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Advances in Computing Research on Science Engineering and technology (ACROSET), International conference on

作者： Kamlesh Mani Kamlesh Kumar Singh Ratnesh Litoriya Computer science and engineering Amity University Lucknow India Electronics and Communication Amity University Lucknow India Computer science and engineering Medi-Caps University Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

This review paper presents a comprehensive overview of IoT sensor-based frameworks for real-time Alzheimer's disease detection and monitoring in elderly populations. The paper examines the current state of Alzheimer's disease, including its definition, stages, symptoms, and challenges in diagnosis. It explores the importance of early detection and monitoring in improving patient outcomes and highlights the role of IoT sensors in enabling real-time monitoring of physiological parameters, activity levels, and behavior patterns associated with Alzheimer's disease. The paper discusses various applications of IoT sensors in healthcare and disease monitoring, including smart home monitoring systems, wearable sensor-based interventions, and community-based screening programs. It analyzes methodologies, technologies, and outcomes in IoT sensor-based research, emphasizing the integration of machine learning algorithms for real-time analysis of sensor data. Additionally, the paper addresses challenges and future directions in the field, including regulatory and ethical considerations, technical challenges, and potential advancements. Overall, this review paper underscores the transformative potential of IoT sensor-based frameworks in improving early detection, personalized care, and disease management for individuals living with Alzheimer's disease.

关键词： Machine learning algorithms Reviews Medical services Real-time systems sensors Alzheimer's disease biomedical monitoring Older adults Monitoring Intelligent sensors

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smart Health Monitoring for Comatose Patients: Integrating IoT and Real-Time Alerts

Smart Health Monitoring for Comatose Patients: Integrating I...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： K Thivyabrabha C Saranya R Aakash T Abishek S Mohamed Hussain Department of Biomedical Engineering Nandha Engineering College Erode India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

This research study develops a specialized monitoring and warning system tailored for coma patients to ensure timely and accurate assessment of their health status. The system integrates various physiological markers, including temperature, heart rate, urine level, finger movement, and blink frequency, using non-invasive sensors. Alongside critical indicators, it continuously monitors these parameters to enable rapid caregiver and medical specialist responses to significant changes in the patient’s condition. Additionally, the system incorporates email and phone notifications for immediate alerts. A computer vision-based eye blink monitoring module tracks and evaluates neurological activity through blink frequency analysis. Furthermore, a real-time urine level monitoring system employs sensors to gather and transmit data, facilitating early detection of potential health issues. Future work includes leveraging AI and machine learning for predictive analytics and enhancing interoperability with other medical devices for comprehensive healthcare management.

关键词： Temperature sensors Temperature measurement Accuracy Tracking Fingers sensor systems Real-time systems Reliability biomedical monitoring Monitoring

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Optimized Stacked Sparse Autoencoder for IoT-Cloud smart Healthcare Networks

Optimized Stacked Sparse Autoencoder for IoT-Cloud Smart Hea...

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Sentiment Analysis and Deep Learning (ICSADL), International conference on

作者： Baba Fakruddin Ali B H L. Guganathan Prasanta Kumar Parida D. Samundeeswari Sridhar S Department of CSE Nagarjuna College of engineering and technology Bengaluru Karnataka India Department of Physics Saveetha School of Engineering Saveetha Insitute of Medical and Technical science (SIMATS) Thandalam Chennai Tamil Nadu India School of Rural Management KIIT University Bhubaneswar Odisha India Department of Mathematics Panimalar Engineering College Chennai Tamil Nadu India Department of Research Rajalakshmi Institute of Technology (Autonomous) Chennai Tamil Nadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

IoT technology growth in healthcare has been a revolution, and that is exciting, with the monitoring of real-time critical physiological and clinical parameters from real-time data: data-driven, personalized healthcare solutions. Even with such potential, health IoT faces challenges such as heterogeneity in data, noise, and inconsistent feature extraction that leads to suboptimal decision-making. Methods have to be developed efficiently for preprocessing and feature extraction to analyze data so that this disease can be predicted or monitored correctly. The motivation is to design a solid framework that bridges such gaps by making sure reliable, accurate predictions fit the smart healthcare application. Testing the proposed system with IoT-enabled wearable devices and clinical electronic health records (EHR), physiological metrics were collected from IoT sensors, supplemented by medical history and diagnostic data retrieved from cloud databases for blood pressure, heart rate, glucose levels, and oxygen saturation. The pre-processing step utilized Global-Local Depth Normalization (GLDN), which normalizes global structures and local variances to remove noise and scale variations. These features were analyzed using a Stacked Sparse Autoencoder (SSAE), optimized by Cat Hunting Optimization (CHO) to enhance prediction accuracy and computational efficiency. Experimental results validate the system with a remarkable 99.7% accuracy, 98.1% precision, and 97.5% recall and reduces computation time by 22% compared to the existing techniques. This shows the robustness and feasibility of the proposed approach in applications of smart healthcare. In conclusion, this framework represents an effective solution for smart healthcare systems that deals with data heterogeneity and the challenge of feature optimization for better patient care and resource management in IoT-driven environments.

