Pattern recognition methods as a tool to build an automatic system for learning coordinated human motions
Authors:
- Krzysztof Wójcik
Abstract
There is great social and economic significance in the teaching and learning of motion activities. This is notably true for teaching the activities involved in rehabilitation, sports, and professional work. The possibility of engaging an automatic teaching system is highly significant. Nevertheless, building an effective system is an ongoing challenge. This article describes a general outline of the teaching system, which includes MEMS (micro-electro-mechanical systems) sensors, haptic actuators, and algorithms for signal classification applied to the online selection of an appropriate teaching method. The main goal of this paper was to prove that the system is able to teach fast and synchronized movements effectively. To this end, system performance was presented and discussed. The statistical tests revealed an efficiency of the proposed approach, especially for tasks of teaching fast and periodic movements. This result was the primary outcome of the presented paper. The described scheme can be utilized for building two types of motor learning systems. The first relates to the “personal” learning systems for rehabilitation and sports. The second type can perform the classification of complex movements of human body parts and may be used in teaching the remote control of machines and vehicles (excavators, cranes, search and rescue drones, etc.).
- Record ID
- CUTe5cc751b82d34e548721fc5c7f1c4a44
- Publication categories
- ;
- Author
- Journal series
- IEEE Access, ISSN 2169-3536
- Issue year
- 2020
- Vol
- 8
- Pages
- 141407-141421
- Other elements of collation
- il. (w tym kolor.); Bibliografia (na s.) - 141421; Bibliografia (liczba pozycji) - 38; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 8
- Keywords in English
- haptic feedback, machine learning, MEMS sensors, motor learning, pattern recognition
- DOI
- DOI:10.1109/ACCESS.2020.3013283 Opening in a new tab
- URL
- https://ieeexplore.ieee.org/document/9153767 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 100
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUTe5cc751b82d34e548721fc5c7f1c4a44/
- URN
urn:pkr-prod:CUTe5cc751b82d34e548721fc5c7f1c4a44
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.