scholarly journals Predicting Emotion and Engagement of Workers in Order Picking Based on Behavior and Pulse Waves Acquired by Wearable Devices

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 165 ◽  
Author(s):  
Yusuke Kajiwara ◽  
Toshihiko Shimauchi ◽  
Haruhiko Kimura
Keyword(s):  
Exercise Intensity ◽  
Pulse Wave ◽  
Order Picking ◽  
Work Efficiency ◽  
Human Errors ◽  
Psychological Model ◽  
Related Research ◽  
Explanatory Variables ◽  
High Exercise Intensity ◽  
Pulse Waves

Many logistics companies adopt a manual order picking system. In related research, the effect of emotion and engagement on work efficiency and human errors was verified. However, related research has not established a method to predict emotion and engagement during work with high exercise intensity. Therefore, important variables for predicting the emotion and engagement during work with high exercise intensity are not clear. In this study, to clarify the mechanism of occurrence of emotion and engagement during order picking. Then, we clarify the explanatory variables which are important in predicting the emotion and engagement during work with high exercise intensity. We conducted verification experiments. We compared the accuracy of estimating human emotion and engagement by inputting pulse wave, eye movements, and movements to deep neural networks. We showed that emotion and engagement during order picking can be predicted from the behavior of the worker with an accuracy of error rate of 0.12 or less. Moreover, we have constructed a psychological model based on the questionnaire results and show that the work efficiency of workers is improved by giving them clear targets.

Cardiovascular Effects of Aerobic Exercise With Self-Selected or Predetermined Intensity in Adolescents With Obesity

2021 ◽  
pp. 1-7
Author(s):  
Tércio A.R. Barros ◽  
Wagner L. do Prado ◽  
Thiago R.S. Tenório ◽  
Raphael M. Ritti-Dias ◽  
Antônio H. Germano-Soares ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Body Mass Index ◽  
Heart Rate ◽  
Pulse Pressure ◽  
Exercise Intensity ◽  
Pulse Wave ◽  
Augmentation Index ◽  
Cardiovascular Effects ◽  
Heart Rate Reserve ◽  
Central Pulse Pressure

This study compared the effects of self-selected exercise intensity (SEI) versus predetermined exercise intensity (PEI) on blood pressure (BP) and arterial stiffness in adolescents with obesity. A total of 37 adolescents, 14.7 (1.6) years old, body mass index ≥95th percentile were randomly allocated into SEI (n = 18; 12 boys) or PEI (n = 19; 13 boys). Both groups exercised for 35 minutes on a treadmill, 3 times per week, for 12 weeks. The SEI could set the speed at the beginning of the sessions and make changes every 5 minutes. The PEI adolescents were trained at an intensity set at 60% to 70% of heart rate reserve. Brachial and central BP, pulse pressure, augmentation index, and carotid–femoral pulse wave were determined at baseline and after 12 weeks. Both groups reduced brachial systolic BP (SEI, Δ = −9 mm Hg; PEI, Δ = −4 mm Hg; P < .01), central systolic BP (SEI, Δ = −4 mm Hg; PEI, Δ = −4 mm Hg; P = .01), and central pulse pressure (SEI, Δ = −4 mm Hg; PEI, Δ = −3 mm Hg; P = .02) without differences between groups. No changes in the augmentation index and carotid–femoral pulse wave were observed in either group. The SEI induced similar changes in various cardiovascular outcomes compared with PEI in adolescents with obesity.

scholarly journals Ultrafast Ultrasound-Derived Muscle Strain Measure Correlates with Carotid Local Pulse Wave Velocity in Habitual Resistance-Trained Individuals

2021 ◽  
Vol 11 (18) ◽  
pp. 8783
Author(s):  
Hsin-Fu Lin ◽  
Yi-Hung Liao ◽  
Pai-Chi Li
Keyword(s):  
Pulse Wave Velocity ◽  
Resistance Exercise ◽  
Strain Rate ◽  
Wave Velocity ◽  
Exercise Intensity ◽  
Pulse Wave ◽  
Muscle Stiffness ◽  
Ultrasound Elastography ◽  
Muscle Strain ◽  
Post Exercise

