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Open AccessArticle

Machine Learning Methodology in a System Applying the Adaptive Strategy for Teaching Human Motions

1
Production Engineering Institute, Cracow University of Technology, Al. Jana Pawla II 37, 31-864 Cracow, Poland
2
Institute of Computer Science, Pedagogical University of Cracow, ul. Podchorazych 2, 30-084 Cracow, Poland
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(1), 314; https://doi.org/10.3390/s20010314
Received: 13 November 2019 / Revised: 30 December 2019 / Accepted: 4 January 2020 / Published: 6 January 2020
(This article belongs to the Special Issue Intelligent Sensor Signal in Machine Learning)
The teaching of motion activities in rehabilitation, sports, and professional work has great social significance. However, the automatic teaching of these activities, particularly those involving fast motions, requires the use of an adaptive system that can adequately react to the changing stages and conditions of the teaching process. This paper describes a prototype of an automatic system that utilizes the online classification of motion signals to select the proper teaching algorithm. The knowledge necessary to perform the classification process is acquired from experts by the use of the machine learning methodology. The system utilizes multidimensional motion signals that are captured using MEMS (Micro-Electro-Mechanical Systems) sensors. Moreover, an array of vibrotactile actuators is used to provide feedback to the learner. The main goal of the presented article is to prove that the effectiveness of the described teaching system is higher than the system that controls the learning process without the use of signal classification. Statistical tests carried out by the use of a prototype system confirmed that thesis. This is the main outcome of the presented study. An important contribution is also a proposal to standardize the system structure. The standardization facilitates the system configuration and implementation of individual, specialized teaching algorithms. View Full-Text
Keywords: pattern recognition; human–machine interface; machine learning; MEMS sensors; haptic feedback; motor learning pattern recognition; human–machine interface; machine learning; MEMS sensors; haptic feedback; motor learning
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Wójcik, K.; Piekarczyk, M. Machine Learning Methodology in a System Applying the Adaptive Strategy for Teaching Human Motions. Sensors 2020, 20, 314.

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