fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees.

Prosthetic arms are designed to assist amputated individuals in the performance of the activities of daily life. Brain machine interfaces are currently employed to enhance the accuracy as well as number of control commands for upper limb prostheses. However, the motion prediction for prosthetic arms and the rehabilitation of amputees suffering from transhumeral amputations is limited. In this paper, functional near-infrared spectroscopy (fNIRS)-based approach for the recognition of human intention for six upper limb motions is proposed. The data were extracted from the study of fifteen healthy subjects and three transhumeral amputees for elbow extension, elbow flexion, wrist pronation, wrist supination, hand open, and hand close. The fNIRS signals were acquired from the motor cortex region of the brain by the commercial NIRSport device. The acquired data samples were filtered using finite impulse response (FIR) filter. Furthermore, signal mean, signal peak and minimum values were computed as feature set. An artificial neural network (ANN) was applied to these data samples. The results show the likelihood of classifying the six arm actions with an accuracy of 78%. The attained results have not yet been reported in any identical study. These achieved fNIRS results for intention detection are promising and suggest that they can be applied for the real-time control of the transhumeral prosthesis.

Static force and tipover stability analysis of an assistive device for paraplegics.

Stability plays a vital role in any robotic system. Its significance increases in systems related to health and medicine. For rehabilitation devices meant for Spinal Cord Injury (SCI) patients, stability is crucial and key element in ensuring patient safety and the usefulness of the devices. In this study, kinematics, force analysis, and the static tip-over stability of a device for rehabilitation of paraplegic patients is discussed. Kinematics modeling and static force analysis provide critical information about position and loading at different points on the device. Force-Angle Stability Criterion is used to find the static tip-over stability of the device while the patient is on board the device. The Criterion relies on the support boundary, tip-over mode axes, and the Center of Mass (COM) of the complete system. The Criterion is sensitive to the COM position and therefore is more suitable for the application. The linear actuator mounted on the device causes the end effector of the device to move. The patient, strapped with the end effector, in turn moves from sitting position to standing position. The study focuses on the analysis of stability based on changing COM during this motion. The results verify that although the system is well within the stability bounds, it is more stable as it moves from sitting position to standing position.