TY - GEN
T1 - Lower Limbs Gesture Recognition Approach to Control a Medical Treatment Bed
AU - Tischler, Christina
AU - Pendl, Klaus
AU - Schimbäck, Erwin
AU - Putz, Veronika
AU - Kastl, Christian
AU - Schlechter, Thomas
AU - Runte, Frederick
N1 - Funding Information:
Acknowledgements. This work has been supported by the COMET-K2 Center of the Linz Center of Mechatronics (LCM) funded by the Austrian federal government and the federal state of Upper Austria.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023/3/3
Y1 - 2023/3/3
N2 - Human machine interaction is showing increasing importance in various areas. In this context a gesture control using machine learning algorithms for a contactless control of a therapy table has been identified as interesting application. Predefined lower limb gestures are performed by an operator, classified by a pocket worn tag, and the results are transferred wirelessly to a remote controller. Two algorithms were compared using a k-nearest neighbor (KNN) and a convolutional neural network (CNN), which are responsible for the classification of the gestures. By using the KNN an accuracy in the range of 75%–82% was achieved. Compared to KNN, CNN achieves 89.1% by applying the categorical classifier and 93.7% by applying the binary classifier. Simplification of work and convenience in using the therapy table can be achieved by high accuracy and fast response of the control system.
AB - Human machine interaction is showing increasing importance in various areas. In this context a gesture control using machine learning algorithms for a contactless control of a therapy table has been identified as interesting application. Predefined lower limb gestures are performed by an operator, classified by a pocket worn tag, and the results are transferred wirelessly to a remote controller. Two algorithms were compared using a k-nearest neighbor (KNN) and a convolutional neural network (CNN), which are responsible for the classification of the gestures. By using the KNN an accuracy in the range of 75%–82% was achieved. Compared to KNN, CNN achieves 89.1% by applying the categorical classifier and 93.7% by applying the binary classifier. Simplification of work and convenience in using the therapy table can be achieved by high accuracy and fast response of the control system.
KW - Gesture recognition
KW - Machine learning
KW - Neural network
UR - http://www.scopus.com/inward/record.url?scp=85151130870&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-25312-6_37
DO - 10.1007/978-3-031-25312-6_37
M3 - Conference contribution
AN - SCOPUS:85151130870
SN - 9783031253119
VL - 13789 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 318
EP - 326
BT - Computer Aided Systems Theory – EUROCAST 2022 - 18th International Conference, Revised Selected Papers
A2 - Moreno-Díaz, Roberto
A2 - Pichler, Franz
A2 - Quesada-Arencibia, Alexis
PB - Springer
T2 - 18th International Conference on Computer Aided Systems Theory, EUROCAST 2022
Y2 - 20 February 2022 through 25 February 2022
ER -