Evaluation of physiological workload assessment methods using heart rate and accelerometry for a smart wearable system. Issue 5 (4th May 2019)

• Record Type: Journal Article
• Authors: Yang, Liyun; Lu, Ke; Forsman, Mikael; Lindecrantz, Kaj; Seoane, Fernando; Ekblom, Örjan; Eklund, Jörgen
• Is Part Of: Ergonomics. Volume 62:Issue 5(2019)
• Page Start: 694
• Page End: 705
• Publication Date: 2019-05-04
• Subjects: Heart rate -- work metabolism -- motion sensing -- wearable sensors -- risk assessment -- estimation models; Human engineering -- Periodicals; Cybernetics -- Periodicals; Industrial management -- Periodicals; Ergonomie -- Périodiques; Cybernétique -- Périodiques; Gestion d'entreprise -- Périodiques; 620.8205
• DOI: 10.1080/00140139.2019.1566579
• Languages: English
• ISSNs: 0014-0139

Abstract

Abstract: Work metabolism (WM) can be accurately estimated by oxygen consumption (VO2 ), which is commonly assessed by heart rate (HR) in field studies. However, the VO2 –HR relationship is influenced by individual capacity and activity characteristics. The purpose of this study was to evaluate three models for estimating WM compared with indirect calorimetry, during simulated work activities. The techniques were: the HR-Flex model; HR branched model, combining HR with hip-worn accelerometers (ACC); and HR + arm-leg ACC model, combining HR with wrist- and thigh-worn ACC. Twelve participants performed five simulated work activities and three submaximal tests. The HR + arm-leg ACC model had the overall best performance with limits of agreement (LoA) of −3.94 and 2.00 mL/min/kg, while the HR-Flex model had −5.01 and 5.36 mL/min/kg and the branched model, −6.71 and 1.52 mL/min/kg. In conclusion, the HR + arm-leg ACC model should, when feasible, be preferred in wearable systems for WM estimation. Practitioner Summary: Work with high energy demand can impair employees' health and life quality. Three models were evaluated for estimating work metabolism during simulated tasks. The model combining heart rate, wrist- and thigh-worn accelerometers showed the best accuracy. This is, when feasible, suggested for wearable systems to assess work metabolism.