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.

Limited non-invasive transhumeral prosthesis control exists due to the absence of signal sources on amputee residual muscles. This paper introduces a hybrid brain-machine interface (hBMI) that integrates surface electromyography (sEMG) and functional near-infrared spectroscopy (fNIRS) signals to overcome the limits of an existing myoelectric upper-limb prosthesis. This hybridization aims to improve classification accuracy (CA) to escalate arm movements' control performance for individuals who have a transhumeral amputation. To evaluate the effectiveness of this hBMI, fifteen healthy and three transhumeral amputee subjects for six arm motions were participating in the experiment. Myo armband was used to acquire sEMG signals corresponding to four arm motions: elbow extension, elbow flexion, wrist pronation, and wrist supination. Whereas, fNIRS brain imaging modality was used to monitor cortical hemodynamics response from the prefrontal cortex region for two hand motions: hand open and hand close. The average accuracy of 94.6 % and 74% was achieved for elbow and wrist motions by sEMG for healthy and amputated subjects, respectively. Simultaneously, the fNIRS modality showed an average accuracy of 96.9% and 94.5% for hand motions of healthy and amputated subjects. This study demonstrates the feasibility of hybridizing sEMG and fNIRS signals to improve the CA for transhumeral amputees, improving the control performances of multifunctional upper-limb prostheses.

Recent technological advances have enabled the prosthetic developers to derive an ideal replacement for the human arm in near future. This research presents design of a 3D Printed Myo Electric Prosthetic Hand that grasp symmetrical objects through Electromyographic signals and is intended for people suffering from Trans-radial Amputation. In the current study, a set of data about three motions that include hand open, close and rest position was been acquired from forearm muscles of a human arm. The computer aided model of the prosthetic hand was developed in SolidWorks^® which was later 3D printed using Poly Lactic Acid (PLA).The proposed design was based closely on tendon actuation, related to human hand functionality. Signal acquisition and processing has been done using Myo Armband. Different features were selected such that they can be passed through classification process in order to control the hand motions. Using the Bluetooth transmitter, the filtered data was sent and saved in Arduino Uno® controller. Later, the Support Vector Machine (SVM) was been evaluated as a classifier. The classification accuracy obtained from SVM was 96.7%. The results were found significant (p<0.01) for twelve able bodied subjects. EMG based grasp control was implemented with successful testing of designed prosthetic hand for three different motions. The practical effectiveness of the Myoelectric Hand was demonstrated by grasping household objects such battery charger, wallet and water bottle.

Development in the field of bio-mechatronics has provided diverse ways to mimic and improve the function of human limbs. Without an elbow joint, the hand remains stiff because all the muscles tension passes through this joint. Advanced myoelectric prosthetic devices are limited due to the lack of appropriate signal sources on residual amputee muscles and insufficient real-time control. Neural-machine interfaces (NMI) are representing a recent approach to develop effective applications. In this research study, an NMI is designed that presents real-time signal processing for command generation. The human brain hemodynamic responses are, therefore, translated into control commands for people suffering from transhumeral amputation. A novel and first of its kind scheme is proposed which utilizes functional near-infrared spectroscopy (fNIRS) to generate the control commands for a three-degree-of-freedom (DOF) prosthetic arm. The time window for fNIRS signals was set to 1 second. The average accuracy was found to be 82% which is a state-of-the-art result for such a technique. The accuracy ranged from 65 to 85% subject-wise. The data were trained and tested on both artificial neural network (ANN) and linear discriminant analysis (LDA). Eight out of 10 motions were correctly predicted in real time by both classifiers.

Progress in Bio robotics is opening several doors to mimic the system and purpose of human limbs, thus allowing better prostheses for amputees. This paper presents EMG based Human Machine Interface framework for estimate of reference positions of prosthetic arm for patients suffering from above elbow amputation. In the present study, a set of information about the four basic motions regarding elbow flexion/extension along with the twist of radius and ulna referred as pronation/supination has been acquired from bicep of human arm. Upon muscle activation, the numerical data set points representing the electromyographic intentions were recorded using the Thalamic Labs product i.e. Myo Armband. To analyze these acquired signals, feature selection followed with feature extraction was done in order to classify the input data extracted from human muscle. Afterwards, the performance of Artificial Neural Network (ANN) for motion classification was evaluated. Utilizing the Computer Aided design skills in SolidWorks®, a 2-DOF prosthetic arm model was developed. A mathematical model of its kinematics based on Denavit-Hardenberg convention was presented in Matlab®. Forward kinematics model were analytically verified using Matlab® with that of PeterCorke® Robotic toolbox. In order to check the physical significance of proposed work, the information of forward kinematics from Matlab® linked with that of Solid works and was simulated with reference to the EMG intentions that were captured and classified to generate the reference positions of above mentioned four motions. The classification accuracy obtained from ANN i.e. 91.9% is found significant (p<0.01) for a group of ten healthy subjects.