Deep-IRTarget: An Automatic Target Detector in Infrared Imagery Using Dual-Domain Feature Extraction and Allocation

Zhang, Ruiheng; Xu, Lixin; Yu, Zhengyu; Shi, Ye; Mu, Chengpo; Xu, Min

doi:10.1109/tmm.2021.3070138

Cited by 74 publications

(36 citation statements)

References 51 publications

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“…It is evident that our method has outperformed several relevant studies with higher accuracy, better usability, and low limitations. However, there are a few studies based on advanced deep-learning models that extract features and perform recognition together [35,36]. We have not used computationally complex deep learning models as we did not want to improve classification accuracy with the cost of high computation but preferred simple machine learning models.…”

Section: Resultsmentioning

confidence: 99%

Smart Piezoelectric-Based Wearable System for Calorie Intake Estimation Using Machine Learning

Hussain

Al‐rimy

Hussain³

et al. 2022

Applied Sciences

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Eating an appropriate food volume, maintaining the required calorie count, and making good nutritional choices are key factors for reducing the risk of obesity, which has many consequences such as Osteoarthritis (OA) that affects the patient’s knee. In this paper, we present a wearable sensor in the form of a necklace embedded with a piezoelectric sensor, that detects skin movement from the lower trachea while eating. In contrast to the previous state-of-the-art piezoelectric sensor-based system that used spectral features, our system fully exploits temporal amplitude-varying signals for optimal features, and thus classifies foods more accurately. Through evaluation of the frame length and the position of swallowing in the frame, we found the best performance was with a frame length of 30 samples (1.5 s), with swallowing located towards the end of the frame. This demonstrates that the chewing sequence carries important information for classification. Additionally, we present a new approach in which the weight of solid food can be estimated from the swallow count, and the calorie count of food can be calculated from their estimated weight. Our system based on a smartphone app helps users live healthily by providing them with real-time feedback about their ingested food types, volume, and calorie count.

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Section: Resultsmentioning

confidence: 99%

Smart Piezoelectric-Based Wearable System for Calorie Intake Estimation Using Machine Learning

Hussain

Al‐rimy

Hussain³

et al. 2022

Applied Sciences

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“…Over the last few years, there has been a lot of development in the use of neural networks for feature extraction in object identification problems. For example, Zhang et al created Deep-IRTarget, a unique backbone network composed of a frequency feature extractor, a spatial feature extractor, and a dual-domain feature resource allocation model, to cope with challenges in feature extraction [ 40 ]. Moreover, the deep learning algorithm is employed in burnt area mapping with the use of Sentinel-12 data [ 41 ].…”

Section: Related Workmentioning

confidence: 99%

Prediction Model of Adverse Effects on Liver Functions of COVID-19 ICU Patients

Mashraqi

Halawani

Alelyani

et al. 2022

Journal of Healthcare Engineering

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SARS-CoV-2 is a recently discovered virus that poses an urgent threat to global health. The disease caused by this virus is termed COVID-19. Death tolls in different countries remain to rise, leading to continuous social distancing and lockdowns. Patients of different ages are susceptible to severe disease, in particular those who have been admitted to an ICU. Machine learning (ML) predictive models based on medical data patterns are an emerging topic in areas such as the prediction of liver diseases. Prediction models that combine several variables or features to estimate the risk of people being infected or experiencing a poor outcome from infection could assist medical staff in the treatment of patients, especially those that develop organ failure such as that of the liver. In this paper, we propose a model called the detecting model for liver damage (DMLD) that predicts the risk of liver damage in COVID-19 ICU patients. The DMLD model applies machine learning algorithms in order to assess the risk of liver failure based on patient data. To assess the DMLD model, collected data were preprocessed and used as input for several classifiers. SVM, decision tree (DT), Naïve Bayes (NB), KNN, and ANN classifiers were tested for performance. SVM and DT performed the best in terms of predicting illness severity based on laboratory testing.

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“…In References [42,43], an automatic target detection and recognition in the infrared images based on a CNN is studied.…”

Section: Related Workmentioning

confidence: 99%

Recurrent Neural Network for Human Activity Recognition in Embedded Systems Using PPG and Accelerometer Data

et al. 2021

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Photoplethysmography (PPG) is a common and practical technique to detect human activity and other physiological parameters and is commonly implemented in wearable devices. However, the PPG signal is often severely corrupted by motion artifacts. The aim of this paper is to address the human activity recognition (HAR) task directly on the device, implementing a recurrent neural network (RNN) in a low cost, low power microcontroller, ensuring the required performance in terms of accuracy and low complexity. To reach this goal, (i) we first develop an RNN, which integrates PPG and tri-axial accelerometer data, where these data can be used to compensate motion artifacts in PPG in order to accurately detect human activity; (ii) then, we port the RNN to an embedded device, Cloud-JAM L4, based on an STM32 microcontroller, optimizing it to maintain an accuracy of over 95% while requiring modest computational power and memory resources. The experimental results show that such a system can be effectively implemented on a constrained-resource system, allowing the design of a fully autonomous wearable embedded system for human activity recognition and logging.

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Deep-IRTarget: An Automatic Target Detector in Infrared Imagery Using Dual-Domain Feature Extraction and Allocation

Cited by 74 publications

References 51 publications

Smart Piezoelectric-Based Wearable System for Calorie Intake Estimation Using Machine Learning

Smart Piezoelectric-Based Wearable System for Calorie Intake Estimation Using Machine Learning

Prediction Model of Adverse Effects on Liver Functions of COVID-19 ICU Patients

Recurrent Neural Network for Human Activity Recognition in Embedded Systems Using PPG and Accelerometer Data

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