Energy Solutions for Wearable Sensors: A Review

Sensors, Journal Year: 2021, Volume and Issue: 21(11), P. 3806 - 3806

Published: May 31, 2021

Wearable sensors have gained popularity over the years since they offer constant and real-time physiological information about human body. been applied in a variety of ways clinical settings to monitor health conditions. These technologies require energy sources carry out their projected functionalities. In this paper, we review main used power wearable sensors. include batteries, solar cells, biofuel supercapacitors, thermoelectric generators, piezoelectric triboelectric radio frequency (RF) harvesters. Additionally, discuss wireless transfer some hybrids above technologies. The advantages drawbacks each technology are considered along with system components attributes that make these devices function effectively. objective is inform researchers latest developments field present future research opportunities.

Language: Английский

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Photoacoustic imaging for monitoring of stroke diseases: A review

Photoacoustics, Journal Year: 2021, Volume and Issue: 23, P. 100287 - 100287

Published: July 24, 2021

Stroke is the leading cause of death and disability after ischemic heart disease. However, there lacking a non-invasive long-time monitoring technique for stroke diagnosis therapy. The photoacoustic imaging approach reconstructs images an object based on energy excitation by optical absorption its conversion to acoustic waves, due corresponding thermoelastic expansion, which has resolution propagation. This emerging functional method technique. Due precision, this particularly attractive purpose. In paper, we review achievements technology applications stroke, as well development status in both animal human applications. Also, various systems multi-modality are introduced potential clinical Finally, challenges discussed.

Language: Английский

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Transforming Cardiovascular Risk Prediction: A Review of Machine Learning and Artificial Intelligence Innovations

Life, Journal Year: 2025, Volume and Issue: 15(1), P. 94 - 94

Published: Jan. 14, 2025

Cardiovascular diseases (CVDs) remain a leading cause of global mortality and morbidity. Traditional risk prediction models, while foundational, often fail to capture the multifaceted nature factors or leverage expanding pool healthcare data. Machine learning (ML) artificial intelligence (AI) approaches represent paradigm shift in prediction, offering dynamic, scalable solutions that integrate diverse data types. This review examines advancements AI/ML for CVD analyzing their strengths, limitations, challenges associated with clinical integration. Recommendations standardization, validation, future research directions are provided unlock potential these technologies transforming precision cardiovascular medicine.

Language: Английский

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Emerging Wearable Biosensor Technologies for Stress Monitoring and Their Real-World Applications

Biosensors, Journal Year: 2022, Volume and Issue: 12(12), P. 1097 - 1097

Published: Nov. 30, 2022

Wearable devices are being developed faster and applied more widely. Wearables have been used to monitor movement-related physiological indices, including heartbeat, movement, other exercise metrics, for health purposes. People also paying attention mental issues, such as stress management. can be emotional status provide preliminary diagnoses guided training functions. The nervous system responds stress, which directly affects eye movements sweat secretion. Therefore, the changes in brain potential, cortisol content could interpret changes, fatigue levels, psychological stress. To better assess users, stress-sensing integrated with applications improve cognitive function, attention, sports performance, learning ability, release. These application-related wearables medical diagnosis treatment, attention-deficit hyperactivity disorder (ADHD), traumatic syndrome, insomnia, thus facilitating precision medicine. However, many factors contribute data errors incorrect assessments, various wearable devices, sensor types, reception methods, processing accuracy algorithms, application reliability validity, actual user actions. future, platforms should developed, product implementations evaluated clinically confirm perform reliable research.

Language: Английский

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The status and perspectives of nanostructured materials and fabrication processes for wearable piezoresistive sensors

Microsystem Technologies, Journal Year: 2022, Volume and Issue: 28(7), P. 1561 - 1580

Published: March 17, 2022

Language: Английский

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Machine Learning-Based Wearable Devices for Smart Healthcare Application With Risk Factor Monitoring

Advances in logistics, operations, and management science book series, Journal Year: 2022, Volume and Issue: unknown, P. 174 - 185

Published: April 1, 2022

The stroke is an important health burden around the world that occurs due to block of blood supply brain. interruption depends on either sudden brain or a vessel leak in tissues. It tricky treat stroke-affected patients because accurate time unknown. Internet things (IoT) active field and plays major role prediction. Many machines learning (ML) techniques have been used automate process enable many detect prediction rate analyze risk factor. ML-based wearable device significant making real-time decisions benefit patients. parameters such as factors associated with sensors machine for are discussed.

