Monitoring Wrist and Fingers Range of Motion using Leap Motion Camera for Physical Rehabilitation

Kavian, Mohammad; Nadian-Ghomsheh, Ali

doi:10.1109/mvip49855.2020.9116876

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…LMC uses a proprietary algorithm to extract hand data from raw sensor data. These data consist of infrared brightness values and calibration data to remove distortions [25].…”

Section: Leap Motion Controllermentioning

confidence: 99%

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Finger Joint Stiffness Estimation with Joint Modular Soft Actuators for Hand Telerehabilitation

Matsunaga,

Kokubu,

Tortos Vinocour

et al. 2023

Robotics

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In a telerehabilitation environment, it is difficult for a therapist to understand the condition of a patient’s finger joints because of the lack of direct assessment. In particular, not enabling the provision of spasticity evaluation significantly reduces the optimal performance of telerehabilitation. In a previous study, it has been proposed that finger stiffness could be estimated using an analytical model of a whole-finger soft actuator. However, because the whole-finger soft actuators require high air pressure for high bending performance and are costly to customize for each patient, using joint modular soft actuators for telerehabilitation turns to be a necessity, though stiffness estimation with joint modular soft actuators has not been studied yet. Another problem is caused by using a marker-based joint angle measurement, which requires the markers to be attached to the exact positions, and limits its application in telerehabilitation. In this study, we proposed a procedure of finger joint stiffness estimation that combines information acquired from a joint modular soft actuator and a marker-less hand joint position acquisition device. Correction parameters were added to the previous analytical model for -the bending analysis of a joint assisted using a joint modular soft actuator. Moreover, a multi-variate regression model was implemented for correcting joint angles obtained from the hand joint position acquisition device. As a result, a reasonable accuracy of stiffness estimation was achieved for rehabilitation with the joint modular soft actuators, which suggests the possibility of using the proposed method to evaluate the finger spasticity in a telerehabilitation environment. This is a big step forward towards optimal hand telerehabilitation.

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“…LMC uses a proprietary algorithm to extract hand data from raw sensor data. These data consist of infrared brightness values and calibration data to remove distortions [25].…”

Section: Leap Motion Controllermentioning

confidence: 99%

“…By designing a system that can measure joint angles without having the soft actuators attached, it is also possible to measure joint range of motion, increasing the system's functionality. In addition, having a high-cost system can limit how many devices can be provided for telerehabilitation [25].…”

Section: Introductionmentioning

confidence: 99%

Finger Joint Stiffness Estimation with Joint Modular Soft Actuators for Hand Telerehabilitation

Matsunaga,

Kokubu,

Tortos Vinocour

et al. 2023

Robotics

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show abstract

“…Currently, gestures are frequently used either to detect an action, a state or a word or to control user interfaces in domains as engineering, IT, recovery healthcare [1], music [2] and education. They support the users' in controlling various hardware or software interfaces without the need for classic input devices such as the mouse and the keyboard.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Data Classification Results for Leap Motion Recovery Gestures

Nicola

Chirila

Stoicu-Tivadar

2022

Studies in Health Technology and Informatics

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Static and dynamic gestures are frequently used in activities supporting learning, recovery healthcare, engineering, and 3D games to increase the interactivity between man and machine. The gestures are detected via hardware devices and data is processed using different software methods. This paper presents the manner of detection and interpretation of two gestures, a hand rotation gesture and a palm closing and opening gesture, using the Leap Motion device. These two dynamic gestures are very often used in hand recovery exercises. For comparing the two gestures we use data classification methods, Support Vector Machine (SVM) and Multilayer Perceptron (MLP). The data for the gesture classification were 80% training data and 20% testing data. The metrics for comparison are precision, recall, F1-score, and the total number of testing cases (support). The SVM classifier gives an accuracy of 99.4% and the MLP classifier a 96.2%. We built two confusion matrices for better visualizing the results.

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Virtual Reality and Augmented Reality Technologies for Smart Physical Rehabilitation

Postolache

Monge

Alexandre

et al. 2021

Smart Sensors, Measurement and Instrumentation

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Monitoring Wrist and Fingers Range of Motion using Leap Motion Camera for Physical Rehabilitation

Cited by 7 publications

References 11 publications

Finger Joint Stiffness Estimation with Joint Modular Soft Actuators for Hand Telerehabilitation

Finger Joint Stiffness Estimation with Joint Modular Soft Actuators for Hand Telerehabilitation

Comparison of Data Classification Results for Leap Motion Recovery Gestures

Virtual Reality and Augmented Reality Technologies for Smart Physical Rehabilitation

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