Purpose: this study investigated the effects of the intensity of machine-based bicep curl resistance exercise on ultrafast ultrasound-derived muscle strain rate and carotid ultrafast pulse wave velocity (ufPWV), and examined the association between muscle strain rate, ufPWV, and established carotid function measures in habitual resistance-trained individuals. Methods: twenty-three young habitual resistance-trained males (age: 24 ± 1 year, body mass index = 24 ± 1 kg/m2) were recruited to participate in two bouts of acute bicep curl exercise. After one-repetition maximum determination (1RM), the participants were randomly assigned to engage in bicep curls at 40 or 80%1RM intensity (10 reps × five sets) by a crossover study design. The muscle strain rate of bicep muscle, carotid ufPWV during systole(ufPWV-sys), and diastole (ufPWV-dia) were obtained pre- and post-exercise. In addition, carotid function measures were calculated by obtained carotid diameter and central blood pressure changes. Results: compared with pre-exercise, the reduction in post-exercise muscle strain rate and its area under the curve of 80%1RM was greater than those of 40%1RM. Both ufPWV-sys and ufPWV-dia increased regardless of exercise intensity. Baseline bicep muscle strain rate correlated not only with ufPWV-sys (r = −0.71, p = 0.001), ufPWV-dia (r = −0.74, p = 0.001), but also carotid compliance (r = 0.49, p = 0.02), distensibility (r = 0.54, p = 0.01) and ß stiffness (r= −0.84, p < 0.0001). The ufPWVs also correlated with ß stiffness (r = 0.64–0.76, p = 0.01). Conclusion: muscle stiffness measured by ultrafast ultrasound elastography increases positively with resistance exercise intensity, and it appears to correlate with carotid ufPWV and established carotid function measures in habitual resistance-trained individuals.

scholarly journals A Robust Beat-to-Beat Artifact Detection Algorithm for Pulse Wave

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Qihan Hu ◽  
Xintao Deng ◽  
Xin Liu ◽  
Aiguo Wang ◽  
Cuiwei Yang
Keyword(s):  
Pulse Wave ◽  
Smart Cities ◽  
Detection Algorithm ◽  
Wearable Devices ◽  
Segmentation Algorithm ◽  
Artifact Detection ◽  
Physiological Conditions ◽  
Physiological Signal ◽  
Pulse Waves ◽  
Complex Algorithm

With the rise of the concept of smart cities and healthcare, artificial intelligence helps people pay increasing attention to the health of themselves. People can wear a variety of wearable devices to monitor their physiological conditions. The pulse wave is a kind of physiological signal which is widely applied in the physiological monitoring system. However, the pulse wave is susceptible to artifacts, which prevents its popularization. In this work, we propose a novel beat-to-beat artifact detection algorithm, which performs pulse wave segmentation based on wavelet transform and then detects artifacts beat by beat based on the decision list. We verified our method on data acquired from different databases and compared with experts’ annotations. The segmentation algorithm achieved an accuracy of 96.13%. When it is applied to detect main peaks, the performance achieved an accuracy of 99.11%. After the previous segmentation algorithm, the artifact detection algorithm can detect beat-to-beat pulse waves and artifacts with an accuracy of 98.11%. The result indicated that the proposed method is robust for pulse waves of different patterns and could effectively detect the artifact without the complex algorithm. In summary, our proposed algorithm is capable of annotating pulse waves of various patterns and determining pulse wave quality. Since our method is developed and evaluated on the transmission-mode PPG data, it is more suitable for the devices and applications inside the hospitals instead of reflectance-mode PPG.

