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Stutz J, Eichenberger PA, Stumpf N, Knobel SEJ, Herbert NC, Hirzel I, Huber S, Oetiker C, Urry E, Lambercy O, Spengler CM. Energy expenditure estimation during activities of daily living in middle-aged and older adults using an accelerometer integrated into a hearing aid. Front Digit Health 2024; 6:1400535. [PMID: 38952746 PMCID: PMC11215182 DOI: 10.3389/fdgth.2024.1400535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 05/23/2024] [Indexed: 07/03/2024] Open
Abstract
Background Accelerometers were traditionally worn on the hip to estimate energy expenditure (EE) during physical activity but are increasingly replaced by products worn on the wrist to enhance wear compliance, despite potential compromises in EE estimation accuracy. In the older population, where the prevalence of hearing loss is higher, a new, integrated option may arise. Thus, this study aimed to investigate the accuracy and precision of EE estimates using an accelerometer integrated into a hearing aid and compare its performance with sensors simultaneously worn on the wrist and hip. Methods Sixty middle-aged to older adults (average age 64.0 ± 8.0 years, 48% female) participated. They performed a 20-min resting energy expenditure measurement (after overnight fast) followed by a standardized breakfast and 13 different activities of daily living, 12 of them were individually selected from a set of 35 activities, ranging from sedentary and low intensity to more dynamic and physically demanding activities. Using indirect calorimetry as a reference for the metabolic equivalent of task (MET), we compared the EE estimations made using a hearing aid integrated device (Audéo) against those of a research device worn on the hip (ZurichMove) and consumer devices positioned on the wrist (Garmin and Fitbit). Class-estimated and class-known models were used to evaluate the accuracy and precision of EE estimates via Bland-Altman analyses. Results The findings reveal a mean bias and 95% limit of agreement for Audéo (class-estimated model) of -0.23 ± 3.33 METs, indicating a slight advantage over wrist-worn consumer devices (Garmin: -0.64 ± 3.53 METs and Fitbit: -0.67 ± 3.40 METs). Class-know models reveal a comparable performance between Audéo (-0.21 ± 2.51 METs) and ZurichMove (-0.13 ± 2.49 METs). Sub-analyses show substantial variability in accuracy for different activities and good accuracy when activities are averaged over a typical day's usage of 10 h (+61 ± 302 kcal). Discussion This study shows the potential of hearing aid-integrated accelerometers in accurately estimating EE across a wide range of activities in the target demographic, while also highlighting the necessity for ongoing optimization efforts considering precision limitations observed across both consumer and research devices.
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Affiliation(s)
- Jan Stutz
- Exercise Physiology Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Philipp A. Eichenberger
- Exercise Physiology Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Nina Stumpf
- Research & Development, Sonova AG, Stäfa, Switzerland
| | | | | | - Isabel Hirzel
- Exercise Physiology Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Sacha Huber
- Exercise Physiology Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Chiara Oetiker
- Exercise Physiology Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Emily Urry
- Research & Development, Sonova AG, Stäfa, Switzerland
| | - Olivier Lambercy
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Christina M. Spengler
- Exercise Physiology Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Zurich Center for Integrative Human Physiology (ZIHP), University of Zurich, Zurich, Switzerland
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Hossain MB, LaMunion SR, Crouter SE, Melanson EL, Sazonov E. A CNN Model for Physical Activity Recognition and Energy Expenditure Estimation from an Eyeglass-Mounted Wearable Sensor. SENSORS (BASEL, SWITZERLAND) 2024; 24:3046. [PMID: 38793899 PMCID: PMC11125058 DOI: 10.3390/s24103046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/04/2024] [Accepted: 05/10/2024] [Indexed: 05/26/2024]
Abstract
Metabolic syndrome poses a significant health challenge worldwide, prompting the need for comprehensive strategies integrating physical activity monitoring and energy expenditure. Wearable sensor devices have been used both for energy intake and energy expenditure (EE) estimation. Traditionally, sensors are attached to the hip or wrist. The primary aim of this research is to investigate the use of an eyeglass-mounted wearable energy intake sensor (Automatic Ingestion Monitor v2, AIM-2) for simultaneous recognition of physical activity (PAR) and estimation of steady-state EE as compared to a traditional hip-worn device. Study data were collected from six participants performing six structured activities, with the reference EE measured using indirect calorimetry (COSMED K5) and reported as metabolic equivalents of tasks (METs). Next, a novel deep convolutional neural network-based multitasking model (Multitasking-CNN) was developed for PAR and EE estimation. The Multitasking-CNN was trained with a two-step progressive training approach for higher accuracy, where in the first step the model for PAR was trained, and in the second step the model was fine-tuned for EE estimation. Finally, the performance of Multitasking-CNN on AIM-2 attached to eyeglasses was compared to the ActiGraph GT9X (AG) attached to the right hip. On the AIM-2 data, Multitasking-CNN achieved a maximum of 95% testing accuracy of PAR, a minimum of 0.59 METs mean square error (MSE), and 11% mean absolute percentage error (MAPE) in EE estimation. Conversely, on AG data, the Multitasking-CNN model achieved a maximum of 82% testing accuracy in PAR, a minimum of 0.73 METs MSE, and 13% MAPE in EE estimation. These results suggest the feasibility of using an eyeglass-mounted sensor for both PAR and EE estimation.
