scholarly journals Simulation of Breathing Patterns and Classification of Sensor Data for the early detection of impending Sudden Infant Death

2019 ◽  
Vol 5 (1) ◽  
pp. 401-403
Author(s):  
Michael Munz ◽  
Nicolas Wolf
Keyword(s):  
Neural Network ◽  
Infant Death ◽  
Inertial Measurement Unit ◽  
Sudden Infant Death ◽  
Sensor Data ◽  
Measurement Unit ◽  
Sudden Infant ◽  
Breathing Patterns ◽  
Inertial Measurement

AbstractIn this work, a methodology for the classification of breathing patterns in order to prevent sudden infant death (SID) incidents is presented. The basic idea is to classify breathing patterns which might lead to SID prior to an incident. A thorax sensor is proposed, which is able to simulate breathing patterns given by certain parameters. A sensor combination of conductive strain fabric and an inertial measurement unit is used for data acquisition. The data is then classified using a neural network.

scholarly journals An Approach to the Classification of Apparent Asphyxial Infant Deaths

2017 ◽  
Vol 7 (2) ◽  
pp. 200-211 ◽  
Author(s):  
Evan W. Matshes ◽  
Emma O. Lew
Keyword(s):  
Risk Factors ◽  
Sudden Infant Death Syndrome ◽  
Infant Death ◽  
Sudden Infant Death ◽  
Sudden Infant ◽  
Death Certification ◽  
High Quality ◽  
Infant Deaths ◽  
Unexpected Infant Death

Recent evidence indicates that with thorough, high quality death investigations and autopsies, forensic pathologists have recognized that many unexpected infant deaths are, in fact, asphyxial in nature. With this recognition has come a commensurate decrease in, and in some cases, abolition of, the label “sudden infant death syndrome” (SIDS). Current controversies often pertain to how and why some infant deaths are determined to be asphyxial in nature and whether or not apparent asphyxial circumstances are risk factors for SIDS, or rather, harbingers of asphyxial deaths. In an effort to sidestep these controversies, some forensic pathologists elected to instead use the noncommittal label “sudden unexpected infant death” (SUID), leading to the unfortunate consequence of SUID – like SIDS – gaining notoriety as an actual disease that could be diagnosed, studied, and ultimately cured. Although it is not possible to provide death certification guidance for every conceivable type of unexpected infant death, we recognize and propose a simple classification system for overarching themes that cover the vast majority of cases where infants die suddenly and unexpectedly.

The Effect of Image Input Transformation from Inertial Measurement Unit Data on the Classification of Skateboarding Tricks

2021 ◽  
pp. 424-432
Author(s):  
Muhammad Amirul Abdullah ◽  
Muhammad Ar Rahim Ibrahim ◽  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Aizzat Zakaria ◽  
Mohd Azraai Mohd Razman ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Inertial Measurement

scholarly journals A Mixed Deep Recurrent Neural Network for MEMS Gyroscope Noise Suppressing

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 181 ◽  
Author(s):  
Changhui Jiang ◽  
Yuwei Chen ◽  
Shuai Chen ◽  
Yuming Bo ◽  
Wei Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Inertial Measurement Unit ◽  
Short Term Memory ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Short Term ◽  
Inertial Measurement ◽  
Long Short Term Memory ◽  
Gated Recurrent Unit

Currently, positioning, navigation, and timing information is becoming more and more vital for both civil and military applications. Integration of the global navigation satellite system and /inertial navigation system is the most popular solution for various carriers or vehicle positioning. As is well-known, the global navigation satellite system positioning accuracy will degrade in signal challenging environments. Under this condition, the integration system will fade to a standalone inertial navigation system outputting navigation solutions. However, without outer aiding, positioning errors of the inertial navigation system diverge quickly due to the noise contained in the raw data of the inertial measurement unit. In particular, the micromechanics system inertial measurement unit experiences more complex errors due to the manufacturing technology. To improve the navigation accuracy of inertial navigation systems, one effective approach is to model the raw signal noise and suppress it. Commonly, an inertial measurement unit is composed of three gyroscopes and three accelerometers, among them, the gyroscopes play an important role in the accuracy of the inertial navigation system’s navigation solutions. Motivated by this problem, in this paper, an advanced deep recurrent neural network was employed and evaluated in noise modeling of a micromechanics system gyroscope. Specifically, a deep long short term memory recurrent neural network and a deep gated recurrent unit–recurrent neural network were combined together to construct a two-layer recurrent neural network for noise modeling. In this method, the gyroscope data were treated as a time series, and a real dataset from a micromechanics system inertial measurement unit was employed in the experiments. The results showed that, compared to the two-layer long short term memory, the three-axis attitude errors of the mixed long short term memory–gated recurrent unit decreased by 7.8%, 20.0%, and 5.1%. When compared with the two-layer gated recurrent unit, the proposed method showed 15.9%, 14.3%, and 10.5% improvement. These results supported a positive conclusion on the performance of designed method, specifically, the mixed deep recurrent neural networks outperformed than the two-layer gated recurrent unit and the two-layer long short term memory recurrent neural networks.

scholarly journals An Efficient Convolutional Neural Network Computer Vision System to Prevent Sudden Infant Death Syndrome

2020 ◽  
Author(s):  
Vivek S Bharati
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Sudden Infant Death Syndrome ◽  
Infant Death ◽  
Vision System ◽  
Sudden Infant Death ◽  
Activation Function ◽  
Sudden Infant ◽  
Computer Vision System ◽  
Sleeping Position

Sudden Infant Death Syndrome (SIDS) causes infants under one year of age to die inexplicably. One of the most important external factors responsible for the syndrome, called an ‘outside stressor’, is the sleeping position of the baby. When the baby sleeps on the stomach with face down, the risk of SIDS occurring is very high. We propose a Convolutional Neural Network (CNN) based computer vision system that can alert caregivers on their mobile phones within a few seconds of the baby moving to a hazardous face-down sleeping position. The model processes real-time image feeds with a single efficient forward pass. It has a low computational load and a low memory footprint. This would allow it to be embedded in low power edge devices such as crib cameras. Processing at the edge would also alleviate privacy concerns in sending images into the network. The CNN architecture is composed of multiple sets of processing units, each unit containing a 2D convolutional layer with the Rectified Linear Unit activation function followed by a Max Pooling layer. The final layer in the architecture is a fully connected dense layer with the Sigmoid activation function and outputs three classes of sleeping position indicators. The seed corpus for the training dataset was generated from realistic baby dolls with diverse racial mix in three sleeping positions (face-up, turning, face-down). These seed images were used to generate additional images by applying various image transformations. We experimented with various numbers of convolutional processing units and dense layers as well as the number of convolutional kernels to arrive at the optimal production configuration. We observed a consistently high accuracy of detection of sleeping position changes to turning and face-down positions with a trend towards even higher accuracies with caregiver feedback. Therefore, this system is a viable candidate for consideration as a non-intrusive technology to assist in preventing the Sudden Infant Death Syndrome.

Preventing Sudden Infant Death Syndrome (SIDS) Based on Motion Estimation and Neural Network

2013 ◽  
Vol 19 (11) ◽  
pp. 3231-3235
Author(s):  
Muhammad Naufal Mansor ◽  
Sazali Yaacob ◽  
Hariharan Muthusamy ◽  
Shafriza Nisha Basah ◽  
Mohd Nazri Rejab
Keyword(s):  
Neural Network ◽  
Motion Estimation ◽  
Sudden Infant Death Syndrome ◽  
Infant Death ◽  
Sudden Infant Death ◽  
Sudden Infant
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