Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions

Future Internet, Journal Year: 2023, Volume and Issue: 15(11), P. 370 - 370

Published: Nov. 18, 2023

Edge AI, an interdisciplinary technology that enables distributed intelligence with edge devices, is quickly becoming a critical component in early health prediction. AI encompasses data analytics and artificial (AI) using machine learning, deep federated learning models deployed executed at the of network, far from centralized centers. careful analysis large datasets derived multiple sources, including electronic records, wearable demographic information, making it possible to identify intricate patterns predict person’s future health. Federated novel approach further enhances this prediction by enabling collaborative training on devices while maintaining privacy. Using computing, can be processed analyzed locally, reducing latency instant decision making. This article reviews role highlights its potential improve public Topics covered include use algorithms for detection chronic diseases such as diabetes cancer computing detect spread infectious diseases. In addition discussing challenges limitations prediction, emphasizes research directions address these concerns integration existing healthcare systems explore full technologies improving

Language: Английский

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Learning under label noise through few-shot human-in-the-loop refinement

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 4, 2025

Wearable technologies enable continuous monitoring of various health metrics, such as physical activity, heart rate, sleep, and stress levels. A key challenge with wearable data is obtaining quality labels. Unlike modalities like video where the videos themselves can be effectively used to label objects or events, do not contain obvious cues about manifestation users usually require rich metadata. As a result, noise become an increasingly thorny issue when labeling data. In this paper, we propose novel solution address noisy learning, entitled Few-Shot Human-in-the-Loop Refinement (FHLR). Our method initially learns seed model using weak Next, it fine-tunes handful expert corrections. Finally, achieves better generalizability robustness by merging fine-tuned models via weighted parameter averaging. We evaluate our approach on four challenging tasks datasets, compare against eight competitive baselines designed deal show that FHLR significantly performance learning from labels state-of-the-art large margin, up $$19\%$$ accuracy improvement under symmetric asymmetric noise. Notably, find particularly robust increased noise, unlike prior works suffer severe degradation. work only generalization in high-stakes sensing benchmarks but also sheds light how affects commonly-used models.

Language: Английский

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Digital health technology in clinical trials

npj Digital Medicine, Journal Year: 2023, Volume and Issue: 6(1)

Published: May 18, 2023

Digital health technologies (DHTs) have brought several significant improvements to clinical trials, enabling real-world data collection outside of the traditional context and more patient-centered approaches. DHTs, such as wearables, allow unique personal at home over a long period. But DHTs also bring challenges, digital endpoint harmonization disadvantaging populations already experiencing divide. A recent study explored growth trends implications established novel in neurology trials past decade. Here, we discuss benefits future challenges DHT usage trials.

Language: Английский

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Occupant-centered indoor environmental quality management: Physiological response measuring methods

Building and Environment, Journal Year: 2023, Volume and Issue: 243, P. 110661 - 110661

Published: July 24, 2023

Language: Английский

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Cyclical underreporting of seizures in patient‐based seizure documentation

Annals of Clinical and Translational Neurology, Journal Year: 2023, Volume and Issue: 10(10), P. 1863 - 1872

Published: Aug. 23, 2023

Circadian and multidien cycles of seizure occurrence are increasingly discussed as to their biological underpinnings in the context forecasting. This study analyzes if patient reported seizures provide valid data on such cyclical occurrence.We retrospectively studied circadian derived from patient-based reporting reflect objective documentation 2003 patients undergoing in-patient video-EEG monitoring.Only 24.1% more than 29000 documented were accompanied by notifications. There was underreporting with a maximum during nighttime, leading significant deviations distribution seizures. Significant found for focal epilepsies originating both, frontal temporal lobes, different types (in particular, unaware bilateral tonic-clonic seizures).Patient diaries may bias rather true distributions. Cyclical reports alone lead suboptimal treatment schemes, an underestimation seizure-associated risks, pose problems finding strongly supports use measures monitor distributions studies decisions based thereon.