关键词： Cloud computing Accuracy Autoencoders Medical services sensor systems Real-time systems biomedical monitoring Optimization Monitoring Diseases

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Hybrid sensor Insole for Wearable Ballistocardiography in Remote Health Monitoring

Hybrid Sensor Insole for Wearable Ballistocardiography in Re...

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IEEE International conference on Flexible and Printable sensors and Systems (FLEPS)

作者： Bastian Latsch Omar Ben Dali Benedikt Hench Romol Chadda Youssef Sellami Alexander A. Altmann Niklas Schäfer Julian Seiler Mario Kupnik Measurement and Sensor Technology Group Technische Universität Darmstadt Germany

ISBN: (数字)9798350383263

ISBN: (纸本)9798350383270

Remote health monitoring improves the quality of life by enabling an early condition detection. The human heartbeat qualifies as physiological measure for body vitality and can be acquired through unobtrusive ballistocardiography (BCG). Current wearable BCG devices lack customizability and posture detection of the wearer. In this work, we present a hybrid insole concept with piezoelectric ferroelectrets and piezoresistive Velostat sensors to complement their properties. The Velostat sensor is evaluated in a universal testing machine with an applied load up to 50 N, resulting in a sensitivity of 200 Ohm/N and 80% resistance change. It reliably activates the BCG once the insole wearer maintains a stable posture. The experiment involves three participants in upright posture on the smart insole, at resting pulse and after physical activity. We apply a continuous wavelet transform to the ferroelectret sensor data to enhance feature detection. The heart rate is extracted from the peak distance and, for resting pulse, aligns with the reference heartbeat acquired from a chest strap. Thus, our hybrid insole system proves suitable as a wearable heart rate detector for health monitoring.

关键词： Sensitivity Heart beat sensor systems Reliability biomedical monitoring Intelligent sensors Wearable sensors

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smart Savior: A Life Jacket Integrated IoT Emergency Response System Utilizing TTGO T-Call ESP32 and Advanced sensor Technologies

Smart Savior: A Life Jacket Integrated IoT Emergency Respons...

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International conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Md. Jakarea Debopom Sutradhar Ariful Islam Talukder S.M. Azizul Hoque Sourov Ripon Kumar Debnath Md Shifat Karim Md. Tamzidul Islam Nion Tania Akter Helena Bulbul Department of EEE United International University Dhaka Bangladesh Department of CSE United International University Dhaka Bangladesh Department of BBA United International University Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

Life jackets are crucial for aquatic safety, preventing drowning and aiding in rescue. However, locating survivors can be challenging. The smart Savior life jacket, enhanced with IoT technology, integrates advanced sensors into a wristband and jacket to monitor environmental and physiological data. Key components include the NEO-6M GPS module, heart rate and body temperature sensors, and the ESP32 microprocessor. Data is transmitted via ESP-NOW for real-time monitoring and analysis. Rescue teams access a dynamic dashboard to prioritize rescue efforts based on geolocation and critical health indicators. The system's performance, evaluated against industry standards, shows faster GPS connectivity, real-time data synchronization, and improved accuracy in heart rate monitoring (98%) and water temperature sensing (±0.5 ∘ C). This integration offers a robust and effective approach to enhancing safety and responsiveness during aquatic disasters, making it a vital tool in saving lives.