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Affiliation(s)
- Md Billal Hossain
- Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA;
| | - Samuel R. LaMunion
- Department of Kinesiology, Recreation and Sport Studies, The University of Tennessee, Knoxville, TN 37996, USA; (S.R.L.); (S.E.C.)
| | - Scott E. Crouter
- Department of Kinesiology, Recreation and Sport Studies, The University of Tennessee, Knoxville, TN 37996, USA; (S.R.L.); (S.E.C.)
| | - Edward L. Melanson
- USA Division of Endocrinology, Metabolism, and Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA;
| | - Edward Sazonov
- Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA;
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Suau Q, Bianchini E, Bellier A, Chardon M, Milane T, Hansen C, Vuillerme N. Current Knowledge about ActiGraph GT9X Link Activity Monitor Accuracy and Validity in Measuring Steps and Energy Expenditure: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:825. [PMID: 38339541 PMCID: PMC10857518 DOI: 10.3390/s24030825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/22/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024]
Abstract
Over recent decades, wearable inertial sensors have become popular means to quantify physical activity and mobility. However, research assessing measurement accuracy and precision is required, especially before using device-based measures as outcomes in trials. The GT9X Link is a recent activity monitor available from ActiGraph, recognized as a "gold standard" and previously used as a criterion measure to assess the validity of various consumer-based activity monitors. However, the validity of the ActiGraph GT9X Link is not fully elucidated. A systematic review was undertaken to synthesize the current evidence for the criterion validity of the ActiGraph GT9X Link in measuring steps and energy expenditure. This review followed the PRISMA guidelines and eight studies were included with a combined sample size of 558 participants. We found that (1) the ActiGraph GT9X Link generally underestimates steps; (2) the validity and accuracy of the device in measuring steps seem to be influenced by gait speed, device placement, filtering process, and monitoring conditions; and (3) there is a lack of evidence regarding the accuracy of step counting in free-living conditions and regarding energy expenditure estimation. Given the limited number of included studies and their heterogeneity, the present review emphasizes the need for further validation studies of the ActiGraph GT9X Link in various populations and in both controlled and free-living settings.
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Affiliation(s)
- Quentin Suau
- AGEIS, Université Grenoble Alpes, 38000 Grenoble, France; (Q.S.); (A.B.); (M.C.); (T.M.); (C.H.)
| | - Edoardo Bianchini
- AGEIS, Université Grenoble Alpes, 38000 Grenoble, France; (Q.S.); (A.B.); (M.C.); (T.M.); (C.H.)
- Department of Neuroscience, Mental Health and Sensory Organs (NESMOS), Sapienza University of Rome, 00189 Rome, Italy
| | - Alexandre Bellier
- AGEIS, Université Grenoble Alpes, 38000 Grenoble, France; (Q.S.); (A.B.); (M.C.); (T.M.); (C.H.)
- CHU Grenoble Alpes, Université Grenoble Alpes, Inserm CIC 1406, 38000 Grenoble, France
| | - Matthias Chardon
- AGEIS, Université Grenoble Alpes, 38000 Grenoble, France; (Q.S.); (A.B.); (M.C.); (T.M.); (C.H.)
- UNESP Human Movement Research Laboratory (MOVI-LAB), Department of Physical Education, Bauru Sao Paulo State University, Bauru 17033-360, SP, Brazil
| | - Tracy Milane
- AGEIS, Université Grenoble Alpes, 38000 Grenoble, France; (Q.S.); (A.B.); (M.C.); (T.M.); (C.H.)
| | - Clint Hansen
- AGEIS, Université Grenoble Alpes, 38000 Grenoble, France; (Q.S.); (A.B.); (M.C.); (T.M.); (C.H.)
- Department of Neurology, Kiel University, 24105 Kiel, Germany
| | - Nicolas Vuillerme
- AGEIS, Université Grenoble Alpes, 38000 Grenoble, France; (Q.S.); (A.B.); (M.C.); (T.M.); (C.H.)
