scholarly journals A Neural Network for Stance Phase Detection in Smart Cane Users

2019 ◽  
pp. 310-321
Author(s):  
Juan Rafael Caro-Romero ◽  
Joaquin Ballesteros ◽  
Francisco Garcia-Lagos ◽  
Cristina Urdiales ◽  
Francisco Sandoval
Keyword(s):  
Neural Network ◽  
Stance Phase ◽  
Phase Detection

Development of Dermatological Skin Disease Detection and Classification based on Wavelet and ANN

2018 ◽  
Vol 8 (5) ◽  
pp. 10
Author(s):  
Revati Kadu ◽  
U. A. Belorkar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Skin Disease ◽  
Skin Diseases ◽  
Screening Method ◽  
Phase Detection ◽  
Statistical Measures ◽  
The World ◽  
Artificial Neural ◽  
Wavelet Techniques

One of the most common and augmenting health problems in the world are related to skin. The most  unpredictable and one of the most difficult entities to automatically detect and evaluate is the human skin disease because of complexities of texture, tone, presence of hair and other distinctive features. Many cases of skin diseases in the world have triggered a need to develop an effective automated screening method for detection and diagnosis of the area of disease. Therefore the objective of this work is to develop a new technique for automated detection and analysis of the skin disease images based on color and texture information for skin disease screening. In this paper, system is proposed which detects the skin diseases using Wavelet Techniques and Artificial Neural Network. This paper presents a wavelet-based texture analysis method for classification of five types of skin diseases. The method applies tree-structured wavelet transform on different color channels of red, green and blue dermoscopy images, and employs various statistical measures and ratios on wavelet coefficients. In all 99 unique features are extracted from the image. By using Artificial Neural Network, the system successfully detects different types of dermatological skin diseases. It consists of mainly three phases image processing, training phase, detection  and classification phase.

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 ZUPT-aided INS Bypassing Stance Phase Detection by Using Foot-Instability-Based Adaptive Covariance

2021 ◽  
Author(s):  
Chi-Shih Jao ◽  
Andrei M. Shkel
Keyword(s):  
Covariance Matrix ◽  
Stance Phase ◽  
Phase Detector ◽  
Vertical Position ◽  
Navigation Systems ◽  
Phase Detection ◽  
Zero Velocity ◽  
Position Errors ◽  
Series Of Experiments ◽  
Navigation Accuracy

<p>In this paper, we propose a Foot-Instability-Based Adaptive (FIBA) covariance to dynamically adjust the covariance matrix for the pseudo-zero-velocity measurements in the Zero velocity UPdaTe (ZUPT)-aided Inertial Navigation Systems (INS). The proposed ZUPT-aided INS using the FIBA covariance is implemented in an Adaptive Extended Kalman Filter (AEKF) framework, where the measurement covariance matrix is updated in each iteration according to the FIBA covariance. The FIBA covariance is designed to have a very high value during the swing phases in a gait cycle, and the value significantly decreases during the stance phases. As a result, the proposed method eliminates a need to use a binary stance phase detector in implementation of the ZUPT-aided INS. Two series of indoor pedestrian navigation experiments were conducted to investigate the navigation performance of the algorithm. In the first series of experiments, which included cases of walking and running, localization solutions produced by the system using the FIBA covariance demonstrated 36% and 64% improvements in navigation accuracy along the horizontal and vertical directions, respectively. In the second series of experiments, which included a pedestrian walking on different indoor terrains, such as flat planes, stairs, and ramps, the navigation accuracy of the system using the FIBA covariance reduced horizontal and vertical position errors by 12% and 45%, respectively, as compared to the conventional ZUPT-aided INS.</p>

SC-PSNET: A deep neural network for automatic P- and S-phase detection and arrival-time picker using 1C recordings

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. U87-U98
Author(s):  
Jing Zheng ◽  
Jerry M. Harris ◽  
Dongzhuo Li ◽  
Badr Al-Rumaih
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
Seismic Data ◽  
Arrival Time ◽  
Time Lapse ◽  
S Phase ◽  
Training Data ◽  
Phase Detector ◽  
Phase Detection ◽  
Seismic Data Processing