Thermal responses during arm and leg and combined arm-leg exercise in water

1984 ◽  
Vol 56 (5) ◽  
pp. 1355-1360 ◽  
Author(s):  
M. M. Toner ◽  
M. N. Sawka ◽  
K. B. Pandolf
Keyword(s):  
Heat Flow ◽  
Exercise Intensity ◽  
Metabolic Responses ◽  
Low Intensity ◽  
Convective Heat Loss ◽  
High Exercise Intensity ◽  
Leg Exercise ◽  
Exercise Type ◽  
Type Of Exercise ◽  
Low Intensity Exercise

Thermal and metabolic responses were examined during exposures in stirred water at approximately 20, 26, and 33 degrees C while subjects were performing 45 min of either arm (A), leg (L), or combined arm-leg (AL) exercise. Eight males immersed to the neck completed a low exercise intensity for A exercise and both a low and high exercise intensity for L and AL exercise. During low-intensity exercise, final metabolic rate (M) for A, L, and AL exercise was not different (P greater than 0.05) between exercise type for each water temperature (Tw). In contrast final rectal temperatures (Tre) for A and AL exercise were significantly lower than L values for each Tw during low-intensity exercise. These findings were supported by both mean weighted skin temperature (Tsk) and mean weighted heat flow (Hc) values, which were greater during A than L for each Tw. During high-intensity exercise, final Tre values were lower (P less than 0.05) during AL compared with L exercise across all Tw. Final Tsk and Hc values were not different between each type of exercise, although M was significantly lower during L exercise in 20 degrees C water. These data suggest a greater conductive and convective heat loss during exercise utilizing the arms when compared with leg-only exercise.

Effects of exercise intensity and duration on norepinephrine spillover and clearance in humans

1993 ◽  
Vol 75 (2) ◽  
pp. 668-674 ◽  
Author(s):  
U. Leuenberger ◽  
L. Sinoway ◽  
S. Gubin ◽  
L. Gaul ◽  
D. Davis ◽  
...  
Keyword(s):  
Nervous System ◽  
Sympathetic Nervous System ◽  
Exercise Intensity ◽  
Time Course ◽  
Work Load ◽  
Dynamic Exercise ◽  
High Exercise Intensity ◽  
Sympathetic Nervous ◽  
Young Subjects ◽  
Norepinephrine Spillover

During dynamic exercise, blood flow to exercising muscle is closely matched to metabolic demands. This is made possible by metabolic vasodilation, vasoconstriction in inactive vascular beds, and a rise in cardiac output. The sympathetic nervous system plays an important role in regulating this exercise response. In this study, we used steady-state infusions of tritiated norepinephrine ([3H]NE) to determine the magnitude and time course of the arterial NE spillover response to sustained upright bicycle exercise at low (n = 11) and moderate-to-high (n = 14) exercise intensity (25 and 65% of maximum work load, respectively) in normal young subjects. In addition, we sought to examine whether exercise was associated with a change in NE clearance. During 30 min of low-level exercise, arterial NE spillover increased from 1.45 +/- 0.13 to 3.14 +/- 0.30 nmol.min-1 x m-2 (P < 0.01) and appeared to plateau at 20–30 min of exercise; NE clearance remained unchanged. During 20 min of moderate-to-high-intensity exercise, we found a substantial and progressive rise of arterial NE spillover from 2.15 +/- 0.27 to 13.52 +/- 1.62 nmol.min-1 x m-2 (P < 0.01). NE clearance decreased from 0.91 +/- 0.05 to 0.80 +/- 0.05 l.min-1 x m-2 (P < 0.05). These data suggest that, during dynamic exercise, sympathetic nervous system activity is related to exercise intensity, and there appears to be an interaction between the effects of exercise intensity and duration on NE spillover. In addition, at moderate-to-high exercise intensity, a small decrease of NE clearance contributes to the rise in plasma NE.

