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Backgrounds and Objective: Facial palsy is a complex pathophysiological condition affecting the personal and professional lives of the involved patients. Sudden muscle weakness or paralysis needs to be rehabilitated to recover a symmetric and expressive face. Computer-aided decision support systems for facial rehabilitation have been developed. However, there is a lack of facial muscle baseline data to evaluate the patient states and guide as well as optimize the rehabilitation strategy. In this present study, we aimed to develop a novel baseline facial muscle database (static and dynamic behaviors) using the coupling between statistical shape modeling and in-silico trial approaches. Methods: 10,000 virtual subjects (5000 males and 5000 females) were generated from a statistical shape modeling (SSM) head model. Skull and muscle networks were defined so that they statistically fit with the head shapes. Two standard mimics: smiling and kissing were generated. The muscle strains of the lengths in neutral and mimic positions were computed and recorded thanks to the muscle insertion and attachment points on the animated head and skull meshes. For validation, five head and skull meshes were reconstructed from the five computed tomography (CT) image sets. Skull and muscle networks were then predicted from the reconstructed head meshes. The predicted skull meshes were compared with the reconstructed skull meshes based on the mesh-to-mesh distance metrics. The predicted muscle lengths were also compared with those manually defined on the reconstructed head and skull meshes. Moreover, the computed muscle lengths and strains were compared with those in our previous studies and the literature. Results: The skull prediction’s median deviations from the CT-based models were 2.2236 mm, 2.1371 mm, and 2.1277 mm for the skull shape, skull mesh, and muscle attachment point regions, respectively. The median deviation of the muscle lengths was 4.8940 mm. The computed muscle strains were compatible with the reported values in our previous Kinect-based method and the literature. Conclusions: The development of our novel facial muscle database opens new avenues to accurately evaluate the facial muscle states of facial palsy patients. Based on the evaluated results, specific types of facial mimic rehabilitation exercises can also be selected optimally to train the target muscles. In perspective, the database of the computed muscle lengths and strains will be integrated into our available clinical decision support system for automatically detecting malfunctioning muscles and proposing patient-specific rehabilitation serious games.

Facial mimics are important to human life, so facial palsy negatively affects the involved patients. Recently, the comparison of muscle action lengths between the left and right-hand sides has been used to evaluate facial palsy quantitatively. However, even in the healthy subjects, their left and right muscle action lengths could not be perfectly symmetrical. These action lengths were formed by insertion points on the head and attachment points on the skull. Consequently, the geometrical dissymmetry between left and right human skulls needs to be analyzed and reported quantitatively. So far, no studies have reported this quantity. In this paper, in the second part of our study, we analyzed symmetrical levels between the left and right sides of human skulls. In particular, 329 skull models reconstructed from computed tomography (CT) images of healthy subjects in neutral mimics were used for calculating. The left and right skull regions were mirrored through a center plane of the skull. Hausdorff distance and volumetric differences between the left skulls (skull convex hulls) and mirrored right skulls (skull convex hulls) were computed as the distance and volumetric symmetries, respectively. As a result, the distance dissymmetrical values (Mean ± SD) are 1.2680 ± 0.3538 mm, and ones of volumetric dissymmetry (Mean ± SD) are 32.1790 ± 23.2725 cm3. In perspective, we will analyze the skull symmetry in more detail with different local shape topologies. Moreover, the global and local shape symmetries will be implemented in our clinical decision support system for facial mimic rehabilitation.

Quadcopter is a kind of robot which is popularly used in both academic and industrial environment. In this paper, we present and implement a method to stabilize a quadcopter prototype’s position using feature extraction and tracking from camera footage. The quadcopter's position and linear velocity are determined from images which are captured by a downward-facing camera - Logitech C270. First, Shi-Tomasi technique is used to detect corners in the images and from this method, displacement of the quadcopter is yielded. Linear velocity is then calculated by using the quadcopter’s displacement. Once the linear velocity of the quadcopter has been estimated, the cascade PID controller is proposed to stabilize the hovering quadcopter’s position. Simulation results prove the ability of controlller on Matlab/Simulink. Then, a real quadcopter prototype is built to evaluate the proposed method and the experimental results recording in approximately 70 seconds show that the quadcopter remained its position with minimal error.

