Modeling the Knowledge of Users in an Augmented Reality-Based Learning Environment Using Fuzzy Logic

Papakostas, Christos; Troussas, Christos; Krouska, Akrivi; Sgouropoulou, Cleo

doi:10.1007/978-3-031-17601-2_12

Cited by 17 publications

(1 citation statement)

References 21 publications

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“…One of the main goals of the robotic hand is to make the device as close as possible to the member, so sensor and signal processing requires the capability of measuring and extracting information from the surrounding environment. For this, processing information with machine learning (ML) algorithms has played an important role in applications such as gesture recognition through sEMG signals, slip detection, and object recognition [ 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Soft-Sensor System for Grasp Type Recognition in Underactuated Hand Prostheses

Arco

Pontes

Segatto

et al. 2023

Sensors

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This paper presents the development of an intelligent soft-sensor system to add haptic perception to the underactuated hand prosthesis PrHand. Two sensors based on optical fiber were constructed, one for finger joint angles and the other for fingertips’ contact force. Three sensor fabrications were tested for the angle sensor by axially rotating the sensors in four positions. The configuration with the most similar response in the four rotations was chosen. The chosen sensors presented a polynomial response with R2 higher than 92%. The tactile force sensors tracked the force made over the objects. Almost all sensors presented a polynomial response with R2 higher than 94%. The system monitored the prosthesis activity by recognizing grasp types. Six machine learning algorithms were tested: linear regression, k-nearest neighbor, support vector machine, decision tree, k-means clustering, and hierarchical clustering. To validate the algorithms, a k-fold test was used with a k = 10, and the accuracy result for k-nearest neighbor was 98.5%, while that for decision tree was 93.3%, enabling the classification of the eight grip types.

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Section: Introductionmentioning

confidence: 99%