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Detecting Elderly Behaviors Based on deep learning for Healthcare: Recent Advances, Methods, Real-World Applications and Challenges

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IEEE ACCESS 2022年 10卷 69802-69821页

作者： Almutairi, Mubarak Gabralla, Lubna A. Abubakar, Saidu Chiroma, Haruna Univ Hafr Al Batin Coll Comp Sci & Engn Hafar al Batin 31991 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Appl Coll Dept Comp Sci & Informat Technol Riyadh 11671 Saudi Arabia Abubakar Tafawa Balewa Univ Dept Math Sci Bauchi 740004 Nigeria

Machine learning has been applied in healthcare domain for the development of smart devices to improve the life of the elderly persons in the society. Taking care of elderly person in the society is a critical issue that need automation. To proffer solution, many researchers developed deep learning algorithms smart devices for detecting elderly behavior to improve the elderly healthcare. Despite the progress made in the applications of deep learning algorithms in elderly healthcare systems, to the best of the author's knowledge no comprehensive recent development has been published on this interesting research area especially focusing on deep learning. In this paper, we presented a comprehensive recent development on the advances, methods and real world applications on developing smart devices for detecting elderly behavior for use in smart home, smart clinic, smart hospital and smart elderly nursing home for elderly person's healthcare. Theories of the deep learning algorithms, recent development recorded as regard to the applicability of deep learning in elderly healthcare systems and case studies were discussed. A taxonomy based on the data extracted from the applicability of deep learning algorithms in elderly healthcare systems is created to ease pointing out areas that need more attention. The article shows that the deep learning algorithm that received tremendous attention from researchers is convolutional neural network architecture and its variants. To help in future development of the research area, we highlighted the challenges associated to the applicability of deep learning algorithms in elderly healthcare system and pointed out new point of view for future research. The research community can use our review as a benchmark for proposing novel deep learning algorithms based smart devices to detect elderly behavior for elderly healthcare systems. Industries and organizations can use the paper as a guide in selecting machine learning based smart device for

关键词： Older adults Medical services deep learning Smart devices Databases Search engines Machine learning algorithms Machine learning deep learning algorithms convolutional neural network elderly person behaviour VGG Internet of Things smart nursing home

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Evaluation of emergency driving behaviour and vehicle collision risk in connected vehicle environment: A deep learning approach

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IET INTELLIGENT TRANSPORT SYSTEMS 2021年第4期15卷 584-594页

作者： Peng, Liqun Wang, Yiting Zhang, Fan Zhang, Jisheng Li, Zhixiong East China Jiaotong Univ Sch Transportat & Logist Nanchang Jiangxi Peoples R China Beijing GOTEC ITS Technol Co Ltd Beijing Peoples R China Yonsei Univ Yonsei Frontier Lab 50 Yonsei Ro Seoul 03722 South Korea Univ Alberta Dept Civil & Environm Engn Edmonton AB Canada

In the latest connected vehicle (CV) message standards, including SAE J2735-2016 and T-CASE 53-2017, the basic safety messages (BSMs) are designed specifically as effective measures for traffic safety management and applications. In this study, a testbed on the Nanchang-Jiujiang Intelligent Highway in Jiangxi, China is illustrated as an example, and the basic architecture and key technologies is introduced for a proactive traffic safety utilisation, where the core basic safety message (BSM) data are sorted and implemented to perceive and predict risky driving behaviours in a field environment. On this basis, an accurate insight into time-critical driving safety issues can be achieved by investigating raw BSM data, such as the inter-vehicle distance, driver manipulation, vehicle speed, and acceleration/deceleration. Furthermore, to effectively take advantage of connected vehicle information and perceive the high uncertainty of driving behaviours during an emergency situation and evaluate the driving safety in mixed traffic scenarios, a long short-term memory (LSTM) based deep learning framework is introduced to build a multi-horizon vehicle crash risk prediction model using continuous BSMs as the inputs. The experimental results demonstrate the significance of connected vehicle data and deep learning algorithms for improving driving safety and promoting widespread deployment and application of connected vehicles.

