Physical Workload Tracking Using Human Activity Recognition with Wearable Devices

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 1; p. 39
• Main Authors: Manjarres, Jose, Narvaez, Pedro, Gasser, Kelly, Percybrooks, Winston, Pardo, Mauricio
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 19.12.2019; MDPI
• Subjects: Accelerometers; Accelerometry - instrumentation; Accelerometry - methods; Adult; Algorithms; Electrocardiography; Exercise; Human Activities; Humans; Machine Learning; Male; Micro-Electrical-Mechanical Systems; Mobile communications networks; Occupational stress; Physical fitness; Sensors; Smartphones; Wearable computers; Wearable Electronic Devices; Workloads; physical workload; human activity recognition; wearable systems for healthcare; machine learning for real-time applications
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20010039

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.