- LabCom Telecom4Health, Orange Labs & Université Grenoble Alpes, CNRS, Inria, Grenoble INP-UGA, 38000 Grenoble, France
- Institut Universitaire de France, 75005 Paris, France
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The association of moderate-to-vigorous and light-intensity physical activity on static balance in middle-aged and older-aged adults. Aging Clin Exp Res 2023; 35:541-550. [PMID: 36696017 DOI: 10.1007/s40520-023-02344-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 01/10/2023] [Indexed: 01/26/2023]
Abstract
BACKGROUND Falls are the leading cause of injury among adults ≥ 65 years of age. Participation in physical activity (PA) is associated with improved balance, though it is impact in the middle-age population is not well understood. AIM The purpose of the current study was to examine the influence of PA intensity on static balance in middle-aged and older aged individuals. METHODS Included were middle-aged adults (40-64 years) and older adults (≥ 65 years) from the 2003-2004 years of the National Health and Nutrition Evaluation Survey. Light physical activity (LPA) and moderate-vigorous physical activity (MVPA) were collected via accelerometer and static balance via the Romberg Test of Standing Balance. RESULTS No significant odds ratio relationship was found between MVPA or LPA and having good static balance in the middle-aged population; 1.04 (95% CI 0.95, 1.13) p = 0.427 and 1.05 (95% CI 0.97, 1.14) p = 0.182, respectively. Whereas, in older adults, every 60-min increase in LPA was significantly associated with 28% higher odds of good balance (95% CI 1.15, 1.41; p < 0.001), and every 10-min increase in MVPA with 25% higher odds of good balance (95% CI 1.08, 1.45; p = 0.006). DISCUSSION LPA and MVPA were not associated with good static balance in middle-aged adults, but in older adults LPA was significantly associated with good static balance. CONCLUSION A significant relationship is found between age and fall risk, which is a major concern in the aging population.
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Leone A, Rescio G, Diraco G, Manni A, Siciliano P, Caroppo A. Ambient and Wearable Sensor Technologies for Energy Expenditure Quantification of Ageing Adults. SENSORS 2022; 22:s22134893. [PMID: 35808387 PMCID: PMC9269397 DOI: 10.3390/s22134893] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/23/2022] [Accepted: 06/26/2022] [Indexed: 12/10/2022]
Abstract
COVID-19 has affected daily life in unprecedented ways, with dramatic changes in mental health, sleep time and level of physical activity. These changes have been especially relevant in the elderly population, with important health-related consequences. In this work, two different sensor technologies were used to quantify the energy expenditure of ageing adults. To this end, a technological platform based on Raspberry Pi 4, as an elaboration unit, was designed and implemented. It integrates an ambient sensor node, a wearable sensor node and a coordinator node that uses the information provided by the two sensor technologies in a combined manner. Ambient and wearable sensors are used for the real-time recognition of four human postures (standing, sitting, bending and lying down), walking activity and for energy expenditure quantification. An important first aim of this work was to realize a platform with a high level of user acceptability. In fact, through the use of two unobtrusive sensors and a low-cost processing unit, the solution is easily accessible and usable in the domestic environment; moreover, it is versatile since it can be used by end-users who accept being monitored by a specific sensor. Another added value of the platform is the ability to abstract from sensing technologies, as the use of human posture and walking activity for energy expenditure quantification enables the integration of a wide set of devices, provided that they can reproduce the same set of features. The obtained results showed the ability of the proposed platform to automatically quantify energy expenditure, both with each sensing technology and with the combined version. Specifically, for posture and walking activity classification, an average accuracy of 93.8% and 93.3% was obtained, respectively, with the wearable and ambient sensor, whereas an improvement of approximately 4% was reached using data fusion. Consequently, the estimated energy expenditure quantification always had a relative error of less than 3.2% for each end-user involved in the experimentation stage, classifying the high level information (postures and walking activities) with the combined version of the platform, justifying the proposed overall architecture from a hardware and software point of view.
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Ergonomic Assessment of Physical Load in Slovak Industry Using Wearable Technologies. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The physical tasks of workers are demanding, particularly when performed long-term in unsuitable working position, with high frequency, heavy load, after injury, with developing damage of health or reduced performance due to advanced age. Work-related musculoskeletal disorders (WMSDs) result from overuse or develop over time. Work activities, which are frequent and repetitive, or activities with awkward postures, cause disorders that may be painful during work or at rest. There is a new technology in the market, occupational exoskeletons, which have the prerequisites for minimizing the negative consequences of workload on WMSDs. We provided pilot quantitative measurements of the ergonomic risk at one selected workplace in a Slovak automotive company with four different workers to prove our methodology using wearable wireless multi-sensor systems Captiv and Actigraph. At first, the test was performed in standard conditions without an exoskeleton. The unacceptable physical load was identified in considerable evaluated body areas—neck, hip, and shoulder. Next, the passive chair exoskeleton Chairless Chair 2.0 was used in trials as an ergonomic measure. Our intention was to determine whether an exoskeleton would be an effective tool for optimizing the workload in selected workplaces and whether the proposed unique quantitative measurement system would give reliable and quick results.
