Mid-Air Gesture Based Multi-Finger Control System For Paralyzed Patients Using Leap Motion

Sneha, K.; Kayalvizhi, R.

doi:10.1109/iccci56745.2023.10128585

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2023

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“…For the classification of the gestures used in recovery, the literature indicates quite a high performance for the KNN model, with an accuracy of 97% on a data set that describes 7 recovery gestures [5]. KNN is also used in the classification of the gestures made by paralyzed people, its accuracy being of 96.13% [6]. In addition to the KNN model, the Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM) models are also used.…”

Section: Introductionmentioning

confidence: 99%

Recovery Gestures Classification Using KNN and LDA Models

Nicola,

Chirila,

Stoicu-Tivadar

2023

Telehealth Ecosystems in Practice

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Hand and joint mobility recovery involve performing a set of exercises. Gestures are often used in the hand mobility recovery process. This paper discusses the selection and the use of 2 models of neural networks for the classification of data that describe Leap Motion gestures. The gestures are: the hand opening and closing gesture and the palm rotation gesture. The purpose is the optimal selection of the neural network model to be used in the classification of the data describing the recovery gestures. The models chosen for the classification of the two gestures were: Linear Discriminant Analysis (LDA) and K-neighbors Classifier (KNN). The accuracies achieved in the classification of the gestures for each model are: 0.91 – LDA and 0.98 – KNN.

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Section: Introductionmentioning

confidence: 99%