Optimal gait and form for animal locomotion

Eadweard Muybridge's 1887 photographic atlas Animal Locomotion is a curious mixture of art and science, a polysemic text that has been subject to a number of readings. This paper focuses on Muybridge's technology. It seeks to understand his commitment to making photographs with a battery of cameras rather than a single camera. It suggests reasons for his choice of apparatus and shows how his final work, The Human Figure in Motion (1901), justifies the choices he made.

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Physical and Biological Properties and Principles

10.1093/oso/9780198743156.003.0001 ◽

2018 ◽

Keyword(s):

Biological Properties ◽

Animal Locomotion ◽

The Media ◽

Key Dimensions ◽

Scaling Analyses ◽

Physical Principles

Studies of animal locomotion are grounded in an understanding of the physical principles that govern how animals move and properties of the media through which they move. These studies, in turn, explain why certain biological devices, such as a wing or a fin, share features that have evolved for movement within their particular fluid environments. In this chapter, we examine the role of the environment and the fundamentals of loading and forces in animal mechanics. We offer a quick review of scaling analyses as well as the key dimensions and units used in this book to assist with your appreciation of the information.

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Croonian lecture—Aspects of animal locomotion

Proceedings of the Royal Society of London Series B Biological Sciences ◽

10.1098/rspb.1939.0043 ◽

1939 ◽

Vol 128 (850) ◽

pp. 28-62 ◽

Cited By ~ 15

Keyword(s):

Biological Activity ◽

Animal Life ◽

Intimate Contact ◽

Animal Locomotion ◽

Time Signals ◽

Marine Engineers ◽

Great Complexity

The power of locomotion is, perhaps, one of the most striking attributes of animal life. It occurs in all groups of animals and is characterized by two conspicuous features: (i) In no other biological activity is an animal brought into closer and more intimate contact with its environment. (ii) Closely related animals may display striking differences of locomotory pattern yet in every cast the animal is able to deal precisely and efficiently with mechanical problems of great complexity. For many years, the study of animal locomotion has been concerned with two, apparently distinct, types of problems. First, attention has been paid to the mechanical or kinematic principles which animals employ in order to progress from one place to another. In many terrestrial animals these principles are relatively simple, for their limbs represent levers of one type or another; in other cases the mechanical principles are more obscure—we know little concerning the kinematics of movement of a fish or a snail, and little or nothing of the forces which propel a bird actively through the air. These problems have long attracted attention and it is encouraging to know that they are now being attacked by methods as precise and as controlled as those employed by aeronautical or marine engineers. The second type of problem is of a different nature; it is concerned with physiological nature of the locomotory machine. What is the nature of the neuro-muscular mechanism which enables and animal to utilize its muscular energy with such conspicuous precision and efficiency? How far are the movements dependent on the higher nervous centres, and how far are they dependent on the receipt of time signals from the outside world?

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3D Reconstruction of Pedestrian Trajectory with Moving Direction Learning and Optimal Gait Recognition

Complexity ◽

10.1155/2018/8735846 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Binbin Wang ◽

Tingli Su ◽

Xuebo Jin ◽

Jianlei Kong ◽

Yuting Bai

Keyword(s):

Learning Algorithm ◽

Gait Recognition ◽

Vertical Direction ◽

Recognition Algorithm ◽

Measurement Unit ◽

Complex Environments ◽

Position Change ◽

Vertical Movements ◽

Moving Direction ◽

Optimal Gait

An inertial measurement unit-based pedestrian navigation system that relies on the intelligent learning algorithm is useful for various applications, especially under some severe conditions, such as the tracking of firefighters and miners. Due to the complexity of the indoor environment, signal occlusion problems could lead to the failure of certain positioning methods. In complex environments, such as those involving fire rescue and emergency rescue, the barometric altimeter fails because of the influence of air pressure and temperature. This paper used an optimal gait recognition algorithm to improve the accuracy of gait detection. Then a learning-based moving direction determination method was proposed. With the Kalman filter and a zero-velocity update algorithm, different gaits could be accurately recognized, such as going upstairs, downstairs, and walking flat. According to the recognition results, the position change in the vertical direction could be reasonably corrected. The obtained 3D trajectory involving both horizontal and vertical movements has shown that the accuracy is significantly improved in practical complex environments.

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