关键词： Traffic engineering computing deep learning approach vehicle speed road safety connected vehicle data latest connected vehicle connected vehicles road traffic control time-critical driving safety issues message standards effective measures roads deep learning algorithms emergency situation promoting widespread deployment raw BSM data field environment proactive traffic safety utilisation T-CASE 53-2017 SAE J2735-2016 deep learning framework risky driving behaviours connected vehicle information basic safety messages Road-traffic system control Nanchang-Jiujiang Intelligent Highway core basic safety message data road vehicles connected vehicle environment mixed traffic scenarios multihorizon vehicle crash risk prediction model learning (artificial intelligence) inter-vehicle distance vehicle collision risk road traffic traffic safety management Other topics in statistics traffic engineering computing Health and safety aspects basic architecture

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Remote Cardiac System Monitoring Using 6G-IoT Communication and deep learning

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WIRELESS PERSONAL COMMUNICATIONS 2024年第1期136卷 123-142页

作者： Banga, Abdulbasid S. Alenazi, Mohammed M. Innab, Nisreen Alohali, Mansor Alhomayani, Fahad M. Algarni, Mohammad H. Saidani, Taoufik Saudi Elect Univ Coll Comp & Informat CCI Riyadh 11673 Saudi Arabia Univ Tabuk Fac Comp & Informat Technol Dept Comp Sci Tabuk 71491 Saudi Arabia AlMaarefa Univ Coll Appl Sci Dept Comp Sci & Informat Syst Riyadh 13713 Saudi Arabia Al Imam Mohammad Ibn Saud Islamic Univ IMSIU Appl Coll Riyadh Saudi Arabia Taif Univ Coll Comp & Informat Technol Dept Comp Engn Taif 26571 Saudi Arabia Taif Univ Coll Comp & Informat Technol Taif 26571 Saudi Arabia Al Baha Univ Dept Comp Sci Al Baha 65779 Saudi Arabia Northern Border Univ Fac Comp & Informat Technol Dept Comp Sci Rafha Saudi Arabia

Remote patient monitoring has recently been popularised due to advanced technological innovations. The advent of the Sixth Generation Internet of Things (6G-IoT) communication technology, combined with deep learning algorithms, presents a groundbreaking opportunity for enhancing remote cardiac system monitoring. This paper proposes an innovative framework leveraging the ultra-reliable, low-latency communication capabilities of 6G-IoT to transmit real-time cardiac data from wearable devices directly to healthcare providers. Integrating deep learning models facilitates the accurate analysis and prediction of cardiac anomalies, significantly improving traditional monitoring systems. Our methodology involves the deployment of cutting-edge wearable sensors capable of capturing high-fidelity cardiac signals. These signals are transmitted via 6G-IoT networks, ensuring minimal delay and maximum reliability. Upon receiving the data, a densely connected deep neural network with an optimised swish activation function-designed explicitly for cardiac anomaly detection is employed to analyse the data in real-time. These algorithms are trained on vast datasets to recognise patterns indicative of potential cardiac issues, allowing immediate intervention when necessary. The proposed system's efficacy is validated through extensive testing in simulated environments, demonstrating its ability to accurately detect and swiftly predict a wide range of cardiac conditions. Moreover, implementing 6G-IoT communication ensures the system's scalability and adaptability to future technological advancements.

关键词： 6G-IoT deep learning algorithms Cardiac system monitoring Wearable sensors

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deep learning-Based Brain Tumor Detection in Privacy-Preserving Smart Health Care Systems

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IEEE ACCESS 2024年 12卷 140722-140733页

作者： Lata, Kusum Singh, Prashant Saini, Sandeep Cenkeramaddi, Linga Reddy LNM Inst Informat Technol Dept Elect & Commun Engn Jaipur 302031 Rajasthan India Univ Agder Dept Informat & Commun Technol N-4879 Grimstad Norway

deep learning has been widely used in medical image processing, which has sparked the development of a wide range of applications and led to a notable increase in the number of therapeutic and diagnostic options available for a range of medical imaging problems. In the era of the Internet of Things (IoT), safeguarding the security and privacy of medical data is crucial to the advancement of sophisticated diagnostic applications for medical imaging. deep learning-based brain tumor detection in smart health care systems with privacy preservation is proposed in this paper. The system under consideration is organized into three discrete stages that are then combined to provide an all-encompassing blueprint. During the first phase, patients with brain tumors are the primary target of an efficient healthcare system that is introduced. A Microsoft-based operating system-compatible application has been developed to accomplish this. Patient data is secure and only available to the hospital and the individual patient, which enables patients to engage with the system both locally and virtually. To obtain the anticipated outcomes, the user must first submit the patient's MRI scan and then enter a special 10-digit code. In the second part, the authors develop a deep learning-based tumor identification platform which also incorporates the AES-128 algorithms and PBKDF2 for secure medical image storage on the server and data transmission via the internet from the client to the server and back to the client upon prediction. The proposed approach integrates ResNet-50, Inception V3, and VGG-16 architecture to build a Convolutional Neural Network (CNN)-based brain tumor diagnosis system. These architectures are enhanced through significant pre-processing, SGD, RMSprop, and Adam optimization. Our research focuses on the application of cutting-edge methods to maintain confidentiality and accomplish precise tumor diagnosis, underscoring the importance of privacy preservation. Our micro-av

