Physical Workload Tracking Using Human Activity Recognition with Wearable Devices

Sensors (Basel). 2019 Dec 19;20(1):39. 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.

Keywords: human activity recognition; machine learning for real-time applications; physical workload; wearable systems for healthcare.

MeSH terms

  • Accelerometry / instrumentation
  • Accelerometry / methods*
  • Adult
  • Exercise
  • Human Activities*
  • Humans
  • Machine Learning
  • Male
  • Micro-Electrical-Mechanical Systems
  • Wearable Electronic Devices*