Physical Workload Tracking Using Human Activity Recognition with Wearable Devices.
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.
['Manjarres|Jose|J|0000-0003-0532-9449', 'Narvaez|Pedro|P|', 'Gasser|Kelly|K|', 'Percybrooks|Winston|W|', 'Pardo|Mauricio|M|0000-0002-7608-3290']
D061725:Accelerometry D000328:Adult D015444:Exercise D006802:Human Activities D006801:Humans D000069550:Machine Learning D008297:Male D055617:Micro-Electrical-Mechanical Systems D000076251:Wearable Electronic Devices
physical workload human activity recognition machine learning for real-time applications wearable systems for healthcare