Early motor reactivity to observed human body postures is affected by body expression, not gender

Purpose Whilst motorcycling is an activity of pleasure in most parts of the world, in India, it is a regular mode of commuting. The number of registered motorized two wheelers increased at the rate of 14.7 percent during the year 2016-2017 to reach the figure of 20.19m in 2018. But, with this increase, the number of motorcycle road accidents is also increasing. Uncomfortable riding clothing is one of the major factors for motorcycle rider’s muscular fatigue, which might at times lead to serious accidents. No kinematic human models have been, so far, used for the design of protective, functional and aesthetic looking products, and the result is, hence, a compromised fit that is not protective or comfortable. The purpose of this paper is to develop virtual 3D human body models for specific postures of a motorcycle rider. Design/methodology/approach Kinematic analysis of a motorcycle rider was conducted to identify typical body postures obtained by the motorcycle rider while mounting and riding a motorcycle. The identified body postures were mapped on a virtual parametric human model to obtain digital model of a motorcycle rider. 3D garment patterns for jacket and trouser were developed on all the four body postures. 3D patterns were flattened out to get 2D flat patterns that were compared and analyzed, and appropriate pattern shapes from each of the four postures were selected. Virtual fit analysis was conducted for the finally garment. Findings It is well established that a static 2D anthropometry fails to accurately capture the dimensions of complex 3D human form, yielding poor garment fit. Therefore, in this study, virtual, 3D human body models were developed in selected dynamic poses. Garment patterns developed in 3D have the typical movement inbuilt in them; hence, they offer more comfort and ease of motion to the wearer. Originality/value The identification of typical body postures of motorcycle rider has not been done before. The CAD models developed in the study can be used for the generation of ergonomic garment patterns for the motorcycle riders.

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Real-time, 3D estimation of human body postures from trinocular images

Proceedings IEEE International Workshop on Modelling People. MPeople'99 ◽

10.1109/people.1999.798340 ◽

2003 ◽

Cited By ~ 20

Author(s):

S. Iwasawa ◽

J. Ohya ◽

K. Takahashi ◽

T. Sakaguchi ◽

S. Kawato ◽

...

Keyword(s):

Real Time ◽

Human Body ◽

Body Postures ◽

3D Estimation

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Non Invasive Skin Hydration Level Detection Using Machine Learning

Electronics ◽

10.3390/electronics9071086 ◽

2020 ◽

Vol 9 (7) ◽

pp. 1086 ◽

Cited By ~ 2

Author(s):

Sidrah Liaqat ◽

Kia Dashtipour ◽

Kamran Arshad ◽

Naeem Ramzan

Keyword(s):

Machine Learning ◽

Human Body ◽

Learning Algorithms ◽

The Elderly ◽

Machine Learning Algorithms ◽

Skin Hydration ◽

Body Postures ◽

Hydration Level ◽

Non Invasive ◽

Conductance Data

Dehydration and overhydration can help to improve medical implications on health. Therefore, it is vital to track the hydration level (HL) specifically in children, the elderly and patients with underlying medical conditions such as diabetes. Most of the current approaches to estimate the hydration level are not sufficient and require more in-depth research. Therefore, in this paper, we used the non-invasive wearable sensor for collecting the skin conductance data and employed different machine learning algorithms based on feature engineering to predict the hydration level of the human body in different body postures. The comparative experimental results demonstrated that the random forest with an accuracy of 91.3% achieved better performance as compared to other machine learning algorithms to predict the hydration state of human body. This study paves a way for further investigation in non-invasive proactive skin hydration detection which can help in the diagnosis of serious health conditions.

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