Language: Английский

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NeuroCARE: A generic neuromorphic edge computing framework for healthcare applications

Frontiers in Neuroscience, Journal Year: 2023, Volume and Issue: 17

Published: Jan. 23, 2023

Highly accurate classification methods for multi-task biomedical signal processing are reported, including neural networks. However, reported works computationally expensive and power-hungry. Such bottlenecks make it hard to deploy existing approaches on edge platforms such as mobile wearable devices. Gaining motivation from the good performance high energy-efficiency of spiking networks (SNNs), a generic neuromorphic framework healthcare applications proposed evaluated various tasks, electroencephalography (EEG) based epileptic seizure prediction, electrocardiography (ECG) arrhythmia detection, electromyography (EMG) hand gesture recognition. This approach, NeuroCARE, uses unique sparse spike encoder generate sequences raw signals makes classifications using spike-based computing engine that combines advantages both CNN SNN. An adaptive weight mapping method specifically co-designed with can efficiently convert SNN without deterioration. The evaluation results show overall performance, accuracy, sensitivity F1 score, achieve 92.7, 96.7, 85.7% detection recognition, respectively. In comparison topologies, computation complexity is reduced by over 80.7% while energy consumption area occupation 80% 64.8%, respectively, indicating approach efficient precision, which paves way deployment at platforms.

Language: Английский

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Integrating AI-driven wearable devices and biometric data into stroke risk assessment: A review of opportunities and challenges

Clinical Neurology and Neurosurgery, Journal Year: 2024, Volume and Issue: 249, P. 108689 - 108689

Published: Dec. 10, 2024

Stroke is a leading cause of morbidity and mortality worldwide, early detection risk factors critical for prevention improved outcomes. Traditional stroke assessments, relying on sporadic clinical visits, fail to capture dynamic changes in such as hypertension atrial fibrillation (AF). Wearable technology (devices), combined with biometric data analysis, offers transformative approach by enabling continuous monitoring physiological parameters. This narrative review was conducted using systematic identify analyze peer-reviewed articles, reports, case studies from reputable scientific databases. The search strategy focused articles published between 2010 till date pre-determined keywords. Relevant were selected based their focus wearable devices AI-driven technologies prevention, diagnosis, rehabilitation. literature categorized thematically explore applications, opportunities, challenges, future directions. explores the current landscape assessment, focusing role detection, personalized care, integration into practice. highlights opportunities presented predictive analytics, where algorithms can provide tailored interventions. Personalized powered machine learning, enable individualized care plans. Furthermore, telemedicine facilitates remote patient rehabilitation, particularly underserved areas. Despite these advances, challenges remain. Issues accuracy, privacy concerns, wearables healthcare systems must be addressed fully realize potential. As evolves, its application could revolutionize improving outcomes reducing global burden stroke.

Language: Английский

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Integration of AI with artificial sensory systems for multidimensional intelligent augmentation

International Journal of Extreme Manufacturing, Journal Year: 2025, Volume and Issue: 7(4), P. 042002 - 042002

Published: March 27, 2025

Abstract Artificial sensory systems mimic the five human senses to facilitate data interaction between real and virtual worlds. Accurate analysis is crucial for converting external stimuli from each artificial sense into user-relevant information, yet conventional signal processing methods struggle with massive scale, noise, characteristics of generated by devices. Integrating intelligence (AI) essential addressing these challenges enhancing performance systems, making it a rapidly growing area research in recent years. However, no studies have systematically categorized output functions or analyzed associated AI algorithms methods. In this review, we present systematic overview latest techniques aimed at cognitive capabilities replicating senses: touch, taste, vision, smell, hearing. We categorize AI-enabled four key areas: simulation, perceptual enhancement, adaptive adjustment, early warning. introduce specialized raw function, designed enhance optimize sensing performance. Finally, offer perspective on future AI-integrated highlighting technical potential real-world application scenarios further innovation. Integration will enable advanced multimodal perception, real-time learning, predictive capabilities. This drive precise environmental adaptation personalized feedback, ultimately positioning as foundational technologies smart healthcare, agriculture, automation.

Language: Английский

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Advancing Post‐Stroke Rehabilitation: Emerging and Current Neuromodulation Approaches and Integration of Artificial Intelligence‐Driven Closed‐Loop Systems

Published: April 1, 2025

ABSTRACT Chronic stroke represents a significant global health burden, requiring innovative rehabilitation strategies that extend beyond conventional therapies. Neuromodulation, including transcutaneous vagus nerve stimulation, deep brain and brain–computer interfaces, has emerged as transformative approach, leveraging neuroplasticity to enhance motor cognitive recovery. Integrating artificial intelligence (AI) within these modalities enables adaptive, patient‐specific interventions through real‐time feedback, predictive modeling, advanced signal processing. This perspective article provides comparative analysis of neuromodulation techniques, examines clinical evidence, while also identifying AI‐centric research priorities address current challenges.

Language: Английский

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