关键词： Temperature sensors Heart rate Geology Robot sensing systems Real-time systems sensor systems biomedical monitoring Intelligent sensors Monitoring Global Positioning System

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Recognizing and predicting muscular fatigue of biceps brachii in motion with novel fabric strain sensors based on machine learning

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biomedical SIGNAL PROCESSING AND CONTROL 2024年第PartB期96卷

作者： Wang, Chuanling Wang, xi Li, Qiao Tao, xiaoming Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Donghua Univ Engn Res Ctr Digitized Text & Apparel Technol Minist Educ Shanghai 201620 Peoples R China Donghua Univ Coll Text Shanghai 201620 Peoples R China Donghua Univ Key Lab Text Sci & Technol Minist Educ Shanghai 201620 Peoples R China Hong Kong Polytech Univ Sch Fash & Text Hong Kong Peoples R China

Localized muscle fatigue is of major kinematic and medical significance in the field of sports and rehabilitations. However, only few works reported fatigue recognition for field training, and via analysis of only the noisesusceptible electromyography. In this study, along with portable EMG sensors, a wearable smart arm band powered by fabric strain sensors was implemented to detect the thickness variation of biceps. Dumbbell curl training protocol was designed, and 32 subjects were enrolled, contributing to a database of 580 curl samples. Fatigue-related characteristics of muscle thickness were proposed according to muscle physiology and combined with that of sEMG signals, forming feature vectors of the samples. Clustering results showed that the fatigued cycles can be successfully recognized and labeled. For potential applications, fatigue prediction models based on supervised learning methods were further proposed and compared, of which the SVM-based model was observed with satisfactory overall accuracy of 83.3%, yet the fatigue samples can be predicted at much higher rate, with effective recall 90%, F1-SCORE 95% and AUC 98.7%. This work not only exploits feasibility of using latent physiological indicators such as muscle thickness in muscle fatigue monitoring, the result and methodologies will also inspire wider horizon of human-centered applications using novel flexible sensors.

关键词： Fatigue recognition Muscle thickness Fabric strain sensor Machine learning

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Multimodal sensor-based identification of stress and compulsive actions in children with obsessive-compulsive disorder for telemedical treatment 44

Multimodal sensor-based identification of stress and compuls...

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44th Annual International conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Thierfelder, Annika Primbs, Jonas Severitt, Bjorn Hohnecker, Carolin S. Kuhnhausen, Jan Alt, Annika K. Pascher, Anja Worz, Ursula Passon, Helene Seemann, Jens Ernst, Christian Lautenbacher, Heinrich Holderried, Martin Kasneci, Enkelejda Giese, Martin A. Bulling, Andreas Menth, Michael Barth, Gottfried M. Ilg, Winfried Hollmann, Karsten Renner, Tobias J. Univ Tubingen Hertie Inst Clin Brain Res Sect Theoret Sensorimotor Control Tubingen Germany Univ Tubingen Dept Comp Sci Commun Networks Tubingen Germany Univ Tubingen Dept Comp Sci Human Comp Interact Tubingen Germany Univ Hosp Tubingen Psychiat Psychosomat & Psychotherapy Childhood & Tubingen Germany Univ Hosp Tubingen Sect Informat Technol Tubingen Germany Univ Hohenheim Inst Healthcare & Publ Management Stuttgart Germany Univ Tubingen Dept Strateg Med Dev & Qual Management Tubingen Germany Univ Stuttgart Inst Visualisat & Interact Syst Stuttgart Germany

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

In modern psychotherapy, digital health technology offers advanced and personalized therapy options, increasing availability as well as ecological validity. These aspects have proven to be highly relevant for children and adolescents with obsessive-compulsive disorder (OCD). Exposure and Response Prevention therapy, which is the state-of-the-art treatment for OCD, builds on the reconstruction of everyday life exposure to anxious situations. However, while compulsive behavior pre-dominantly occurs in home environments, exposure situations during therapy are limited to clinical settings. Telemedical treatment allows to shift from this limited exposure reconstruction to exposure situations in real life. In the SSTeP KiZ study (smart sensor technology in telepsychotherapy for children and adolescents with OCD), we combine video therapy with wearable sensors delivering physiological and behavioral measures to objectively determine the stress level of patients. The setup allows to gain information from exposure to stress in a realistic environment both during and outside of therapy sessions. In a first pilot study, we explored the sensitivity of individual sensor modalities to different levels of stress and anxiety. For this, we captured the obsessive-compulsive behavior of five adolescents with an ECG chest belt, inertial sensors capturing hand movements, and an eye tracker. Despite their prototypical nature, our results deliver strong evidence that the examined sensor modalities yield biomarkers allowing for personalized detection and quantification of stress and anxiety. This opens up future possibilities to evaluate the severity of individual compulsive behavior based on multi-variate state classification in real-life situations.