It is important to autopick an event’s arrival time and classify the corresponding phase for seismic data processing. Traditional arrival-time picking algorithms usually need 3C seismograms to classify event phase. However, a large number of borehole seismic data sets are recorded by arrays of hydrophones or distributed acoustic sensing elements whose sensors are 1C and cannot be analyzed for particle motion or phase polarization. With the development of deep learning techniques, researchers have tried data mining with the convolutional neural network (CNN) for seismic phase autopicking. In the previous work, CNN was applied to process 3C seismograms to detect phase and pick arrivals. We have extended this work to process 1C seismic data and focused on two main points. One is the effect of the label vector on the phase detection performance. The other is to propose an architecture to deal with the challenge from the insufficiency of training data in the coverage of different scenarios of [Formula: see text] ratios. Two novel points are summarized after this analysis. First, the width of the label vector can be designed through signal time-frequency analysis. Second, a combination of CNN and recurrent neural network architecture is more suitable for designing a P- and S-phase detector to deal with the challenge from the insufficiency of training data for 1C recordings in time-lapse seismic monitoring. We perform experiments and analysis using synthetic and field time-lapse seismic recordings. The experiments show that it is effective for 1C seismic data processing in time-lapse monitoring surveys.

MEMS Based Pedestrian Navigation System

2005 ◽  
Vol 59 (1) ◽  
pp. 135-153 ◽  
Author(s):  
Seong Yun Cho ◽  
Chan Gook Park
Keyword(s):  
Neural Network ◽  
Navigation System ◽  
Stance Phase ◽  
Finite Impulse Response ◽  
Step Length ◽  
Magnetic Compass ◽  
Pedestrian Navigation ◽  
Novel Technique ◽  
Seamless Positioning ◽  
Impulse Response Filter

In this paper we present a micro-electrical mechanical system (MEMS) based pedestrian navigation system (PNS) for seamless positioning. The sub-algorithms for the PNS are developed and the positioning performance is enhanced using the modified receding horizon Kalman finite impulse response filter (MRHKF). The PNS consists of a biaxial accelerometer and a biaxial magnetic compass mounted on a shoe. The PNS detects a step using a novel technique during the stance phase and simultaneously calculates walking information. Step length is estimated using a neural network whose inputs are the walking information. The azimuth is calculated using the magnetic compass, the walking information and the tilt compensation algorithm. Using the proposed sub-algorithms, seamless positioning can be accomplished. However, the magnetic compass based azimuth may have an error that varies according to the surrounding magnetic field. In this paper, the varying error is compensated using the MRHKF filter. Finally, the performance enhanced seamless positioning is achieved, and the performance is verified by experiment.

scholarly journals Surgical phase recognition by learning phase transitions

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Manish Sahu ◽  
Angelika Szengel ◽  
Anirban Mukhopadhyay ◽  
Stefan Zachow
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Phase Detection ◽  
Short Term ◽  
Video Frames ◽  
Usage Patterns ◽  
The Difference ◽  
Automatic Tool ◽  
Context Aware System ◽  
Phase Recognition

AbstractAutomatic recognition of surgical phases is an important component for developing an intra-operative context-aware system. Prior work in this area focuses on recognizing short-term tool usage patterns within surgical phases. However, the difference between intra- and inter-phase tool usage patterns has not been investigated for automatic phase recognition. We developed a Recurrent Neural Network (RNN), in particular a state-preserving Long Short Term Memory (LSTM) architecture to utilize the long-term evolution of tool usage within complete surgical procedures. For fully automatic tool presence detection from surgical video frames, a Convolutional Neural Network (CNN) based architecture namely ZIBNet is employed. Our proposed approach outperformed EndoNet by 8.1% on overall precision for phase detection tasks and 12.5% on meanAP for tool recognition tasks.

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