Trends in Workplace Wearable Technologies and Connected‐Worker Solutions for Next‐Generation Occupational Safety, Health, and Productivity

Advanced Intelligent Systems, Journal Year: 2021, Volume and Issue: 4(1)

Published: Sept. 23, 2021

The workplace influences the safety, health, and productivity of workers at multiple levels. To protect promote total worker smart hardware, software tools have emerged for identification, elimination, substitution, control occupational hazards. Wearable devices enable constant monitoring individual environment, whereas connected solutions provide contextual information decision support. Here, recent trends in commercial technologies to monitor manage risks, injuries, accidents, diseases are reviewed. Workplace safety wearables safe lifting, ergonomics, hazard sleep monitoring, fatigue management, heat cold stress discussed. Examples asset tracking, augmented reality, gesture motion control, brain wave sensing, work management given. health designed work-related musculoskeletal disorders, functional movement respiratory hazards, cardiovascular outdoor sun exposure, continuous glucose shown. Connected platforms discussed with about architecture, system modules, intelligent operations, industry applications. Predictive analytics resource allocation, equipment failure, predictive maintenance. Altogether, these examples highlight ground-level benefits real-time visibility frontline workers, distributed assets, workforce efficiency, compliance

Language: Английский

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Smartwatches in healthcare medicine: assistance and monitoring; a scoping review

BMC Medical Informatics and Decision Making, Journal Year: 2023, Volume and Issue: 23(1)

Published: Nov. 3, 2023

Abstract Smartwatches have become increasingly popular in recent times because of their capacity to track different health indicators, including heart rate, patterns sleep, and physical movements. This scoping review aims explore the utilisation smartwatches within healthcare sector. According Arksey O'Malley's methodology, an organised search was performed PubMed/Medline, Scopus, Embase, Web Science, ERIC Google Scholar. In our strategy, 761 articles were returned. The exclusion/inclusion criteria applied. Finally, 35 selected for extracting data. These included six studies on stress monitoring, movement disorders, three sleep tracking, blood pressure, two disease, covid pandemic, safety validation. use has been found be effective diagnosing symptoms various diseases. particular, shown promise detecting diseases, even early signs COVID-19. Nevertheless, it should emphasised that there is ongoing discussion concerning reliability smartwatch diagnoses systems. Despite potential advantages offered by utilising disease detection, imperative approach data interpretation with prudence. discrepancies detection between algorithms important implications use. accuracy used are crucial, as well high changes status themselves. calls development medical watches creation AI-hospital assistants. assistants will designed help patient appointment scheduling, medication management tasks. They can educate patients answer common questions, freeing providers focus more complex

Language: Английский

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ChatDiet: Empowering personalized nutrition-oriented food recommender chatbots through an LLM-augmented framework

Smart Health, Journal Year: 2024, Volume and Issue: 32, P. 100465 - 100465

Published: March 24, 2024

Language: Английский

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Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members’ vision – part 2

The Journal of Headache and Pain, Journal Year: 2025, Volume and Issue: 26(1)

Published: Jan. 2, 2025

Part 2 explores the transformative potential of artificial intelligence (AI) in addressing complexities headache disorders through innovative approaches, including digital twin models, wearable healthcare technologies and biosensors, AI-driven drug discovery. Digital twins, as dynamic representations patients, offer opportunities for personalized management by integrating diverse datasets such neuroimaging, multiomics, sensor data to advance research, optimize treatment, enable virtual trials. In addition, devices equipped with next-generation biosensors combined multi-agent chatbots could real-time physiological biochemical monitoring, diagnosing, facilitating early attack forecasting prevention, disease tracking, interventions. Furthermore, advances discovery leverage machine learning generative AI accelerate identification novel therapeutic targets treatment strategies migraine other disorders. Despite these advances, challenges standardization, model explainability, ethical considerations remain pivotal. Collaborative efforts between clinicians, biomedical biotechnological engineers, scientists, legal representatives bioethics experts are essential overcoming barriers unlocking AI's full transforming research healthcare. This is a call action proposing frameworks AI-based into care.

