ArticleSensors (Basel, Switzerland)2023
A Classification Method for Workers' Physical Risk.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 13 citations in OpenAlex.
- Promoting active participation in robot-aided rehabilitation via machine learning and impedance control.Frontiers in digital health · 2025Article
- SenseRisc: An instrumented smart shirt for risk prevention in the workplace.Wearable technologies · 2025Article
- Biomechanical and physiological effects of passive upper limb exoskeletons in simulated manufacturing tasks.Wearable technologies · 2025Article
- An ML-Based Approach to Reconstruct Heart Rate from PPG in Presence of Motion Artifacts.Biosensors · 2023Article
- Affective state estimation based on Russell's model and physiological measurements.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In Industry 4.0 scenarios, wearable sensing allows the development of monitoring solutions for workers' risk prevention. Current approaches aim to identify the presence of a risky event, such as falls, when it has already occurred. However, there is a need to develop methods capable of identifying the presence of a risk condition in order to prevent the occurrence of the damage itself. The measurement of vital and non-vital physiological parameters enables the worker's complex state estimation to identify risk conditions preventing falls, slips and fainting, as a result of physical overexertion and heat stress exposure. This paper aims at investigating classification approaches to identify risk conditions with respect to normal physical activity by exploiting physiological measurements in different conditions of physical exertion and heat stress. Moreover, the role played in the risk identification by specific sensors and features was investigated. The obtained results evidenced that k-Nearest Neighbors is the best performing algorithm in all the experimental conditions exploiting only information coming from cardiorespiratory monitoring (mean accuracy 88.7±7.3% for the model trained with
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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.