关键词： Tumors Medical services Medical diagnostic imaging Artificial intelligence Security Convolutional neural networks Privacy Brain tumor detection classification CNN cryptography deep learning algorithms MRI privacy preservation smart healthcare systems

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A Comparison of deep learning Techniques for Arterial Blood Pressure Prediction

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COGNITIVE COMPUTATION 2022年第5期14卷 1689-1710页

作者： Paviglianiti, Annunziata Randazzo, Vincenzo Villata, Stefano Cirrincione, Giansalvo Pasero, Eros Politecn Torino DET Dept Elect & Telecommun Turin Italy Univ Picardie Jules Verne Lab LTI Amiens France Univ South Pacific Suva Fiji

Continuous vital signal monitoring is becoming more relevant in preventing diseases that afflict a large part of the world's population;for this reason, healthcare equipment should be easy to wear and simple to use. Non-intrusive and non-invasive detection methods are a basic requirement for wearable medical devices, especially when these are used in sports applications or by the elderly for self-monitoring. Arterial blood pressure (ABP) is an essential physiological parameter for health monitoring. Most blood pressure measurement devices determine the systolic and diastolic arterial blood pressure through the inflation and the deflation of a cuff. This technique is uncomfortable for the user and may result in anxiety, and consequently affect the blood pressure and its measurement. The purpose of this paper is the continuous measurement of the ABP through a cuffless, non-intrusive approach. The approach of this paper is based on deep learning techniques where several neural networks are used to infer ABP, starting from photoplethysmogram (PPG) and electrocardiogram (ECG) signals. The ABP was predicted first by utilizing only PPG and then by using both PPG and ECG. Convolutional neural networks (ResNet and WaveNet) and recurrent neural networks (LSTM) were compared and analyzed for the regression task. Results show that the use of the ECG has resulted in improved performance for every proposed configuration. The best performing configuration was obtained with a ResNet followed by three LSTM layers: this led to a mean absolute error (MAE) of 4.118 mmHg on and 2.228 mmHg on systolic and diastolic blood pressures, respectively. The results comply with the American National Standards of the Association for the Advancement of Medical Instrumentation. ECG, PPG, and ABP measurements were extracted from the MIMIC database, which contains clinical signal data reflecting real measurements. The results were validated on a custom dataset created at Neuronica Lab, Politecnico di

关键词： Arterial blood pressure deep learning algorithms Electrocardiogram Machine learning Photoplethysmogram

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Wearable Recognition System for Complex Motions Based on Hybrid deep-learning-Enhanced Strain Sensors

ADVANCED INTELLIGENT SYSTEMS

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ADVANCED INTELLIGENT SYSTEMS 2023年第11期5卷

作者： Nie, Meng Chen, Pengfan Wen, Lei Fan, Jinwen Zhang, Qian Yin, Kuibo Dou, Guangbin Southeast Univ SEU FEI Nanop Ctr Sch Elect Sci & Engn Key Lab MEMSMinist Educ Nanjing 210096 Jiangsu Peoples R China

Wearable recognition systems based on flexible electronics present immense potential for applications in human-machine interfaces, medical care, soft robots, etc. However, they experience challenges in terms of the nonideal consistency and stability of flexible sensors, which are responsible for detecting physical signals from human motions. These challenges hinder the improvement of recognition precision and capability in the wearable systems. Furthermore, the computational consumption for the recognition increases as more sensors are used to extensively gather information for distinguishing between complex motions. Herein, a wearable recognition system based on deep-learning-enhanced strain sensors for distinguishing between the complex motions of the human body is presented. A strain sensor based on peak-valley microstructures is fabricated and packaged to improve consistency and stability. Moreover, a lightweight hybrid convolutional neural network long short-term memory model is designed to lower the computational costs of the deep learning process. In particular, by designing Butterworth filtering and Z-score normalization algorithms, the error in feature extraction caused by sensor signal fluctuation is reduced, thereby improving the recognition accuracy of the proposed wearable system to 95.72% for seven gait motions and 100% for four different continuous series of Tai Chi forms.