Language: Английский

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A real-world dataset of group emotion experiences based on physiological data

Scientific Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: Jan. 23, 2024

Abstract Affective computing has experienced substantial advancements in recognizing emotions through image and facial expression analysis. However, the incorporation of physiological data remains constrained. Emotion recognition with shows promising results controlled experiments but lacks generalization to real-world settings. To address this, we present G-REx, a dataset for affective computing. We collected (photoplethysmography electrodermal activity) using wrist-worn device during long-duration movie sessions. annotations were retrospectively performed on segments elevated responses. The includes over 31 sessions, totaling 380 h+ from 190+ subjects. group setting, which can give further context emotion systems. Our setup aims be easily replicable any real-life scenario, facilitating collection large datasets novel

Language: Английский

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Photoplethysmography signal quality assessment using attractor reconstruction analysis

Biomedical Signal Processing and Control, Journal Year: 2023, Volume and Issue: 86, P. 105142 - 105142

Published: June 19, 2023

Language: Английский

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Evaluation of Data Processing and Artifact Removal Approaches Used for Physiological Signals Captured Using Wearable Sensing Devices during Construction Tasks

Journal of Construction Engineering and Management, Journal Year: 2023, Volume and Issue: 150(1)

Published: Oct. 25, 2023

Wearable sensing devices (WSDs) have enormous promise for monitoring construction worker safety. They can track workers and send safety-related information in real time, allowing more effective preventative decision making. WSDs are particularly useful on sites since they workers' health, safety, activity levels, among other metrics that could help optimize their daily tasks. may also assist recognizing health-related safety risks (such as physical fatigue) taking appropriate action to mitigate them. The data produced by these WSDs, however, is highly noisy contaminated with artifacts been introduced the surroundings, experimental apparatus, or subject's physiological state. These very strong frequently found during field experiments. So, when there a lot of artifacts, signal quality drops. Recently, removal has greatly enhanced developments processing, which vastly performance. Thus, proposed review aimed provide an in-depth analysis approaches currently used analyze remove from signals obtained via construction-related First, this study provides overview likely be recorded monitor health Second, identifies most prevalent detrimental effect utility signals. Third, comprehensive existing artifact-removal were presented. Fourth, each identified artifact detection approach was analyzed its strengths weaknesses. Finally, conclusion, few suggestions future research improving captured using approaches.

Language: Английский

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Home recording of 3‐Hz spike–wave discharges in adults with absence epilepsy using the wearable Sensor Dot

Epilepsia, Journal Year: 2023, Volume and Issue: 65(2), P. 378 - 388

Published: Dec. 1, 2023

Home monitoring of 3-Hz spike-wave discharges (SWDs) in patients with refractory absence epilepsy could improve clinical care by replacing the inaccurate seizure diary objective counts. We investigated use and performance Sensor Dot (Byteflies) wearable persons their home environment.

Language: Английский

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Patients with chronic cluster headache may show reduced activity energy expenditure on ambulatory wrist actigraphy recordings during daytime attacks

Brain and Behavior, Journal Year: 2024, Volume and Issue: 14(1)

Published: Jan. 1, 2024

Abstract Objective To investigate the changes in activity energy expenditure (AEE) throughout daytime cluster headache (CH) attacks patients with chronic CH and to evaluate usefulness of actigraphy as a digital biomarker attacks. Background is primary disorder characterized by severe very unilateral pain (orbital, supraorbital, temporal, or any combination these sites), ipsilateral cranial autonomic symptoms and/or sense restlessness agitation. We hypothesized increased AEE from hyperactivity during measured actigraphy. Methods An observational study including was conducted. During 21 days, wore an device on nondominant wrist recorded attack‐related data dedicated smartphone application. Accelerometer were used for calculation before that occurred ambulatory settings, without restrictions acute preventive treatment. compared movements pre‐ictal, ictal, postictal phases wrist‐worn time‐concordant intervals non‐headache periods. Results Four provided 34 attacks, which 15 met eligibility criteria further analysis. In contrast initial hypothesis decrease movement observed pre‐ictal phase (30 min onset onset) phase. A significant ( p < .01) proportion high‐intensity majority oxygen‐treated, observed. This trend less present low‐intensity movements. Conclusion The unexpected under treatment settings has important implications future research CH.

Language: Английский

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