Language: Английский

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Discrepancies between subjective and objective sleep assessments revealed by in-home electroencephalography during real-world sleep

Proceedings of the National Academy of Sciences, Journal Year: 2025, Volume and Issue: 122(3)

Published: Jan. 16, 2025

Sleep insufficiency and sleep disorders pose serious health challenges. This study aimed to determine the potential discrepancy between subjective objective assessments, including latter made by physicians, analyzing a 421-participant dataset in Japan comprising multiple nights of in-home electroencephalogram (EEG) data questionnaire responses on habits or experiences. We employed logistic regression models examine which parameters physicians are paying attention when assessing insufficiency, insomnia, quality, apnea. Questionnaire responses, exhibited poor performance predicting physicians’ whereas demonstrated good predictive performance, indicating assessments. Although EEG measurements had minimal first night effects, incorporating over can improve detection insomnia. Moreover, we found that participants with severe overestimated their duration, those insomnia but without underestimated it. Additionally, quality reflected efficiency not frequency short awakenings depth. In particular, effects apnea were subjectively perceived. Collectively, our findings suggest assessments alone insufficient for evaluating checkups advice based may be useful improving early disorders.

Language: Английский

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Accuracy Assessment of Oura Ring Nocturnal Heart Rate and Heart Rate Variability in Comparison With Electrocardiography in Time and Frequency Domains: Comprehensive Analysis

Journal of Medical Internet Research, Journal Year: 2021, Volume and Issue: 24(1), P. e27487 - e27487

Published: Nov. 9, 2021

Background Photoplethysmography is a noninvasive and low-cost method to remotely continuously track vital signs. The Oura Ring compact photoplethysmography-based smart ring, which has recently drawn attention remote health monitoring wellness applications. ring used acquire nocturnal heart rate (HR) HR variability (HRV) parameters ubiquitously. However, these are highly susceptible motion artifacts environmental noise. Therefore, validity assessment of the required in everyday settings. Objective This study aims evaluate accuracy time domain frequency HRV collected by against medical grade chest electrocardiogram monitor. Methods We conducted overnight home-based using an Shimmer3 device. 35 healthy individuals were assessed. evaluated within 2 tests, that is, values from 5-minute recordings (ie, short-term analysis) average per night sleep. A linear regression method, Pearson correlation coefficient, Bland–Altman plot compare measurements devices. Results Our findings showed low mean biases both average-per-night tests. In test, error variances different. provided dashboard root square successive differences [RMSSD]) relatively variance compared with extracted normal interbeat interval signals. coefficient tests (P<.001) indicated HR, RMSSD, beat intervals (AVNN), percentage beat-to-beat differ more than 50 ms (pNN50) had high positive correlations baseline values; SD (SDNN) (HF) moderate correlations, (LF) LF:HF ratio correlations. AVNN, pNN50 narrow 95% CIs; however, SDNN, LF, HF, wider CIs. contrast, test pNN50, HF relationships (P<.001), relationship (P<.001). also considerably lower for parameters. Conclusions could accurately measure RMSSD It acceptable SDNN but not test. LF rates

Language: Английский

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Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation

Sensors, Journal Year: 2021, Volume and Issue: 21(7), P. 2281 - 2281

Published: March 24, 2021

Pregnancy is a unique time when many mothers gain awareness of their lifestyle and its impacts on the fetus. High-quality care during pregnancy needed to identify possible complications early ensure mother’s her unborn baby’s health well-being. Different studies have thus far proposed maternal monitoring systems. However, they are designed for specific problem or limited questionnaires short-term data collection methods. Moreover, requirements challenges not been evaluated in long-term studies. Maternal necessitates comprehensive framework enabling continuous pregnant women. In this paper, we present an Internet-of-Things (IoT)-based system provide ubiquitous postpartum. The consists various collectors track condition, including stress, sleep, physical activity. We carried out full implementation conducted real human subject study women Southwestern Finland. then system’s feasibility, energy efficiency, reliability. Our results show that implemented feasible terms usage nine months. also indicate smartwatch, used our study, has acceptable efficiency able collect reliable photoplethysmography data. Finally, discuss integration presented with current healthcare system.

