An inverse kinematics algorithm for upper-limb joint reconstruction during robot-aided motor therapy

Papaleo, Eugenia; Zollo, Loredana; Sterzi, Silvia; Guglielmelli, Eugenio

doi:10.1109/biorob.2012.6290861

Cited by 17 publications

(8 citation statements)

References 13 publications

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“…The kinematics of the human body is explicitly concerned with figuring and understanding for the interpretation, turn, position, and speed of each body section in movements [16]. The movement of a kinematic chain, regardless of whether it is a robot or an animated character, is modeled by the kinematic equations of the chain [17], [18]. The IK determined the common parameters that give an ideal position of the end-effector to finish an assignment (activity arranging) [19].…”

Section: Inverse Kinematics (Ik)mentioning

confidence: 99%

Fusion of Artificial Intelligence in Neuro-Rehabilitation Video Games

2019

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In this paper, an intuitive neuro-rehabilitation video game has been developed employing the fusion of artificial neural networks (ANNs), inverse kinematics (IK), and fuzzy logic (FL) algorithms. The embedded algorithms automatically adjust the game difficulty level based on the player's interaction with the game. Moreover, it is manifested as an alternative approach for possible movements to improve incorrect positioning through real-time visual feedback on the screen; 52 participants volunteered to engage in the program. Motor assessment scale (MAS) was determined to assess the participants' functional ability preand post-treatments. The system input is received via the Microsoft Kinect, a foot Pedal (Saitek), and the Thalmic Myo armband. The ANN classifier integrates the limb joints orientation, angular velocity, lower arms' muscle activity, hand gestures, feet sole (plantar) pressure parameters, and the MAS scores to learn from data and predict the improvement following the intervention. The fuzzy input generates a crisp output and provides a personalized rehabilitation program with the potential to be integrated into clinical protocols. Experiments to obtain the input signals and desired outputs were conducted for the learning and validation of the network. The networks pattern recognition, self-organizing map, and non-linear auto-regression analysis performed using feed-forward and Levenberg-Marquardt backpropagation (LMBP) procedure. The results showed the effectiveness of the non-linear auto-regression using the optimized LMBP algorithm to classify and visualize the target categories. Furthermore, the state of the network demonstrates the prediction accuracy exceeding 94%. Clustering algorithm grouped the data based on the similarity. Self-organizing map trained the network to learn the topology of samples with high correlation, presented outputs with high achievement.

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Section: Inverse Kinematics (Ik)mentioning

confidence: 99%

Fusion of Artificial Intelligence in Neuro-Rehabilitation Video Games

2019

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“…By assigning upper-limb frames, according to D-H convention, a homogeneous transformation matrix, ( ), T q 7 0 r was obtained as a product of the matrices ( , , ), T q q q 5 6 7 related to the shoulder, elbow, and wrist, respectively [12]. The kinematic model was validated by means of an optoelectronic motion analysis system, as explained in [13].…”

Section: Upper-limb Kinematic Reconstructionmentioning

confidence: 99%

“…J q a v One recent work by some of the authors [13] presents the validation of the joint reconstruction method through a comparative analysis with the joint angles measured by an optoelectronic motion capturing system. The mean error committed in joint reconstruction is 8.3*e-3 rad (with a standard deviation of 1.8*e-3 rad).…”

Section: Upper-limb Kinematic Reconstructionmentioning

confidence: 99%

Patient-Tailored Assistance: A New Concept of Assistive Robotic Device That Adapts to Individual Users

Morales

Badesa

García-Aracil

et al. 2014

IEEE Robot. Automat. Mag.

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“…The kinematics of the human body is specifically concerned with formulating and solving for the translation, rotation, position, and velocity of each body segment in real-world motions. The movement of a kinematic chain, whether it is a robot or an animated character, is modelled by the kinematic equations of the chain [37] , [38] , [39] . The IK determines the typical parameters that provide a desired position of the end-effector to achieve the task and is known as action planning [39] .…”

Section: Modelmentioning

confidence: 99%

ReHabgame: A non-immersive virtual reality rehabilitation system with applications in neuroscience

Esfahlani

Thompson

Parsa

et al. 2018

Heliyon

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This paper proposes the use of a non-immersive virtual reality rehabilitation system “ReHabgame” developed using Microsoft Kinect™ and the Thalmic™ Labs Myo gesture control armband. The ReHabgame was developed based on two third-person video games that provide a feasible possibility of assessing postural control and functional reach tests. It accurately quantifies specific postural control mechanisms including timed standing balance, functional reach tests using real-time anatomical landmark orientation, joint velocity, and acceleration while end trajectories were calculated using an inverse kinematics algorithm. The game was designed to help patients with neurological impairment to be subjected to physiotherapy activity and practice postures of daily activities. The subjective experience of the ReHabgame was studied through the development of an Engagement Questionnaire (EQ) for qualitative, quantitative and Rasch model.The Monte-Carlo Tree Search (MCTS) and Random object (ROG) generator algorithms were used to adapt the physical and gameplay intensity in the ReHabgame based on the Motor Assessment Scale (MAS) and Hierarchical Scoring System (HSS). Rasch analysis was conducted to assess the psychometric characteristics of the ReHabgame and to identify if these are any misfitting items in the game.Rasch rating scale model (RSM) was used to assess the engagement of players in the ReHabgame and evaluate the effectiveness and attractiveness of the game. The results showed that the scales assessing the rehabilitation process met Rasch expectations of reliability, and unidimensionality. Infit and outfit mean squares values are in the range of (0.68–1.52) for all considered 16 items. The Root Mean Square Residual (RMSR) and the person separation reliability were acceptable. The item/person map showed that the persons and items were clustered symmetrically.

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An inverse kinematics algorithm for upper-limb joint reconstruction during robot-aided motor therapy

Cited by 17 publications

References 13 publications

Fusion of Artificial Intelligence in Neuro-Rehabilitation Video Games

Fusion of Artificial Intelligence in Neuro-Rehabilitation Video Games

Patient-Tailored Assistance: A New Concept of Assistive Robotic Device That Adapts to Individual Users

ReHabgame: A non-immersive virtual reality rehabilitation system with applications in neuroscience

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