scholarly journals Generalized Seismic Phase Detection with Deep Learning

2018 ◽  
Vol 108 (5A) ◽  
pp. 2894-2901 ◽  
Author(s):  
Zachary E. Ross ◽  
Men‐Andrin Meier ◽  
Egill Hauksson ◽  
Thomas H. Heaton
Keyword(s):  
Deep Learning ◽  
Phase Detection

scholarly journals Convolutional Neural Network for Breathing Phase Detection in Lung Sounds

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1798 ◽  
Author(s):  
Cristina Jácome ◽  
Johan Ravn ◽  
Einar Holsbø ◽  
Juan Carlos Aviles-Solis ◽  
Hasse Melbye ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Evaluation Methods ◽  
Phase Detection ◽  
Lung Sounds ◽  
Lung Sound ◽  
Average Sensitivity ◽  
Sound Recordings ◽  
Level Performance

We applied deep learning to create an algorithm for breathing phase detection in lung sound recordings, and we compared the breathing phases detected by the algorithm and manually annotated by two experienced lung sound researchers. Our algorithm uses a convolutional neural network with spectrograms as the features, removing the need to specify features explicitly. We trained and evaluated the algorithm using three subsets that are larger than previously seen in the literature. We evaluated the performance of the method using two methods. First, discrete count of agreed breathing phases (using 50% overlap between a pair of boxes), shows a mean agreement with lung sound experts of 97% for inspiration and 87% for expiration. Second, the fraction of time of agreement (in seconds) gives higher pseudo-kappa values for inspiration (0.73–0.88) than expiration (0.63–0.84), showing an average sensitivity of 97% and an average specificity of 84%. With both evaluation methods, the agreement between the annotators and the algorithm shows human level performance for the algorithm. The developed algorithm is valid for detecting breathing phases in lung sound recordings.

scholarly journals Deep learning for seismic phase detection and picking in the aftershock zone of 2008 M7.9 Wenchuan Earthquake

2019 ◽  
Vol 293 ◽  
pp. 106261 ◽  
Author(s):  
Lijun Zhu ◽  
Zhigang Peng ◽  
James McClellan ◽  
Chenyu Li ◽  
Dongdong Yao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wenchuan Earthquake ◽  
Phase Detection

scholarly journals An Acceleration Based Fusion of Multiple Spatiotemporal Networks for Gait Phase Detection

2020 ◽  
Vol 17 (16) ◽  
pp. 5633 ◽  
Author(s):  
Tao Zhen ◽  
Lei Yan ◽  
Jian-lei Kong
Keyword(s):  
Deep Learning ◽  
Information Acquisition ◽  
Gait Recognition ◽  
Medical Rehabilitation ◽  
Identification Accuracy ◽  
Sensor Data ◽  
Human Gait ◽  
Phase Detection ◽  
Feature Extractor ◽  
Gait Phase

Human-gait-phase-recognition is an important technology in the field of exoskeleton robot control and medical rehabilitation. Inertial sensors with accelerometers and gyroscopes are easy to wear, inexpensive and have great potential for analyzing gait dynamics. However, current deep-learning methods extract spatial and temporal features in isolation—while ignoring the inherent correlation in high-dimensional spaces—which limits the accuracy of a single model. This paper proposes an effective hybrid deep-learning framework based on the fusion of multiple spatiotemporal networks (FMS-Net), which is used to detect asynchronous phases from IMU signals. More specifically, it first uses a gait-information acquisition system to collect IMU sensor data fixed on the lower leg. Through data preprocessing, the framework constructs a spatial feature extractor with CNN module and a temporal feature extractor, combined with LSTM module. Finally, a skip-connection structure and the two-layer fully connected layer fusion module are used to achieve the final gait recognition. Experimental results show that this method has better identification accuracy than other comparative methods with the macro-F1 reaching 96.7%.

scholarly journals Hybrid Deep-Learning Framework Based on Gaussian Fusion of Multiple Spatiotemporal Networks for Walking Gait Phase Recognition

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Tao Zhen ◽  
Jian-lei Kong ◽  
Lei Yan
Keyword(s):  
Deep Learning ◽  
Em Algorithm ◽  
Information Acquisition ◽  
Dimensional Space ◽  
Human Gait ◽  
Phase Detection ◽  
Feature Maps ◽  
Learning Framework ◽  
Feature Extractor ◽  
Gait Phase

Human gait phase detection is a significance technology for robotics exoskeletons control and exercise rehabilitation therapy. Inertial Measurement Units (IMUs) with accelerometer and gyroscope are convenient and inexpensive to collect gait data, which are often used to analyze gait dynamics for personal daily applications. However, current deep-learning methods that extract spatial and the isolated temporal features can easily ignore the correlation that may exist in the high-dimensional space, which limits the recognition effect of a single model. In this study, an effective hybrid deep-learning framework based on Gaussian probability fusion of multiple spatiotemporal networks (GFM-Net) is proposed to detect different gait phases from multisource IMU signals. Furthermore, it first employs the gait information acquisition system to collect IMU data fixed on lower limb. With the data preprocessing, the framework constructs a spatial feature extractor with AutoEncoder and CNN modules and a multistream temporal feature extractor with three collateral modules combining RNN, LSTM, and GRU modules. Finally, the novel Gaussian probability fusion module optimized by the Expectation-Maximum (EM) algorithm is developed to integrate the different feature maps output by the three submodels and continues to realize gait recognition. The framework proposed in this paper implements the inner loop that also contains the EM algorithm in the outer loop and optimizes the reverse gradient in the entire network. Experiments show that this method has better performance in gait classification with accuracy reaching more than 96.7%.

scholarly journals Real-Time Tool Detection for Workflow Identification in Open Cranial Vault Remodeling

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 817
Author(s):  
Alicia Pose Díez de la Lastra ◽  
Lucía García-Duarte Sáenz ◽  
David García-Mato ◽  
Luis Hernández-Álvarez ◽  
Santiago Ochandiano ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Surgical Simulation ◽  
Cranial Vault ◽  
Network Architectures ◽  
Phase Detection ◽  
Surgical Tools ◽  
3D Printed ◽  
Learning Architectures ◽  
Cranial Vault Remodeling

Deep learning is a recent technology that has shown excellent capabilities for recognition and identification tasks. This study applies these techniques in open cranial vault remodeling surgeries performed to correct craniosynostosis. The objective was to automatically recognize surgical tools in real-time and estimate the surgical phase based on those predictions. For this purpose, we implemented, trained, and tested three algorithms based on previously proposed Convolutional Neural Network architectures (VGG16, MobileNetV2, and InceptionV3) and one new architecture with fewer parameters (CranioNet). A novel 3D Slicer module was specifically developed to implement these networks and recognize surgical tools in real time via video streaming. The training and test data were acquired during a surgical simulation using a 3D printed patient-based realistic phantom of an infant’s head. The results showed that CranioNet presents the lowest accuracy for tool recognition (93.4%), while the highest accuracy is achieved by the MobileNetV2 model (99.6%), followed by VGG16 and InceptionV3 (98.8% and 97.2%, respectively). Regarding phase detection, InceptionV3 and VGG16 obtained the best results (94.5% and 94.4%), whereas MobileNetV2 and CranioNet presented worse values (91.1% and 89.8%). Our results prove the feasibility of applying deep learning architectures for real-time tool detection and phase estimation in craniosynostosis surgeries.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning
Sign in / Sign up

Export Citation Format

Share Document