Language: Английский

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Human NREM Sleep Promotes Brain-Wide Vasomotor and Respiratory Pulsations

Journal of Neuroscience, Journal Year: 2022, Volume and Issue: 42(12), P. 2503 - 2515

Published: Feb. 8, 2022

The physiological underpinnings of the necessity sleep remain uncertain. Recent evidence suggests that increases convection cerebrospinal fluid (CSF) and promotes export interstitial solutes, thus providing a framework to explain why all vertebrate species require sleep. Cardiovascular, respiratory vasomotor brain pulsations have each been shown drive CSF flow along perivascular spaces, yet it is unknown how such may change during in humans. To investigate these pulsation phenomena relation sleep, we simultaneously recorded fast fMRI, magnetic resonance encephalography (MREG), electroencephalography (EEG) signals group healthy volunteers. We quantified sleep-related changes signal frequency distributions by spectral entropy analysis calculated strength (vasomotor, respiratory, cardiac) power sum 15 subjects (age 26.5 ± 4.2 years, 6 females). Finally, identified spatial similarities between EEG slow oscillation (0.2–2 Hz) MREG pulsations. Compared with wakefulness, nonrapid eye movement (NREM) was characterized reduced increased intensity. These effects were most pronounced posterior areas for very low-frequency (≤0.1 but also evident brain-wide pulsations, lesser extent cardiac There regions spatially overlapping those showing changes. suggest enhanced intensity are characteristic NREM With our findings oscillation, present results support proposition transport human brain. SIGNIFICANCE STATEMENT report mechanisms driven vasomotor, respiration, rhythms increase extending previous observations their association glymphatic clearance rodents. magnitudes follow rank order greater than correspondingly declining extents. Spectral entropy, previously known as vigilance an anesthesia metric, decreased compared awake state low frequencies, indicating complexity. An occurring early phase (NREM 1–2) overlapped changes, reciprocal measures.

Language: Английский

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Emognition dataset: emotion recognition with self-reports, facial expressions, and physiology using wearables

Scientific Data, Journal Year: 2022, Volume and Issue: 9(1)

Published: April 7, 2022

Abstract The Emognition dataset is dedicated to testing methods for emotion recognition (ER) from physiological responses and facial expressions. We collected data 43 participants who watched short film clips eliciting nine discrete emotions: amusement, awe, enthusiasm, liking, surprise, anger, disgust, fear, sadness. Three wearables were used record data: EEG, BVP (2x), HR, EDA, SKT, ACC (3x), GYRO (2x); in parallel with the upper-body videos. After each clip, completed two types of self-reports: (1) related emotions (2) three affective dimensions: valence, arousal, motivation. obtained facilitates various ER approaches, e.g., multimodal ER, EEG- vs. cardiovascular-based dimensional representation transitions. technical validation indicated that watching elicited targeted emotions. It also supported signals’ high quality.

Language: Английский

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Smart Consumer Wearables as Digital Diagnostic Tools: A Review

Diagnostics, Journal Year: 2022, Volume and Issue: 12(9), P. 2110 - 2110

Published: Aug. 31, 2022

The increasing usage of smart wearable devices has made an impact not only on the lifestyle users, but also biological research and personalized healthcare services. These devices, which carry different types sensors, have emerged as digital diagnostic tools. Data from such enabled prediction detection various physiological well psychological conditions diseases. In this review, we focused applications wrist-worn wearables to detect multiple diseases cardiovascular diseases, neurological disorders, fatty liver metabolic including diabetes, sleep quality, illnesses. fruitful requires fast insightful data analysis, is feasible through machine learning. discussed machine-learning outcomes for analyses. Finally, current challenges with data, future perspectives tools domains.

Language: Английский

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