Facial paralysis grading methods are mostly based on geometrical dissymmetry between left and right heads. Dissymmetry also appears even on healthy subjects. Consequently, this dissymmetry should be computed to distinguish with one on facial palsy patients. However, no studies have been quantitatively reported this characteristic. In the first part of our study, we tried to calculate this quantity by computing distance and volumetric differences between left and right heads. In particular, 329 head models were reconstructed from medical images of subjects with normal head geometries in neutral mimics. These heads were then automatically cut into left and right regions. Then, Hausdorff distances between left heads and mirrored right heads were computed. Moreover, volumetric differences between left head convex hulls and mirrored right head convex hulls were also calculated. As a result, the dissymmetry values (Mean ± SD) in mean Hausdorff distances are 1.8243 ± 0.7029 mm, and ones in volumetric are 78.1254 ± 65.7040 cm3. In perspective, in the second part of our study, we will try to analyze geometrical symmetry on human skulls. Moreover, shape symmetry will be analyzed in more detail with different local shape topologies in different facial mimics. These analyses will finally be implemented on our clinical decision-support system for facial mimic rehabilitation.

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Skull prediction from the head is a challenging issue toward a cost-effective therapeutic solution for facial disorders. This issue was initially studied in our previous work using full head-to-skull relationship learning. However, the head-skull thickness topology is locally shaped, especially in the face region. Thus, the objective of the present study was to enhance our head-to-skull prediction problem by using local topological features for training and predicting. Head and skull feature points were sampled on 329 head and skull models from computed tomography (CT) images. These feature points were classified into the back and facial topologies. Head-to-skull relations were trained using the partial least square regression (PLSR) models separately in the two topologies. A hyperparameter tuning process was also conducted for selecting optimal parameters for each training model. Thus, a new skull could be generated so that its shape was statistically fitted with the target head. Mean errors of the predicted skulls using the topology-based learning method were better than those using the non-topology-based learning method. After tenfold cross-validation, the mean error was enhanced 36.96% for the skull shapes and 14.17% for the skull models. Mean error in the facial skull region was especially improved with 4.98%. The mean errors were also improved 11.71% and 25.74% in the muscle attachment regions and the back skull regions respectively. Moreover, using the enhanced learning strategy, the errors (mean ± SD) for the best and worst prediction cases are from 1.1994 ± 1.1225 mm (median: 0.9036, coefficient of multiple determination (R

A new approach based on linear matrix inequality (LMI) technique for stabilizing the inverted pendulum is developed in this article. The unknown states are estimated as well as the system is stabilized simultaneously by employing the observer-based controller. In addition, the impacts of the uncertainties are taken into consideration in this paper. Unlike the previous studies, the uncertainties in this study are unnecessary to satisfy the bounded constraints. These uncertainties will be converted into the unknown input disturbances, and then a disturbance observer-based controller will be synthesized to estimate the information of the unknown states, eliminate completely the effects of the uncertainties, and stabilize inverted pendulum system. With the support of lyapunov methodology, the conditions for constructing the observer and controller under the framework of linear matrix inequalities (LMIs) are derived in main theorems. Finally, the simulations for system with and without uncertainties are exhibited to show the merit and effectiveness of the proposed methods.

Both transcranial direct current stimulation (tDCS) and wrist robot-assisted training have demonstrated to be promising approaches for stroke rehabilitation. However, the effects of the combination of the two treatments in subacute stroke patients are not clear yet. To investigate the effectiveness of combining tDCS and wrist robot-assisted rehabilitation in subacute stroke patients in comparison with the wrist robotic training only, a single-blind, randomized, sham-controlled trial was performed with 40 subacute stroke patients (25 ± 7 days from stroke onset time). Patients were randomly assigned to experimental group (EG, ${n} =20$ ) where patients receive real tDCS [2 mA, 20 min, and the anodal electrode on the primary motor cortex—M1—area of the affected hemisphere (C3/C4 in the 10–20 EEG system and the cathodal electrode on the contralateral orbit bone)] or control group (CG, ${n} =20$ ) where patients receive sham tDCS (5 s) during wrist robotic rehabilitation training. The effects of the treatment were evaluated by means of the upper extremity, shoulder-elbow, and wrist subsections of the Fugl–Meyer assessment scale, Modified Ashworth Scale, Motricity Index and Box and Block Test together with kinematic parameters. One out of 20 patients in the CG did not complete the treatment. All the clinical outcome measures except the Modified Ashworth Scale showed a significant increase after the treatment in both groups. However, no significant difference in the average changes after treatment between groups was observed. The movement velocity and smoothness showed significant increases after the training, even though no significant difference between groups was observed. The combination of wrist robot-assisted training and tDCS did not show additional effects in comparison with wrist robot-assisted training only in subacute stroke patients. The negative results found in this paper are specific for the specific intervention. The timing of delivering the tDCS and the robot-assisted therapy has to be deeply investigated to enhance the effectiveness of the training.

