A 3-DoF Robotic Platform for the Rehabilitation and Assessment of Reaction Time and Balance Skills of MS Patients

The central nervous system (CNS) exploits anticipatory (APAs) and compensatory (CPAs) postural adjustments to maintain the balance.The postural adjustments comprising stability of the center of mass (CoM) and the pressure distribution of the body influence each other if there is a lack of performance in either of them.Any predictable or sudden perturbation may pave the way for the divergence of CoM from equilibrium and in homogeneous pressure distribution of the body.Such a situation is often observed in daily livings of Multiple Sclerosis (MS) patients owing to their poor APAs and CPAs, and induces their falls.The way of minimizing risk of falls in neurological patients is utilizing perturbation-based rehabilitation, as it is efficient in the recovery of the balance disorder.In the light of the findings, we present the design, implementation, and experimental evaluation of a novel 3 DoF parallel manipulator to treat the balance disorder of MS.The robotic platform allows angular motion of the ankle based on its anthropomorphic freedom.The end-effector endowed with upper and lower platforms is designed to evaluate both the pressure distribution of each foot and the CoM of the body, respectively.Data gathered from the platforms are utilized to both evaluate performance of the patients and used in high-level control of the robotic platform to regulate the difficulty level of tasks.In this study, kinematic and dynamic analyses of the robot are derived and validated in the simulation environment. Low-level control of the prototype is also successfully implemented through PID controller.The capacity of each platform is evaluated with a set of experiments considering assessment of pressure distribution and CoM of the foot-like-objects on the end-effector. Experimental results indicate that such a system well-address the need for balance skill training and assessment through the APAs and CPAs.Comment: 12 figures, 29 pages, PLOS ON

Data acquisition and feature extraction for classification of prehensile semg signals for control of a multifunctional prosthetic hand

This study focuses on the SEMG (surface electromyography) signals that carry the valuable information of the neuromuscular activity of a muscle and are utilized in the man-machine interfaces such as multi-functional prostheses. The SEMG signals measured from four different muscle groups of the forearm are weak, sophisticated and very sensitive to ambient noise. The first stage of this study is hardware design and implementation for the SEMG measurement. The fundamentals of the design are mainly based on the specifications of the SEMG signal and the factors that affect the signal quality. The second purpose of the thesis is applying various methodologies to the recorded SEMG signal to give meaning to its nature to be used in the further processes. The raw EMG signals have nonlinear characteristics and present useful information if they are quantified. For this purpose, various signal processing methods are applied to the SEMG signal to acquire useful information, features. Features of the signal are extracted to be used for classification of prehensile motions of multi-functional prosthetics. In this part, many algorithms that have been employe as feature extraction methods are compared with respect to their classification performance

Detection of intention level in response to task difficulty from EEG signals

We present an approach that enables detecting intention levels of subjects in response to task difficulty utilizing an electroencephalogram (EEG) based brain-computer interface (BCI). In particular, we use linear discriminant analysis (LDA) to classify event-related synchronization (ERS) and desynchronization (ERD) patterns associated with right elbow flexion and extension movements, while lifting different weights. We observe that it is possible to classify tasks of varying difficulty based on EEG signals. Additionally, we also present a correlation analysis between intention levels detected from EEG and surface electromyogram (sEMG) signals. Our experimental results suggest that it is possible to extract the intention level information from EEG signals in response to task difficulty and indicate some level of correlation between EEG and EMG. With a view towards detecting patients' intention levels during rehabilitation therapies, the proposed approach has the potential to ensure active involvement of patients throughout exercise routines and increase the efficacy of robot assisted therapies

I-BaR: Integrated Balance Rehabilitation Framework

Neurological diseases are observed in approximately one billion people worldwide. A further increase is foreseen at the global level as a result of population growth and aging. Individuals with neurological disorders often experience cognitive, motor, sensory, and lower extremity dysfunctions. Thus, the possibility of falling and balance problems arise due to the postural control deficiencies that occur as a result of the deterioration in the integration of multi-sensory information. We propose a novel rehabilitation framework, Integrated Balance Rehabilitation (I-BaR), to improve the effectiveness of the rehabilitation with objective assessment, individualized therapy, convenience with different disability levels and adoption of an assist-as-needed paradigm and, with an integrated rehabilitation process as a whole, i.e., ankle-foot preparation, balance, and stepping phases, respectively. Integrated Balance Rehabilitation allows patients to improve their balance ability by providing multi-modal feedback: visual via utilization of Virtual Reality; vestibular via anteroposterior and mediolateral perturbations with the robotic platform; proprioceptive via haptic feedback.Comment: 37 pages, 2 figures, journal pape

