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Fusion of heart rate, respiration and motion measurements from a wearable sensor system to enhance energy expenditure estimation
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH). Institute of Environmental Medicine, Karolinska Institutet, Solnavägen 1, 171 77 Solna, Sweden.ORCID iD: 0000-0002-3256-9029
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Ergonomics.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH). Institute of Environmental Medicine, Karolinska Institutet, Solnavägen 1, 171 77 Solna, Sweden.
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2018 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 9, article id 3092Article in journal (Refereed) Published
Abstract [en]

This paper presents a new method that integrates heart rate, respiration, and motion information obtained from a wearable sensor system to estimate energy expenditure. The system measures electrocardiography, impedance pneumography, and acceleration from upper and lower limbs. A multilayer perceptron neural network model was developed, evaluated, and compared to two existing methods, with data from 11 subjects (mean age, 27 years, range, 21–65 years) who performed a 3-h protocol including submaximal tests, simulated work tasks, and periods of rest. Oxygen uptake was measured with an indirect calorimeter as a reference, with a time resolution of 15 s. When compared to the reference, the new model showed a lower mean absolute error (MAE = 1.65 mL/kg/min, R2 = 0.92) than the two existing methods, i.e., the flex-HR method (MAE = 2.83 mL/kg/min, R2 = 0.75), which uses only heart rate, and arm-leg HR+M method (MAE = 2.12 mL/kg/min, R2 = 0.86), which uses heart rate and motion information. As indicated, this new model may, in combination with a wearable system, be useful in occupational and general health applications. 

Place, publisher, year, edition, pages
MDPI AG , 2018. Vol. 18, no 9, article id 3092
Keywords [en]
Accelerometer, Energy expenditure, Impedance pneumography, Neural network, Wearable device, Accelerometers, Heart, Neural networks, Energy expenditure estimation, Mean absolute error, Motion measurements, Multi-layer perceptron neural networks, Wearable devices, Wearable sensor systems, Wearable sensors
National Category
Health Sciences
Identifiers
URN: urn:nbn:se:kth:diva-236691DOI: 10.3390/s18093092ISI: 000446940600351PubMedID: 30223429Scopus ID: 2-s2.0-85053711948OAI: oai:DiVA.org:kth-236691DiVA, id: diva2:1262471
Note

Export Date: 22 October 2018; Article; Correspondence Address: Ke, L.; School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Hälsovägen 11C, Sweden; email: kelu@kth.se; Funding details: 18454; Funding details: Dnr 150039; Funding text: Funding: This work was supported by AFA Insurance under Grant Dnr 150039, EIT Health under project no. 18454 “Wellbeing, Health and Safety @ Work”, and CSC Scholarship Council. QC 20181112

Available from: 2018-11-12 Created: 2018-11-12 Last updated: 2018-11-20Bibliographically approved
In thesis
1. Wearable Solutions for P-Health at Work: Precise, Pervasive and Preventive
Open this publication in new window or tab >>Wearable Solutions for P-Health at Work: Precise, Pervasive and Preventive
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

With a demographic change towards an older population, the structure of the labor force is shifting, and people are expected to work longer within their extended life span. However, for many people, wellbeing has been compromised by work-related problems before they reach the retirement age. Prevention of chronic diseases such as cardiovascular diseases and musculoskeletal disorders is needed to provide a sustainable working life. Therefore, pervasive tools for risk assessment and intervention are needed. The vision is to use wearable technologies to promote a sustainable work life, to be more detailed, to develop a system that integrates wearable technologies into workwear to provide pervasive and precise occupational disease prevention. This thesis presents some efforts towards this vision, including system-level design for a wearable risk assessment and intervention system, as well as specific insight into solutions for in-field assessment of physical workload and technologies to make smart sensing garments. The overall system is capable of providing unobtrusive monitoring of several signs, automatically estimating risk levels and giving feedback and reports to different stakeholders. The performance and usability of current energy expenditure estimation methods based on heart rate monitors and accelerometers were examined in occupational scenarios. The usefulness of impedance pneumography-based respiration monitoring for energy expenditure estimation was explored. A method that integrates heart rate, respiration and motion information using a neuronal network for enhancing the estimation is shown. The sensing garment is an essential component of the wearable system. Smart textile solutions that improve the performance, usability and manufacturability of sensing garments, including solutions for wiring and textile-electronics interconnection as well as an overall garment design that utilizes different technologies, are demonstrated.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2018. p. 42
Series
TRITA-CBH-FOU ; 2018-59
Keywords
wearable technology, occupational health, energy expenditure, smart textile
National Category
Medical Engineering
Research subject
Applied Medical Technology
Identifiers
urn:nbn:se:kth:diva-239156 (URN)978-91-7873-042-1 (ISBN)
Public defence
2018-12-10, Sal T2, Hälsovägen 11, Flemingsberg, 10:00 (English)
Opponent
Supervisors
Note

QC 20181119

Available from: 2018-11-19 Created: 2018-11-16 Last updated: 2018-11-19Bibliographically approved

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Lu, KeYang, LiyunForsman, Mikael

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