ArticleSensors (Basel, Switzerland)2019
Physical Workload Tracking Using Human Activity Recognition with Wearable Devices.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Wearable Devices for Ergonomics: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Engineering with Biomedical Sciences Changing the Horizon of Healthcare-A Review.Bioengineered · 2024Review
- Article
- Physical Exertion Recognition Using Surface Electromyography and Inertial Measurements for Occupational Ergonomics.Sensors (Basel, Switzerland) · 2023Article
- Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature.Diagnostics (Basel, Switzerland) · 2022Review
- Interpretable Passive Multi-Modal Sensor Fusion for Human Identification and Activity Recognition.Sensors (Basel, Switzerland) · 2022Article
- Assessing the Impact of Work Activities on the Physiological Load in a Sample of Loggers in Sicily (Italy).International journal of environmental research and public health · 2022Article
- Influence of Non-Occupational Physical Activity on Burnout Syndrome, Job Satisfaction, Stress and Recovery in Fitness Professionals.International journal of environmental research and public health · 2021Article
- Determining Physical Activity Characteristics from Wristband Data for Use in Automated Insulin Delivery Systems.IEEE sensors journal · 2020Article
- Transtheoretical Model Is Better Predictor of Physiological Stress than Perceived Stress Scale and Work Ability Index among Office Workers.International journal of environmental research and public health · 2020Article
- Data Analytics and Applications of the Wearable Sensors in Healthcare: An Overview.Sensors (Basel, Switzerland) · 2020Article
- A Model-Based Design Floating-Point Accumulator. Case of Study: FPGA Implementation of a Support Vector Machine Kernel Function.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
In this work, authors address workload computation combining human activity recognition and heart rate measurements to establish a scalable framework for health at work and fitness-related applications. The proposed architecture consists of two wearable sensors: one for motion, and another for heart rate. The system employs machine learning algorithms to determine the activity performed by a user, and takes a concept from ergonomics, the Frimat's score, to compute the corresponding physical workload from measured heart rate values providing in addition a qualitative description of the workload. A random forest activity classifier is trained and validated with data from nine subjects, achieving an accuracy of 97.5%. Then, tests with 20 subjects show the reliability of the activity classifier, which keeps an accuracy up to 92% during real-time testing. Additionally, a single-subject twenty-day physical workload tracking case study evinces the system capabilities to detect body adaptation to a custom exercise routine. The proposed system enables remote and multi-user workload monitoring, which facilitates the job for experts in ergonomics and workplace health.
Indexed as
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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.