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Gómez-García M, Torrado J, Bia D, Zócalo Y. Influence of Epoch Length and Recording Site on the Relationship Between Tri-Axial Accelerometry-Derived Physical Activity Levels and Structural, Functional, and Hemodynamic Properties of Central and Peripheral Arteries. Front Sports Act Living 2022; 4:799659. [PMID: 35280222 PMCID: PMC8909126 DOI: 10.3389/fspor.2022.799659] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 01/17/2022] [Indexed: 12/11/2022] Open
Abstract
BackgroundIt remains to be established to what extent physical activity (PA) levels among individuals are independently associated with deviations from the “optimal” state of the arterial system. Accelerometers have been proposed as means to obtain reliable, objective, and more comprehensive data of PA. Decisions at the time of data collection/processing could influence the association between accelerometry-derived indices and arterial properties.Objectives(i) To identify to what extent the strength of association between arterial properties and accelerometer-derived indices depend on the recording site and/or the epoch length; (ii) to determine whether some arterial characteristics (hemodynamic vs. structural vs. functional) or regions (elastic vs. transitional vs. muscular arteries; central vs. peripheral) have higher levels of association with accelerometry-derived indices.MethodsPhysical activity (PA), cardiovascular risk factors (CRFs), and cardiovascular properties were evaluated in 60 volunteers (general population; age: 23–62 years; women: 43%). PA was measured daily for 7 days (free-living situation; triaxial-accelerometers ActiGraph-GT3X+; hip and wrist; “Worn-to-wrist” option) and raw data was converted at epoch lengths of 1, 5, 10, 30, and 60-s. PA-related energy expenditure, daily time in moderate-to-vigorous PA, steps/minute, and counts-per-minute for vector magnitude were calculated. The cardiovascular evaluation included hemodynamic (central and peripheral pressure), structural (diameters and intima-media thickness), and functional (local and regional stiffness) parameters of carotids, femoral, and brachial arteries, and carotid-femoral and carotid-radial pathways. Arterial z-scores were obtained using age-related equations derived from healthy participants not exposed to CRFs (n = 1,688; age: 2–84 years; female: 51.2%) to evaluate at which degree each parameter deviates from the “optimal” value.MethodsIn general, hip recordings outperformed those obtained on the wrist regarding the strength of association with arterial parameters. Accelerometer-derived indices and their association with arterial properties vary depending on the recording site and epoch length. PA indices are stronger associated with functional (local) than structural variables and with central than peripheral arteries.ConclusionsRegardless of the PA index, there were independent associations with central artery characteristics, which reinforces that these territories would be the most related to PA levels. Differences in data acquisition and processing could lead to differences in conclusions when addressing the association between accelerometer-derived indices and the cardiovascular system.
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Affiliation(s)
- Mariana Gómez-García
- Departamento de Educación Física y Salud, Instituto Superior de Educación Física, Universidad de la República, Montevideo, Uruguay
- Grupo “Centro Universitario de Investigación, Innovación y Diagnóstico Arterial – Movimiento, Actividad, Salud” (CUiiDARTE-MAS), Comisión Sectorial de Investigación Científica (CSIC), Universidad de la República, Montevideo, Uruguay
| | - Juan Torrado
- Grupo “Centro Universitario de Investigación, Innovación y Diagnóstico Arterial – Movimiento, Actividad, Salud” (CUiiDARTE-MAS), Comisión Sectorial de Investigación Científica (CSIC), Universidad de la República, Montevideo, Uruguay
- Department of Internal Medicine, Jacobi Medical Center, Albert Einstein College of Medicine, New York, NY, United States
- Departamento de Fisiología, Facultad de Medicina, Centro Universitario de Investigación, Innovación y Diagnóstico Arterial (CUiiDARTE), Universidad de la República, Montevideo, Uruguay
| | - Daniel Bia
- Grupo “Centro Universitario de Investigación, Innovación y Diagnóstico Arterial – Movimiento, Actividad, Salud” (CUiiDARTE-MAS), Comisión Sectorial de Investigación Científica (CSIC), Universidad de la República, Montevideo, Uruguay
- Departamento de Fisiología, Facultad de Medicina, Centro Universitario de Investigación, Innovación y Diagnóstico Arterial (CUiiDARTE), Universidad de la República, Montevideo, Uruguay
| | - Yanina Zócalo
- Grupo “Centro Universitario de Investigación, Innovación y Diagnóstico Arterial – Movimiento, Actividad, Salud” (CUiiDARTE-MAS), Comisión Sectorial de Investigación Científica (CSIC), Universidad de la República, Montevideo, Uruguay
- Departamento de Fisiología, Facultad de Medicina, Centro Universitario de Investigación, Innovación y Diagnóstico Arterial (CUiiDARTE), Universidad de la República, Montevideo, Uruguay
- *Correspondence: Yanina Zócalo
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