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. 2019 Dec 19;20(1):39.
doi: 10.3390/s20010039.

Physical Workload Tracking Using Human Activity Recognition with Wearable Devices

Jose Manjarres  1 , Pedro Narvaez  1 , Kelly Gasser  1 , Winston Percybrooks  1 , Mauricio Pardo  1
Affiliations

Physical Workload Tracking Using Human Activity Recognition with Wearable Devices

Jose Manjarres et al. Sensors (Basel). .

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.

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

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Human activity recognition hardware. The case allows the system to be worn on the hip.
Figure 2
Figure 2
User interface of the mobile application. (a) Training mode. (b) Testing mode.
Figure 3
Figure 3
Normalized confusion matrices for: (a) random forest (RF) classifier and (b) k-nearest neighbor (kNN) classifier.
Figure 4
Figure 4
Bar graph of the importance of the features in RF classifier.
Figure 5
Figure 5
Variation of the overall accuracy with the number of trees in the RF classifier.
Figure 6
Figure 6
Confusion matrix of the optimized RF classifier.
Figure 7
Figure 7
Confusion matrix from testing data.
Figure 8
Figure 8
Subject wearing the devices before exercising.
Figure 9
Figure 9
Physical workload tracking results for an individual after 20 days.
Figure 10
Figure 10
Physical workload assessment after each session.
See this image and copyright information in PMC

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