ReviewSensors (Basel, Switzerland)2024
Machine Learning Applied to Edge Computing and Wearable Devices for Healthcare: Systematic Mapping of the Literature.
Review in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Balancing innovation and risk: artificial intelligence in travel medicine.Tropical diseases, travel medicine and vaccines · 2026Review
- From Biosignals to Bedside: A Review of Real-Time Edge Machine Learning for Wearable Health Monitoring.Bioengineering (Basel, Switzerland) · 2026Review
- Application of artificial intelligence in schizophrenia rehabilitation management: a systematic scoping review.Translational psychiatry · 2026Article
- Compact target-aware stability-based feature selection for cross-dataset wearable activity monitoring.PloS one · 2026Article
- An Attention-Residual Convolutional Network for Real-Time Seizure Classification on Edge Devices.Sensors (Basel, Switzerland) · 2025Article
- Development and Validation of an IMU Sensor-Based Behaviour-Alert Detection Collar for Assistance Dogs: A Proof-of-Concept Study.Animals : an open access journal from MDPI · 2025Article
- Skin-Inspired Healthcare Electronics.Biomimetics (Basel, Switzerland) · 2025Review
- The Role of Phase-Change Memory in Edge Computing and Analog In-Memory Computing: An Overview of Recent Research Contributions and Future Challenges.Sensors (Basel, Switzerland) · 2025Review
- Unsupervised clustering of pre-ictal EEG in children: a reproducible and lightweight CPU-based workflow.Frontiers in neurology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of machine learning (ML) with edge computing and wearable devices is rapidly advancing healthcare applications. This study systematically maps the literature in this emerging field, analyzing 171 studies and focusing on 28 key articles after rigorous selection. The research explores the key concepts, techniques, and architectures used in healthcare applications involving ML, edge computing, and wearable devices. The analysis reveals a significant increase in research over the past six years, particularly in the last three years, covering applications such as fall detection, cardiovascular monitoring, and disease prediction. The findings highlight a strong focus on neural network models, especially Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs), and diverse edge computing platforms like Raspberry Pi and smartphones. Despite the diversity in approaches, the field is still nascent, indicating considerable opportunities for future research. The study emphasizes the need for standardized architectures and the further exploration of both hardware and software to enhance the effectiveness of ML-driven healthcare solutions. The authors conclude by identifying potential research directions that could contribute to continued innovation in healthcare technologies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.