Christina Tischler, Klaus Pendl, Erwin Schimbäck, Veronika Putz, Christian Kastl, T. Schlechter, Frederik Runte,
"Lower Limbs Gesture Recognition Approach to Control a Medical Treatment Bed"
, in Moreno-Díaz, Robertoand Pichler, Franzand Quesada-Arencibia, Alexis: Computer Aided Systems Theory -- EUROCAST 2022, Springer Nature Switzerland, Cham, Seite(n) 318--326, 2-2023, ISBN: 978-3-031-25312-6
Original Titel:
Lower Limbs Gesture Recognition Approach to Control a Medical Treatment Bed
Sprache des Titels:
Englisch
Original Buchtitel:
Computer Aided Systems Theory -- EUROCAST 2022
Original Kurzfassung:
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.
Forschungs-