High Exercise Intensity Increases Myocellular Signaling In Resistance Exercise Modes Equalized For Time Under Tension

2014 ◽  
Vol 46 ◽  
pp. 306-307
Author(s):  
Sebastian Gehlert ◽  
Frank Suhr ◽  
Lena Willkomm ◽  
Daniel Jacko ◽  
Katrin Gutsche ◽  
...  
Keyword(s):  
Resistance Exercise ◽  
Exercise Intensity ◽  
High Exercise Intensity

scholarly journals Machine Learning–Based Signal Quality Evaluation of Single-Period Radial Artery Pulse Waves: Model Development and Validation (Preprint)

2020 ◽  
Author(s):  
Xiaodong Ding ◽  
Feng Cheng ◽  
Robert Morris ◽  
Cong Chen ◽  
Yiqin Wang
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Radial Artery ◽  
Pulse Wave ◽  
Disease Diagnosis ◽  
Recursive Feature Elimination ◽  
Physiological Parameter ◽  
Support Vector ◽  
External Interference ◽  
Pulse Waves

BACKGROUND The radial artery pulse wave is a widely used physiological signal for disease diagnosis and personal health monitoring because it provides insight into the overall health of the heart and blood vessels. Periodic radial artery pulse signals are subsequently decomposed into single pulse wave periods (segments) for physiological parameter evaluations. However, abnormal periods frequently arise due to external interference, the inherent imperfections of current segmentation methods, and the quality of the pulse wave signals. OBJECTIVE The objective of this paper was to develop a machine learning model to detect abnormal pulse periods in real clinical data. METHODS Various machine learning models, such as k-nearest neighbor, logistic regression, and support vector machines, were applied to classify the normal and abnormal periods in 8561 segments extracted from the radial pulse waves of 390 outpatients. The recursive feature elimination method was used to simplify the classifier. RESULTS It was found that a logistic regression model with only four input features can achieve a satisfactory result. The area under the receiver operating characteristic curve from the test set was 0.9920. In addition, these classifiers can be easily interpreted. CONCLUSIONS We expect that this model can be applied in smart sport watches and watchbands to accurately evaluate human health status.

scholarly journals Blood Pressure Model Based on Hybrid Feature Convolution Neural Network in Promoting Rehabilitation of Patients with Hypertensive Intracerebral Hemorrhage

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhixia Zheng ◽  
Limei Bai ◽  
Shaoquan Li
Keyword(s):  
Neural Network ◽  
Blood Pressure ◽  
Pulse Wave ◽  
Convolution Neural Network ◽  
Control Group ◽  
Propagation Time ◽  
Model Group ◽  
Pulse Wave Propagation ◽  
Pulse Waves ◽  
Hypertensive Intracerebral Hemorrhage

Objective. Accurate prediction of the rise of blood pressure is essential for the hypertensive intracerebral hemorrhage. This study uses the hybrid feature convolution neural network to establish the blood pressure model instead of the traditional method of pulse waves. Methods. The pulse waves of 100 patients were collected, and the pulse wave was decomposed into three bell wave compound forms to obtain the accurate pulse wave propagation time. Then, the mixed feature convolution neural network model ABP-net was proposed, which combined the pulse wave propagation time characteristics with the pulse wave waveform characteristics automatically extracted by one-dimensional convolution to predict the arterial blood pressure. Finally, according to the prediction results, 20 patients were treated before the high blood pressure appeared (model group), and another 20 patients with a daily fixed treatment scheme were selected as the control group. Results. In 80 training sets, compared with linear regression and the random forest method, the hybrid feature convolution neural network has higher accuracy in predicting blood pressure. In 20 test sets, the blood pressure error was eliminated within 5 mmHg. The total effective rate in the model group and the control group was 95.0% and 85.0%, respectively ( P = 0.035 ). After treatment, the scores of self-care ability of daily life and limb motor function in the model group were higher than those in the control group ( P < 0.05 ). There were 8 cases (13.6%) in the model group and 17 cases (28.3%) in the control group due to the recurrence of cerebrovascular accident ( P = 0.043 ). Conclusion. Drug treatment guided by a blood pressure model based on a hybrid feature convolution neural network for patients with hypertensive cerebral hemorrhage can significantly and smoothly reduce blood pressure, promote the health recovery, and reduce the occurrence of cerebrovascular accidents.

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