关键词： Pediatrics Sensitivity Tracking Anxiety disorders Medical treatment Behavioral sciences biomedical monitoring

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Wireless sensor Networks for Sports Injury Management and Player Safety Enhancement

Wireless Sensor Networks for Sports Injury Management and Pl...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International conference on

作者： K. Bashkaran S. Selvarasu S. Valarmathy K. Radhika M. Muthulekshmi Department of Biomedical Engineering Kongunadu College of Engineering and Technology Thottium Tamil Nadu India Department of Electronics and Communication Engineering Sri Shanmugha College of Engineering and Technology Salem Tamil Nadu India Department of Electronics and Communication Engineering Vinayaka Mission's Kirupananda Variyar Engineering College Salem Tamil nadu India Department of Electronics and Communication Engineering Muthayammal Engineering College Rasipuram Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

Injuries in sports may be better managed, and player safety can be improved by using wireless sensor networks. All aspects of a player's performance may be tracked in realtime because of these networks' capacity for collecting, analyzing, and communicating data. Heart rate, body temperature, acceleration, and joint movement are just a few of the physiological and biomechanical factors investigated in this paper and relates to the use of wearable sensors and smart devices to monitor these variables. The data are sent wirelessly to a central system where sophisticated analytics are used to identify risks for injuries, track trends in performance, and allow for prompt action. Given the potential benefits to players' health and performance, the proposed system might bring to sports medicine, it's easy to view why it's getting so much attention. It suggests wireless sensor networks and the Internet of Things may work together to improve sports safety and injury prevention.

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Monitoring Vital Parameters of Comatose Patients using smart sensors Integrated with Cloud Storage

Monitoring Vital Parameters of Comatose Patients using Smart...

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International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： P. Sinthia Malathi. M Sripriya. T Rahul Krishnan Gurumoorthy. G K. Jalaldeen Dept. of Biomedical Engineering Saveetha Engineering College Chennai India Dept. of ECE Rajalakshmi Institute of Technology Chennai India Dept. of ECE Jeppiaar Institute of Technology Chennai India Dept. of ECE SRM Institute of Science and Technology Chennai India Dept. of Medical Electronics Saveetha Engineering College Chennai India Dept. of ECE Solamalai College of Engineering Chennai India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The monitoring of comatose patients is critical for identifying adverse changes in their physiological state and ensuring timely medical intervention. This research presents a novel IoT-based system for remotely monitoring and assessing vital signs in comatose patients. The proposed system utilizes various IoT sensors to measure pulse, respiratory rate, and eye movement, transmitting data to a centralized database and connected devices for real-time monitoring by healthcare professionals and family members. This approach offers a significant improvement over traditional observation methods, which can be time-consuming and prone to human error. By providing up-to-date information about the patient's vital signs and body movements, the IoT-based system enhances the quality of care and improves patient outcomes. This paper discusses the system's characteristics, building blocks, and implementation challenges based on existing research in this area.

关键词： Temperature sensors Cloud computing Pulse measurements Medical services sensor systems Real-time systems Reliability smart devices Monitoring Intelligent sensors

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IoMT & ML Model to Detect Stress in Human

IoMT & ML Model to Detect Stress in Human

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International conference on Computational and Characterization Techniques in Engineering & Sciences (CCTES)

作者： Bhaludra R Nadh Singh Sindhura Ghanta Thokala SaiShivani Basingala Sai Vaishnavi Polishetty Sravya Shaik Shifa Department of Computer Science and Engineering Bhoj Reddy Engineering College for Women Hyderabad India Department of Computer Science and Engineering Bhoj Reddy Engineering College Hyderabad India Department of Computer Science and Engineering Bhoj Reddy Engineering College Hyderabad Telangana India Department of Information Technology (IT) Bhoj Reddy Engineering College Hyderabad India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Today's fast-moving surroundings cause stress for heaps. Real-time stress administration may be troublesome, regardless of the allure of negative effects on tangible and insane fitness. ML, IoMT, and AI abilities change how we live and work and solve this question. "A smart biomedical Healthcare System to Detect Stress Utilizing IoT, Machine Learning and Artificial Intelligence," intends to utilize physiological sensors like soul rate, hotness, dampness (sweat) sensors and first verbalizations to detect stress. The submitted approach can path and categories folk into "difficult" and "non-tense" circumstances. Our classification results manifest the advantage of our approach in certain-occasion stress identification. The main aim of the original search out supports a novel procedure of identifying early stress something did surroundings, knowledge atmospheres, and administrative settings to support laborers’ and scholars' welfare and someday increase productivity

关键词： Temperature sensors Temperature measurement Anxiety disorders Real-time systems sensor systems Biosensors biomedical monitoring Intelligent sensors Stress Monitoring

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