Sex Estimation From Sternal Measurements Using Multidetector Computed Tomography

We aimed to show the utility and reliability of sternal morphometric analysis for sex estimation. Sex estimation is a very important step in forensic identification. Skeletal surveys are main methods for sex estimation studies. Morphometric analysis of sternum may provide high accuracy rated data in sex discrimination. In this study, morphometric analysis of sternum was evaluated in 1mm chest computed tomography scans for sex estimation. Four hundred forty 3 subjects (202 female, 241 male, mean age: 44 +/- 8.1 [ distribution: 30-60 year old]) were included the study. Manubrium length (ML), mesosternum length (2L), Sternebra 1 (S1W), and Sternebra 3 (S3W) width were measured and also sternal index (SI) was calculated. Differences between genders were evaluated by student t-test. Predictive factors of sex were determined by discrimination analysis and receiver operating characteristic (ROC) analysis. Male sternalmeasurement values are significantly higher than females (P< 0.001) while SI is significantly low in males (P< 0.001). In discrimination analysis, MSL has high accuracy rate with 80.2% in females and 80.9% in males. MSL also has the best sensitivity (75.9%) and specificity (87.6%) values. Accuracy rates were above 80% in 3 stepwise discrimination analysis for both sexes. Stepwise 1(ML, MSL, S1W, S3W) has the highest accuracy rate in stepwise discrimination analysis with 86.1% in females and 83.8% in males. Our study showed that morphometric computed tomography analysis of sternum might provide important information for sex estimation

Virtual morphometric method using seven cervical vertebrae for sex estimation on the Turkish population

Sex estimation from skeletal remains is crucial for the estimation of the biological profile of an individual. Although the most commonly used bones for means of sex estimation are the pelvis and the skull, research has shown that acceptable accuracy rates might be achieved by using other skeletal elements such as vertebrae. This study aims to contribute to the development of sex estimation standards from a Turkish population through the examination of CT scans from the seven cervical vertebrae. A total of 294 individuals were included in this study. The CT scans were obtained from patients attending the Bakirkoy Training and Research Hospital (Turkey) and the data was collected retrospectively by virtually taking measurements from each cervical vertebrae. The full database was divided into a training set (N = 210) and a validation set (N = 84) to test the fit of the models. Observer error was assessed through technical error of measurement and sex differences were explored using parametric and non-parametric approaches. Logistic regression was applied in order to explore different combinations of vertebral parameters. The results showed low intra- and inter-observer errors. All parameters presented statistically significant differences between the sexes and a total of 15 univariate and multivariate models were generated producing accuracies ranging from a minimum of 83.30% to a maximum of 91.40% for a model including three parameters collected from four vertebrae. This study presents a virtual method using cervical vertebrae for sex estimation on the Turkish population providing error rates comparable to other metric studies conducted on the postcranial skeleton. The presented results contribute not only to the development of population-specific standards but also to the generation of virtual methods that can be tested, validated, and further examined in future forensic cases

Forensic age diagnostics by magnetic resonance imaging of the proximal humeral epiphysis

The most commonly used radiological method for age estimation of living individuals is X-ray. Computed tomography is not commonly used due to high radiation exposure, which raises ethical concerns. This problem can be solved with the use of magnetic resonance imaging (MRI), which avoids the use of ionizing radiation. The purpose of the present study was to evaluate the utility of MRI analysis of the proximal humeral epiphyses for forensic age estimations of living individuals. In this study, 395 left proximal humeral epiphyses (patient age 12-30years) were evaluated with fast-spin-echo proton density-weighted image (FSE PD) sequences in a coronal oblique orientation on shoulder MRI images. A five-stage scoring system was used following the method of Dedouit et al. The intra- and interobserver reliabilities assessed using Cohen's kappa statistic were =0.818 and =0.798, respectively. According to this study, stage five first appeared at 20 and 21years of age in males and females, respectively. These results are not directly comparable to any other published study due to the lack of MRI data on proximal humeral head development. These findings may provide valuable information for legally important age thresholds using shoulder MRI. The current study demonstrates that MRI of the proximal humerus can support forensic age estimation. Further research is needed to establish a standardized protocol that can be applied worldwide