关键词： Butterworth filtering deep learning algorithms strain sensors wearable recognition systems Z-score normalization

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Reliable hybrid deep learning technique for an effective spectrum sensing in cognitive radio

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第6期44卷 10765-10779页

作者： Sivaranjani, S. Vivek, C. M Kumarasamy Coll Engn Dept Elect & Commun Engn Karur Tamilnadu India Sri Eshwar Coll Engn Dept Elect & Commun Engn Coimbatore Tamil Nadu India

Spectrum sensing will be an essential component in developing cognitive radio networks, which will be an essential component of the subsequent generation of wireless communication systems. Over the course of several decades, a great deal of different strategies, including cyclo-stationary, energy detectors, and matching filters, have been put up as potential solutions. Obviously, each of these methods comes with a few of negatives that you have to take into consideration. When the Signal-to-Noise Ratio (SNR) changes, energy detectors work poorly;cyclo-stationary detectors are technically sophisticated;and employing matching filters needs experience with Primary User (PU) signals. Researchers have recently been devoting a great deal of attention to Machine learning (ML) and deep learning (DL) algorithms as a result of the potential uses that these algorithms may have in the development of exceptionally accurate spectrum sensing models. The capacity to learn from data in a way that traditional learning algorithms are unable to has led to the rise in prominence of these types of algorithms. The Hybrid Model of Improved Long Short Term Memory with Improved Extreme learning Models (HILSTM-IELM), to be more specific, is what is being suggested since it reduces the amount of energy that is used during data transmission as well as the range and the duty cycle. Because of this, the disadvantage in existing methodology, proposed technique reduced to a certain level in energy consumption. In the last step of this analysis, the performance of the HILSTM-IELM-based spectrum sensing is compared to that of a variety of different methods that are currently in use. According to the findings of recent studies, the spectrum sensing method that was created provides superior performance to that of competing technologies in terms of the accuracy, sensitivity, and specificity of data transmission systems.

关键词： Improved long short-term memory improved extreme learning machines energy detectors cyclo-stationary features machine learning deep learning algorithms

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End-to-end waveform level receiver with deep learning

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IET COMMUNICATIONS 2022年第11期16卷 1315-1324页

作者： Zhu, Zhaorui Yu, Hongyi Shen, Caiyao PLA Strateg Support Force Informat Engn Univ Zhengzhou Henan Peoples R China

Numerous researches on communication receiver design with deep learning algorithms have been implemented effectively recently. In this paper, an innovative scheme is proposed to construct an end-to-end neural network (NN) receiver to directly recover information bits from unsynchronized waveform sequences. Considering the correlation between samples, multiple bidirectional long-short term memory (BiLSTM) layers to process oversampled signals. Then, shifted binary cross-entropy (SBCE) function is devised to tackle the overfitting issue and eliminate instances with abnormal bit error rate (BER), which originate from standard binary cross-entropy (BCE) loss function. Substantial simulation results demonstrate that the trained NN receiver can achieve theory BER values under excellent conditions for transceivers. Compared with traditional synchronization algorithms, the proposed method can obtain significant BER performance gain under harsh conditions, such as low oversample rate, small roll-off factor, short frame length.

关键词： trained NN receiver radio receivers traditional synchronization algorithms synchronisation BER end-to-end waveform level receiver recurrent neural nets NN deep learning (artificial intelligence) shifted binary cross entropy function shifted binary cross entropy BiLSTM layers bidirectional long-short term memory communication receiver design standard binary cross entropy loss function error statistics abnormal bit error rate unsynchronized waveform sequences end-to-end neural network receiver entropy deep learning algorithms long-short term memory layers telecommunication computing information bits

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Harvesting Earth's heat: A deep learning Odyssey for reservoir characterization and sustainable geothermal energy management

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2024年 238卷

作者： Ullah, Jar Li, Huan Forster, Hans-Juergen Abdalla, Rifaat M. Ehsan, Muhsan Faisal, Mohmed Cent South Univ Sch Geosci & Info Phys Key Lab Metallogen Predict Nonferrous Met & Geol E Minist Educ Changsha 410083 Peoples R China Helmholtz Ctr Potsdam German Res Ctr Geosci GFZD D-14473 Potsdam Germany Sultan Qaboos Univ Dept Earth Sci Coll Sci Al Khoud Oman Suez Canal Univ Fac Sci Dept Geol Ismailia 41522 Egypt

