The transfer function of human body on vertical sinusoidal vibration.

Kubo, Mitsunori; Aoki, Hiroyuki; Terauchi, Fumio

doi:10.5100/jje.32.29

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…Many people are exposed to whole-body vibration in vehicles: cars, buses, trains, ships and airplanes, on a daily basis. In our previous paper, it was confirmed that whole-body vibration caused a subject discomfort, fatigue and physical pains [1]. There are several reports describing how vibration interferes with people's working efficiency, safety and health [2].…”

Section: Introductionmentioning

confidence: 80%

Modeling of the Seated Human Body in a Vibrational Medium

Barbu

2014

AMM

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Vibrations are mechanical oscillations produced by regular or irregular period movements of a member or body about its rest position. Vibration can affect visual perception, muscles, concentration, circulation and the respiratory system and at certain levels can even result in physical harm to the body. The effect of vibration on the human body is related to the natural frequency of parts of the human body affected. This paper studies the dynamic characteristics of a seated human body system in a vibration environment. The main result is a multi degrees of freedom lumped parameter model. The model provided an analytical tool for human body dynamics research. It also enabled a primary tool for seat and cushioning design. Combining the geometry and the mechanical characteristics of a structure under large deformation into a lumped parameter model enables successful analysis of the human/seat interface system and provides practical results for body protection in dynamic environment. The relative displacements of human parts are evaluated, which can be a basis for the assessment of vibration risk. It is suggested that the multi-body dynamic model is used to evaluate the vibration effect to the seated subjects.

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Section: Introductionmentioning

confidence: 80%

Modeling of the Seated Human Body in a Vibrational Medium

Barbu

2014

AMM

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“…In the grid search, we performed leave-one-out crossvalidation using data from all subjects and obtained the hyperparameters that maximize the F-score. There were three hyperparameters that needed to be adjusted: threshold determination method (Gini coefficient, entropy), tree depth (1-10), and number of trees (1)(2)(3)(4)(5)(6)(7)(8)(9)(10). The other hyperparameters were the initial values of the scikit-learn random forest.…”

Section: Sleep Stage Estimatingmentioning

confidence: 99%

“…As this relationship with sleep stages has been shown, as well, the heart rate interval can be a factor that can be used to effectively estimate other stages of sleep beyond the distinction between being asleep and being awake. The human body also has resonance frequencies, as muscles and bones act as springs and dampers [ 10 ]. During REM sleep, all one’s muscles, such as eyes and respiratory muscles, are relaxed, so the human body’s vibrations are transmitted in minute movements such as breathing and BCG changes [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Estimating Sleep Stages Using a Head Acceleration Sensor

Yoshihi

Okada

Wang

et al. 2021

Sensors

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Sleep disruption from causes, such as changes in lifestyle, stress from aging, family issues, or life pressures are a growing phenomenon that can lead to serious health problems. As such, sleep disorders need to be identified and addressed early on. In recent years, studies have investigated sleep patterns through body movement information collected by wristwatch-type devices or cameras. However, these methods capture only the individual’s awake and sleep states and lack sufficient information to identify specific sleep stages. The aim of this study was to use a 3-axis accelerometer attached to an individual’s head to capture information that can identify three specific sleep stages: rapid eye movement (REM) sleep, light sleep, and deep sleep. These stages are measured by heart rate features captured by a ballistocardiogram and body movement. The sleep experiment was conducted for two nights among eight healthy adult men. According to the leave-one-out cross-validation results, the F-scores were: awake 76.6%, REM sleep 52.7%, light sleep 78.2%, and deep sleep 67.8%. The accuracy was 74.6% for the four estimates. This proposed measurement system was able to estimate the sleep stages with high accuracy simply by using the acceleration in the individual’s head.

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Nonlinear dual-axis biodynamic response of the semi-supine human body during vertical whole-body vibration

Huang

Griffin

2008

Journal of Sound and Vibration

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The transfer function of human body on vertical sinusoidal vibration.

Cited by 4 publications

References 2 publications

Modeling of the Seated Human Body in a Vibrational Medium

Modeling of the Seated Human Body in a Vibrational Medium

Estimating Sleep Stages Using a Head Acceleration Sensor

Nonlinear dual-axis biodynamic response of the semi-supine human body during vertical whole-body vibration

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