The goal of this study is to analyse the effects of combined transcranial direct current stimulation (tDCS) and wrist robot-assisted therapy in subacute stroke patients.

In this study an upper limb biomechanical model which includes strain-adjustable ligaments of the acromioclavicular joint was developed. The model including 7° of freedom is able to evaluate the movements of the shoulder, elbow and wrist. The ligaments strain can be adjusted in order to simulate different types of Rockwood acromioclavicular dislocation.

Aim of the study This review classifies the kinematic measures used to evaluate post-stroke motor impairment following upper limb robot-assisted rehabilitation and investigates their correlations with clinical outcome measures. Methods An online literature search was carried out in PubMed, MEDLINE, Scopus and IEEE-Xplore databases. Kinematic parameters mentioned in the studies included were categorized into the International Classification of Functioning, Disability and Health (ICF) domains. The correlations between these parameters and the clinical scales were summarized. Results Forty-nine kinematic parameters were identified from 67 articles involving 1750 patients. The most frequently used parameters were: movement speed, movement accuracy, peak speed, number of speed peaks, and movement distance and duration. According to the ICF domains, 44 kinematic parameters were categorized into Body Functions and Structure, 5 into Activities and no parameters were categorized into Participation and Personal and Environmental Factors. Thirteen articles investigated the correlations between kinematic parameters and clinical outcome measures. Some kinematic measures showed a significant correlation coefficient with clinical scores, but most were weak or moderate. Conclusions The proposed classification of kinematic measures into ICF domains and their correlations with clinical scales could contribute to identifying the most relevant ones for an integrated assessment of upper limb robot-assisted rehabilitation treatments following stroke. Increasing the assessment frequency by means of kinematic parameters could optimize clinical assessment procedures and enhance the effectiveness of rehabilitation treatments.

In this paper, the recovery of proximal and distal segments in stroke patients who received distal training alone was investigated. Forty (20 subacute and 20 chronic) stroke patients were recruited to perform wrist robot-assisted rehabilitation training. The upper extremity, shoulder–elbow, and wrist subsections of the Fugl-Meyer assessment scale were used to assess the motor recovery of distal and proximal segments. In addition, the modified Ashworth scale, motricity index, and box and block test were used as clinical outcome measures together with kinematic parameters to evaluate the effects of the training. Significant increases in the wrist and shoulder–elbow subsections of the Fugl-Meyer assessment scale, motricity index, and box and block test were found in both the groups. Average changes in shoulder–elbow and upper extremity subsections of the Fugl-Meyer assessment scale in the subacute group (6.10 ± 6.60 and 15.65 ± 14.04) were significantly higher ( ${p} ) than those in the chronic group (2.30 ± 2.76 and 6.60 ± 4.64). In addition, significant increases in the movement velocity, movement smoothness, and movement quality were observed in the subacute group. Our findings provide evidence that following a robot-assisted rehabilitation treatment, there is a distal-to-proximal generalization in subacute stroke patients.

The main goal of this study is to analyse the effects of combined transcranial direct current stimulation (tDCS) and wrist robot-assisted therapy in subacute stroke patients. Twenty-four patients were included in this study and randomly assigned to the experimental (EG) or control group (CG). All participants performed wrist robot-assisted training a) in conjunction with tDCS (real stimulation for patients in EG) or b) without tDCS (sham stimulation for patients in CG). Clinical scales and kinematic parameters recorded by the robot were used for the assessment. Clinical outcome measures show a significant decrease in motor impairment after the treatment in both groups. Kinematic data show several significant improvements after the integrated therapy in both groups. However, no significant differences in both clinical outcome measures and kinematic parameters was found between two groups. The potential advantages of combined tDCS and wrist robot-assisted therapy in subacute stroke patients are still unclear.