scholarly journals Fusion of Heart Rate, Respiration and Motion Measurements from a Wearable Sensor System to Enhance Energy Expenditure Estimation

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3092 ◽  
Author(s):  
Ke Lu ◽  
Liyun Yang ◽  
Fernando Seoane ◽  
Farhad Abtahi ◽  
Mikael Forsman ◽  
...  
Keyword(s):  
Heart Rate ◽  
Energy Expenditure ◽  
Absolute Error ◽  
Lower Limbs ◽  
Sensor System ◽  
Motion Information ◽  
Wearable Sensor ◽  
New Model ◽  
Impedance Pneumography ◽  
Work Tasks

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.

scholarly journals Deep Learning-Based Optimal Smart Shoes Sensor Selection for Energy Expenditure and Heart Rate Estimation

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7058
Author(s):  
Heesang Eom ◽  
Jongryun Roh ◽  
Yuli Sun Hariyani ◽  
Suwhan Baek ◽  
Sukho Lee ◽  
...  
Keyword(s):  
Heart Rate ◽  
Deep Learning ◽  
Energy Expenditure ◽  
Mean Squared Error ◽  
Pressure Sensors ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Proposed Model ◽  
Point Pressure

Wearable technologies are known to improve our quality of life. Among the various wearable devices, shoes are non-intrusive, lightweight, and can be used for outdoor activities. In this study, we estimated the energy consumption and heart rate in an environment (i.e., running on a treadmill) using smart shoes equipped with triaxial acceleration, triaxial gyroscope, and four-point pressure sensors. The proposed model uses the latest deep learning architecture which does not require any separate preprocessing. Moreover, it is possible to select the optimal sensor using a channel-wise attention mechanism to weigh the sensors depending on their contributions to the estimation of energy expenditure (EE) and heart rate (HR). The performance of the proposed model was evaluated using the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Moreover, the RMSE was 1.05 ± 0.15, MAE 0.83 ± 0.12 and R2 0.922 ± 0.005 in EE estimation. On the other hand, and RMSE was 7.87 ± 1.12, MAE 6.21 ± 0.86, and R2 0.897 ± 0.017 in HR estimation. In both estimations, the most effective sensor was the z axis of the accelerometer and gyroscope sensors. Through these results, it is demonstrated that the proposed model could contribute to the improvement of the performance of both EE and HR estimations by effectively selecting the optimal sensors during the active movements of participants.

Acceptability and effects of a seated active workstation during sedentary work

2014 ◽  
Vol 7 (1) ◽  
pp. 2-15 ◽  
Author(s):  
Lucas J. Carr ◽  
Hotaka Maeda ◽  
Brandon Luther ◽  
Patrick Rider ◽  
Sharon J. Tucker ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Cognitive Function ◽  
Energy Expenditure ◽  
Work Performance ◽  
Muscle Activation ◽  
Phase 1 ◽  
Phase 2 ◽  
Content Type ◽  
Work Tasks

Purpose – The purpose of this paper is to test the user acceptability (Phase 1) and effects (Phase 2) of completing sedentary work while using a seated active workstation. Design/methodology/approach – In Phase 1, 45 sedentary employees completed an acceptability questionnaire immediately after performing sedentary work tasks (typing, mousing) while using the seated active workstation for 30 minutes. In Phase 2, the paper tested the differential effects of completing sedentary work tasks at two different workstations (sedentary workstation vs seated active workstation) on physiological (energy expenditure, muscle activity, heart rate, blood pressure), cognitive (learning, memory, attention) and work performance (typing and mousing ability) outcomes among 18 sedentary employees. Findings – In Phase 1, 96 percent of participants reported they would use the seated active workstation “daily” if provided access in their office. In Phase 2, working while using the seated active workstation increased energy expenditure (p<0.001; d=3.49), heart rate (p<0.001; d=1.26), systolic blood pressure (p=0.02; d=0.79), and muscle activation of the biceps femoris (p<0.001; d=1.36) and vastus lateralis (p<0.001; d=1.88) over the sedentary workstation. No between-group differences were observed for any measures of cognitive function. Mouse point and click time was slower while using the seated active workstation (p=0.02). Research limitations/implications – These findings suggest this seated active workstation to be acceptable by users and effective for offsetting occupational sedentary time without compromising cognitive function and/or work performance. Originality/value – The present study is the first to test the potential of this seated active workstation in any capacity.

scholarly journals A Preliminary Test of Measurement of Joint Angles and Stride Length with Wireless Inertial Sensors for Wearable Gait Evaluation System

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Takashi Watanabe ◽  
Hiroki Saito ◽  
Eri Koike ◽  
Kazuki Nitta
Keyword(s):  
Evaluation System ◽  
Stride Length ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Preliminary Test ◽  
Lower Limbs ◽  
Sensor System ◽  
Wearable Sensor ◽  
Joint Angles ◽  
Optical Motion

The purpose of this study is to develop wearable sensor system for gait evaluation using gyroscopes and accelerometers for application to rehabilitation, healthcare and so on. In this paper, simultaneous measurement of joint angles of lower limbs and stride length was tested with a prototype of wearable sensor system. The system measured the joint angles using the Kalman filter. Signals from the sensor attached on the foot were used in the stride length estimation detecting foot movement automatically. Joint angles of the lower limbs were measured with stable and reasonable accuracy compared to those values measured with optical motion measurement system with healthy subjects. It was expected that the stride length measurement with the wearable sensor system would be practical by realizing more stable measurement accuracy. Sensor attachment position was suggested not to affect significantly measurement of slow and normal speed movements in a test with the rigid body model. Joint angle patterns measured in 10 m walking with a healthy subject were similar to common patterns. High correlation between joint angles at some characteristic points and stride velocity were also found adequately. These results suggested that the wireless wearable inertial sensor system could detect characteristics of gait.

