{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T14:14:38Z","timestamp":1784211278715,"version":"3.55.0"},"reference-count":55,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,19]],"date-time":"2019-12-19T00:00:00Z","timestamp":1576713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007637","name":"Departamento Administrativo de Ciencia, Tecnolog\u00eda e Innovaci\u00f3n (COLCIENCIAS)","doi-asserted-by":"publisher","award":["809-2018"],"award-info":[{"award-number":["809-2018"]}],"id":[{"id":"10.13039\/100007637","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s20010039","type":"journal-article","created":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T03:15:01Z","timestamp":1577070901000},"page":"39","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Physical Workload Tracking Using Human Activity Recognition with Wearable Devices"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0532-9449","authenticated-orcid":false,"given":"Jose","family":"Manjarres","sequence":"first","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081001, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pedro","family":"Narvaez","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081001, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kelly","family":"Gasser","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081001, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Winston","family":"Percybrooks","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081001, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7608-3290","authenticated-orcid":false,"given":"Mauricio","family":"Pardo","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081001, Colombia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,19]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2003). 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