of seafarer’s falls and timely warnings to safety officers can reduce the loss and severe consequences of falls to seafarers. To improve the detection accuracy and real-time performance of the seafarer fall detection algorithm, a seafarer fall detection algorithm based on BlazePose–LSTM is proposed. This algorithm can automatically extract the human body key point information from the video image obtained by the vision sensor, analyze its internal data correlation characteristics, and realize the process from RGB camera image processing to seafarer fall detection. This fall detection algorithm extracts the human body key point information through the optimized BlazePose human body key point information extraction network. In this section, a new method for human bounding-box acquisition is proposed. In this study, a head detector based on the Vitruvian theory was used to replace the pre-trained SSD body detector in the BlazePose preheating module. Simultaneously, an offset vector is proposed to update the bounding box obtained. This method can reduce the frequency of repeated use of the head detection module. The algorithm then uses the long short-term memory neural network to detect seafarer falls. After extracting fall and related behavior data from the URFall public data set and FDD public data set to enrich the self-made data set, the experimental results show that the algorithm can achieve 100% accuracy and 98.5% specificity for the seafarer’s falling behavior, indicating that the algorithm has reasonable practicability and strong generalization ability. The detection frame rate can reach 29 fps on a CPU, which can meet the effect of real-time detection. The proposed method can be deployed on common vision sensors.

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Section: Related Workmentioning

confidence: 99%

Fall Detection for Shipboard Seafarers Based on Optimized BlazePose and LSTM

Liu

et al. 2022

Sensors

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“…In 2020, Bhattacharya and Vaughan [29] used spectrograms as input of CNN to distinguish falling and non-falling. In the same year, Maitre et al [30] and Erol et al [31] used multiple radar sensors for HAR to solve the problem that a single radar sensor could only be used in a small range. Hochreiter et al [32] proposed a long short-term memory network (LSTM)) to solve the problem of gradient vanishing and gradient explosion.…”

Section: Related Workmentioning

confidence: 99%

Human Activity Recognition Based on Non-Contact Radar Data and Improved PCA Method

Zhao

Zhou

et al. 2022

Applied Sciences

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Human activity recognition (HAR) can effectively improve the safety of the elderly at home. However, non-contact millimeter-wave radar data on the activities of the elderly is often challenging to collect, making it difficult to effectively improve the accuracy of neural networks for HAR. We addressed this problem by proposing a method that combines the improved principal component analysis (PCA) and the improved VGG16 model (a pre-trained 16-layer neural network model) to enhance the accuracy of HAR under small-scale datasets. This method used the improved PCA to enhance features of the extracted components and reduce the dimensionality of the data. The VGG16 model was improved by deleting the complex Fully-Connected layers and adding a Dropout layer between them to prevent the loss of useful information. The experimental results show that the accuracy of our proposed method on HAR is 96.34%, which is 4.27% higher after improvement, and the training time of each round is 10.88 s, which is 12.8% shorter than before.

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“…However, there are still many open problems related to the low signal-to-noise ratio, high aspect angles, obstacles, dynamic environments, discrimination of very similar activities, non-focal motion, and the large and timevarying nature of human activities. Overcoming the challenges in healthcare and biomedical applications requires further advancement of the state of the art in statistical signal processing and machine learning techniques (Maitre et al, 2020).…”

Section: Radar For Biomedicine and E-healthcarementioning

confidence: 99%

Grand Challenges in Radar Signal Processing

Gini

2021

Front. Signal Process.

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Fall Detection With UWB Radars and CNN-LSTM Architecture

Cited by 64 publications

References 40 publications

Fall Detection for Shipboard Seafarers Based on Optimized BlazePose and LSTM

Fall Detection for Shipboard Seafarers Based on Optimized BlazePose and LSTM

Human Activity Recognition Based on Non-Contact Radar Data and Improved PCA Method

Grand Challenges in Radar Signal Processing

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