Preliminary Evaluation of a Wearable Sensor System for Heart Rate Assessment in Guide Dog Puppies

2020 ◽  
Vol 20 (16) ◽  
pp. 9449-9459 ◽  
Author(s):  
Marc Foster ◽  
Rita Brugarolas ◽  
Katherine Walker ◽  
Sean Mealin ◽  
Zach Cleghern ◽  
...  
Keyword(s):  
Heart Rate ◽  
Sensor System ◽  
Preliminary Evaluation ◽  
Wearable Sensor

scholarly journals A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3719
Author(s):  
Aoxin Ni ◽  
Arian Azarang ◽  
Nasser Kehtarnavaz
Keyword(s):  
Heart Rate ◽  
Deep Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Video Camera ◽  
Measurement Methods ◽  
Rate Measurement ◽  
Learning Methods ◽  
The Public ◽  
Heart Rate Measurement

The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute.

scholarly journals Estimating Physical Activity Energy Expenditure Using an Ensemble Model-Based Patch-Type Sensor Module

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 861
Author(s):  
Kyeung Ho Kang ◽  
Mingu Kang ◽  
Siho Shin ◽  
Jaehyo Jung ◽  
Meina Li
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Ensemble Model ◽  
Direct Calorimetry ◽  
Patch Type ◽  
Mortality And Morbidity ◽  
Squared Error ◽  
Sensor Module

Chronic diseases, such as coronary artery disease and diabetes, are caused by inadequate physical activity and are the leading cause of increasing mortality and morbidity rates. Direct calorimetry by calorie production and indirect calorimetry by energy expenditure (EE) has been regarded as the best method for estimating the physical activity and EE. However, this method is inconvenient, owing to the use of an oxygen respiration measurement mask. In this study, we propose a model that estimates physical activity EE using an ensemble model that combines artificial neural networks and genetic algorithms using the data acquired from patch-type sensors. The proposed ensemble model achieved an accuracy of more than 92% (Root Mean Squared Error (RMSE) = 0.1893, R2 = 0.91, Mean Squared Error (MSE) = 0.014213, Mean Absolute Error (MAE) = 0.14020) by testing various structures through repeated experiments.

scholarly journals Contactless Video-Based Heart Rate Monitoring of a Resting and an Anesthetized Pig

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 442
Author(s):  
Meiqing Wang ◽  
Ali Youssef ◽  
Mona Larsen ◽  
Jean-Loup Rault ◽  
Daniel Berckmans ◽  
...  
Keyword(s):  
Heart Rate ◽  
Estimation Error ◽  
Well Being ◽  
Absolute Error ◽  
Mean Value ◽  
Video Data ◽  
Window Functions ◽  
The Face ◽  
Short Time ◽  
Better Than

Heart rate (HR) is a vital bio-signal that is relatively easy to monitor with contact sensors and is related to a living organism’s state of health, stress and well-being. The objective of this study was to develop an algorithm to extract HR (in beats per minute) of an anesthetized and a resting pig from raw video data as a first step towards continuous monitoring of health and welfare of pigs. Data were obtained from two experiments, wherein the pigs were video recorded whilst wearing an electrocardiography (ECG) monitoring system as gold standard (GS). In order to develop the algorithm, this study used a bandpass filter to remove noise. Then, a short-time Fourier transform (STFT) method was tested by evaluating different window sizes and window functions to accurately identify the HR. The resulting algorithm was first tested on videos of an anesthetized pig that maintained a relatively constant HR. The GS HR measurements for the anesthetized pig had a mean value of 71.76 bpm and standard deviation (SD) of 3.57 bpm. The developed algorithm had 2.33 bpm in mean absolute error (MAE), 3.09 bpm in root mean square error (RMSE) and 67% in HR estimation error below 3.5 bpm (PE3.5). The sensitivity of the algorithm was then tested on the video of a non-anaesthetized resting pig, as an animal in this state has more fluctuations in HR than an anaesthetized pig, while motion artefacts are still minimized due to resting. The GS HR measurements for the resting pig had a mean value of 161.43 bpm and SD of 10.11 bpm. The video-extracted HR showed a performance of 4.69 bpm in MAE, 6.43 bpm in RMSE and 57% in PE3.5. The results showed that HR monitoring using only the green channel of the video signal was better than using three color channels, which reduces computing complexity. By comparing different regions of interest (ROI), the region around the abdomen was found physiologically better than the face and front leg parts. In summary, the developed algorithm based on video data has potential to be used for contactless HR measurement and may be applied on resting pigs for real-time monitoring of their health and welfare status, which is of significant interest for veterinarians and farmers.

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