Abstract
As materials are getting softer, we need to interact with the object directly to find out what it is and how we should handle it. Thus, movement becomes increasingly important. Human movements are divided into two. One is Motion observed from outside and the other is Motor inside of us. In the case of Motion, our trajectories vary widely at the early stage but when we get close to the object, muscles harden and move together with skeleton. So, we can apply mathematical approaches and control the trajectory. Why trajectories vary widely at the early stage is because we need to mobilize many body parts to balance our body to adapt to the environment and situation. So, coordination is important, but the way to secure coordination is not established yet. Further, muscles are analog, so their degrees of freedom are tremendously large. Therefore, we cannot solve the problem mathematically. We need another way of computing to find out how we should handle the object. Thus, strategic decision making is called for. So Pragmatic computing, which repeats the Plan \(\to\) Do \(\to\) Study \(\to\) Act cycle is needed. This paper proposed quantitative performance evaluation tool using Mahalanobis Distance and pattern. The uniqueness and usefulness of this approach is it allows us focus our attention on any location where we wish to, and it helps us understand how we can improve our movements. This is made possible by making the most of instinct and proprioception, which have been ignored in regular computing.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bernstein, N.A.: The Co-ordination and Regulation of Movements. Pergamon Press, Oxford (1967)
Mahalanobis, P.C.: On the generalized distance in statistics. Proc. Nat. Institute Sci. India 2(1), 49–55 (1936)
Taguchi, G., Chowdhury, S., Wu, Y.: The mahalanobis-taguchi system. McGraw-Hill Professional, New York (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Fukuda, S. (2022). PDSA Computing. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-030-80119-9_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-80119-9_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80118-2
Online ISBN: 978-3-030-80119-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)