Intelligent Human Interface by means of Motion Recognition

This chapter presents an alternative to the popular critical vein that sees Joyce’s Ulysses and early cinema as conveying a mechanical, impersonal view of the world. It is argued that Ulysses and certain genres of early cinema were engaged—naively or otherwise—in a revaluation of Cartesian dualism, involving the reappraisal of mind/body and human/machine binaries. The physical comedy of Bloom and Charlie Chaplin is analysed with reference to phenomenological ideas on prosthesis and the machine–human interface, while other genres of early cinema, such as Irish melodrama and trick films, are considered in the light of phenomenological theories of gesture and embodiment. By comically mocking mind/body separation and depicting the inseparability of subjectivity and corporeality, Joyce and the early film-makers go beyond the ideas of Bergson and anticipate Merleau-Ponty’s later notion of the ‘body-subject’.

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Radar Human Motion Recognition Using Motion States and Two-Way Classifications

2020 IEEE International Radar Conference (RADAR) ◽

10.1109/radar42522.2020.9114613 ◽

2020 ◽

Cited By ~ 1

Author(s):

Moeness G. Amin ◽

Ronny G. Guendel

Keyword(s):

Human Motion ◽

Motion Recognition ◽

Human Motion Recognition

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Improved One-Dimensional Convolutional Neural Networks for Human Motion Recognition

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) ◽

10.1109/bibm49941.2020.9313296 ◽

2020 ◽

Author(s):

Shengzhi Wang ◽

Shuo Xiao ◽

Zhenzhen Huang ◽

Zhiou Xu ◽

Wei Chen

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Human Motion ◽

Motion Recognition ◽

One Dimensional ◽

Human Motion Recognition

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A Manufacturing Systems Engineering View of IT Research and Development

Journal of Information Technology ◽

10.1177/026839628700200303 ◽

1987 ◽

Vol 2 (3) ◽

pp. 114-118

Author(s):

M.W. Dale

Keyword(s):

Information Technology ◽

Research And Development ◽

Systems Engineering ◽

Manufacturing Systems ◽

Technology Development ◽

Human Interface ◽

Real World Applications ◽

Computing Skills ◽

Information Technologists ◽

Information Technology Development

This paper presents a manufacturing systems engineering view of important issues relating to IT research and development. It argues for an approach to the next phase of information technology development which is heavily based on real-world applications with the dominant influences held by educated users and engineers who have added computing skills, rather than information technologists. It argues for ‘consolidation’ with particular attention to total systems integration and an emphasis on the need to professionally engineer the human interface.

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Comparison of machine learning methods in sEMG signal processing for shoulder motion recognition

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2021.102577 ◽

2021 ◽

Vol 68 ◽

pp. 102577

Author(s):

Yang Zhou ◽

Chaoyang Chen ◽

Mark Cheng ◽

Yousef Alshahrani ◽

Sreten Franovic ◽

...

Keyword(s):

Machine Learning ◽

Signal Processing ◽

Learning Methods ◽

Semg Signal ◽

Motion Recognition ◽

Machine Learning Methods ◽

Shoulder Motion

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Application of human motion recognition technology in extreme learning machine

International Journal of Advanced Robotic Systems ◽

10.1177/1729881420983219 ◽

2021 ◽

Vol 18 (1) ◽

pp. 172988142098321

Author(s):

Anzhu Miao ◽

Feiping Liu

Keyword(s):

Extreme Learning Machine ◽

Research Work ◽

Human Motion ◽

Feature Descriptor ◽

Background Variable ◽

Motion Recognition ◽

Network Training ◽

Other Information ◽

Human Motion Recognition ◽

Learning Machine

Human motion recognition is a branch of computer vision research and is widely used in fields like interactive entertainment. Most research work focuses on human motion recognition methods based on traditional video streams. Traditional RGB video contains rich colors, edges, and other information, but due to complex background, variable illumination, occlusion, viewing angle changes, and other factors, the accuracy of motion recognition algorithms is not high. For the problems, this article puts forward human motion recognition based on extreme learning machine (ELM). ELM uses the randomly calculated implicit network layer parameters for network training, which greatly reduces the time spent on network training and reduces computational complexity. In this article, the interframe difference method is used to detect the motion region, and then, the HOG3D feature descriptor is used for feature extraction. Finally, ELM is used for classification and recognition. The results imply that the method proposed here has achieved good results in human motion recognition.

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