Rehabilitation at home is a growing need worldwide. Previous studies have suggested different devices in terms of motion and force transmission. In this study, we present design and development process of a novel hand exercise exoskeleton. The main advantages of our device that are portable, wearable, light weight (345 grams) and suitable for home use. The greatest feature of the device is the force transmitting mechanism. The spring mechanism manufactured by using commercial compression springs has some advantages in terms of size and weight. In design studies of the device, we have made use of the systematic approach. In this way, the best of three possible design solutions has been determined. Then the best design solution was selected. A few prototypes of the device were manufactured. The device has been tested clinically on both unimpaired individuals and hemiplegic hand patients for a short time. It was reported that the exoskeleton was suited to passive exercises. The result section gives an evaluation of the device in terms of exercises, ergonomics and the market. Additionally, a patent registration certificate was issued to our device for our country.

The categorization of hand gestures holds significant importance in controlling orthotic and prosthetic devices, enabling human-machine interaction, and facilitating telerehabilitation applications. For many years, methods of motion analysis based on image processing techniques have been employed to detect hand motions. However, recent research has focused on utilizing muscle contraction for detecting hand movements. Specifically, there has been an increase in studies that classify hand movements using surface electromyography (sEMG) data from the muscles of the hand and arm. In our study, we estimated the open (extension of the fingers) and closed (flexion of the fingers) positions of the hand by analyzing EMG data obtained from 4 volunteer participants' Extensor digitorum and Flexor carpi radialis muscles. In order to accurately discriminate EMG signals, various statistical measures such as variance, standard deviation, root mean square, average energy, minimum and maximum features were utilized. The dataset containing these additional features was then subjected to classification algorithms including Support Vector Machines (SVM), K Nearest Neighbour (KNN), Decision Tree (DT), and Gaussian Naive Bayes (GNB) for the purpose of classifying hand positions into open or closed states. Among the tested algorithms, SVM achieved the highest success rate with a maximum accuracy of 73.1%, while KNN yielded the lowest success rate at a minimum accuracy of 55.9%. To further enhance prediction accuracy in future studies, it is suggested that data from a larger set of muscles be collected.

Schizophrenia is a chronic mental illness that can negatively affect emotions, thoughts, social interaction, motor behavior, attention, and perception. Early diagnosis is still challenging and is based on the disease’s symptoms. However, electroencephalography (EEG) signals yield incredibly detailed information about the activities and functions of the brain. In this study, a hybrid algorithm approach is proposed to improve the search performance of the marine predator algorithm (MPA) based on chaotic maps. For evaluating the performance of the proposed chaotic-based marine predator algorithm (CMPA), benchmark datasets are used. The results of the suggested variation method on the benchmarks show that the Sine Chaotic-based MPA (SCMPA) significantly outperforms the other MPA variants. The algorithm was verified using a public dataset consisting of 14 subjects. Moreover, the proposed SCMPA is essential for EEG electrode selection because it minimizes model complexity and selects the best representative features for providing optimal solutions. The extracted features for each subject were used in the decision tree (DT), random forest (RF), and extra tree (ET) methods. Performance measures showed that the proposed model was successful at differentiating schizophrenia patients (SZ) from healthy controls (HC). In the end, it was demonstrated that the feature selection technique SCMPA, which is the subject of this research, performs significantly better in regard to classification using EEG signals.

Determining the joint torques of the hand is a useful design parameter for humanoid mechanism, rehabilitation robots and orthotic and prosthetic devices. In previous studies, different methods have been suggested for estimating joint torques of the hand such as mathematical models and simulation models. This paper presents a 16 DOF hand kinematic model and its simulation. Kinematic structure of an average human hand was created by SimMechanics and Simulink on MATLAB. The model was simulated by inverse dynamics method using the video record of a healthy subject (male, 29 years, 71 kg and 174 cm) and the joint torques of the index finger and wrist were calculated. The maximum torque at index finger with 0.015 Nm occurs at the MCP joint during the cylindrical grip. The maximum wrist torque was calculated as 0.225 Nm during movement of flexion/extension. Flexion/extension and abduction/adduction movements of the fingers and the wrist can be analyzed with this model changing joint rotation axis easily. This MATLAB model can be used in order to calculate the joint torques at different movements of the hand without complex equations and external devices.