Characterizing and predicting reservoir behavior pose significant challenges in geothermal reservoir engineering. For sustainable geothermal field management, the potential occurrence of thermal breakthroughs in producers during the injection of cold water necessitates a profound understanding of how production is influenced by the injection philosophy. The fractured nature of geothermal reservoirs adds complexity and nonlinearity to the relationship between production and injection wells. In this study, we explore alternative models to simulate reservoir behavior as substitutes for full reservoir simulations. Utilizing deep learning algorithms, we investigate two different architectures, namely the standard Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). These models map injection flow rates at the injectors to tracer concentration data at the producers through layers, nodes, and activation functions. Training these models on a synthetic geothermal reservoir, Notably, the GRU architecture consistently demonstrates superior predictive capabilities across various scenarios for all three producers. Specifically, in configurations involving a target feed, the GRU model yields higher R2 values compared to its LSTM counterpart, indicating its effectiveness in capturing inter-well relationships. Furthermore, when considering all producers collectively, the GRU model exhibits a trend of lower test errors and higher R2 values, reaffirming its proficiency in modeling complex reservoir dynamics. These findings underscore the significance of GRU as a preferred choice for accurate reservoir behavior prediction. In addition to the aforementioned insights, our study contributes novel approaches to reservoir behavior prediction in the field of geothermal reservoir engineering. By leveraging deep learning algorithms, specifically the GRU and LSTM architectures, we introduce innovative methodologies for simulating reservoir behavior as alternatives to full reservoir si

关键词： Geothermal reservoir engineering Sustainable energy management Thermal breakthrough deep learning algorithms Long short -term memory (LSTM) Gated recurrent unit (GRU)

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Hybrid Plasmonic Biosensors with deep learning for Colorectal Cancer Detection

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PLASMONICS 2024年 1-15页

作者： Kumarganesh, S. Murugesan, Malathi Ganesh, C. Santhiyakumari, N. Thangavel, M. Jayaram, K. Sagayam, K. Martin Pandey, Binay Kumar Pandey, Digvijay Knowledge Inst Technol Dept ECE Salem Tamil Nadu India EGS Pillay Engn Coll Dept ECE Nagapattinam Tamil Nadu India Sri Eshwar Coll Engn Dept CCE Coimbatore Tamil Nadu India Kalaignarkarunanidhi Inst Technol Dept ECE Coimbatore Tamil Nadu India Karunya Inst Technol & Sci Dept ECE Coimbatore Tamil Nadu India Govind Ballabh Pant Univ Agr & Technol Coll Technol Dept Informat Technol Pantnagar Uttarakhand India Govt UP Dept Tech Educ Uttar Pradesh Lucknow India

Colorectal cancer remains an important health issue worldwide, necessitating the development of more effective diagnostic tools for early detection and intervention. This work presents a novel approach that combines plasmonic biosensor technology with advanced medical imaging and deep learning algorithms to improve colorectal cancer early detection. Plasmonic biosensors based on surface plasmon resonance (SPR) technology were used to detect small malignant mutations in biological samples with high sensitivity and specificity. These biosensors enable real-time, label-free detection, which, when combined with medical imaging techniques, has the potential to revolutionize colorectal cancer screening. In our methodology, plasmonic biosensor analysis was used to identify biomarkers associated with cancer in biological samples. The imaging data was then subjected to deep learning algorithms to increase the detection accuracy. The biosensor showed a 98.7% sensitivity and 96.4% specificity in differentiating between samples that were cancerous and those that were not. Moreover, the biosensor's detection limit of 10 picograms per milliliter (pg/mL) has been identified, demonstrating its efficaciousness in detecting minute malignant alterations. In conclusion, our work emphasizes the possibility of greatly improving colorectal cancer screening programs by merging plasmonic biosensors and deep learning algorithms. This integration addresses important obstacles to the early detection of colorectal cancer and may result in earlier diagnoses and better patient outcomes.

关键词： Plasmonic biosensors Medical imaging Colorectal cancer detection Surface plasmon resonance Early diagnosis Sensitivity Specificity Detection limit deep learning algorithms

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