Prosthetic Hand using BCI System

2021 ◽  
pp. 185-208
Author(s):  
N A Abu Osman ◽  
S Yahud
Keyword(s):  
Prosthetic Hand ◽  
Bci System

scholarly journals Design of a 3D-printed hand prosthesis featuring articulated bio-inspired fingers

2020 ◽  
pp. 095441192098088
Author(s):  
Juan Sebastian Cuellar ◽  
Dick Plettenburg ◽  
Amir A Zadpoor ◽  
Paul Breedveld ◽  
Gerwin Smit
Keyword(s):  
3D Printing ◽  
Design Principles ◽  
Prosthetic Hand ◽  
Hand Prosthesis ◽  
Fused Deposition ◽  
Pinch Force ◽  
Actuation System ◽  
3D Printed ◽  
Energy Dissipated ◽  
Dual Material

Various upper-limb prostheses have been designed for 3D printing but only a few of them are based on bio-inspired design principles and many anatomical details are not typically incorporated even though 3D printing offers advantages that facilitate the application of such design principles. We therefore aimed to apply a bio-inspired approach to the design and fabrication of articulated fingers for a new type of 3D printed hand prosthesis that is body-powered and complies with basic user requirements. We first studied the biological structure of human fingers and their movement control mechanisms in order to devise the transmission and actuation system. A number of working principles were established and various simplifications were made to fabricate the hand prosthesis using a fused deposition modelling (FDM) 3D printer with dual material extrusion. We then evaluated the mechanical performance of the prosthetic device by measuring its ability to exert pinch forces and the energy dissipated during each operational cycle. We fabricated our prototypes using three polymeric materials including PLA, TPU, and Nylon. The total weight of the prosthesis was 92 g with a total material cost of 12 US dollars. The energy dissipated during each cycle was 0.380 Nm with a pinch force of ≈16 N corresponding to an input force of 100 N. The hand is actuated by a conventional pulling cable used in BP prostheses. It is connected to a shoulder strap at one end and to the coupling of the whiffle tree mechanism at the other end. The whiffle tree mechanism distributes the force to the four tendons, which bend all fingers simultaneously when pulled. The design described in this manuscript demonstrates several bio-inspired design features and is capable of performing different grasping patterns due to the adaptive grasping provided by the articulated fingers. The pinch force obtained is superior to other fully 3D printed body-powered hand prostheses, but still below that of conventional body powered hand prostheses. We present a 3D printed bio-inspired prosthetic hand that is body-powered and includes all of the following characteristics: adaptive grasping, articulated fingers, and minimized post-printing assembly. Additionally, the low cost and low weight make this prosthetic hand a worthy option mainly in locations where state-of-the-art prosthetic workshops are absent.

Prosthetic Hand Controlled Via Intelligent Fuzzy Controller

2020 ◽  
Author(s):  
Asma. R. Qishqish ◽  
A. M. EIbreki ◽  
Tawfiq. H. Elmenfy ◽  
Zakariya Rajab
Keyword(s):  
Fuzzy Controller ◽  
Prosthetic Hand

scholarly journals The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1613
Author(s):  
Man Li ◽  
Feng Li ◽  
Jiahui Pan ◽  
Dengyong Zhang ◽  
Suna Zhao ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
System Control ◽  
Average Accuracy ◽  
Training Samples ◽  
The Disabled ◽  
Limited Training Samples ◽  
The Stability ◽  
Electroencephalogram Eeg ◽  
Bci System

In addition to helping develop products that aid the disabled, brain–computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance or because they easily cause fatigue. In this paper, we propose a P300 brain–computer-interface game (MindGomoku) to explore a feasible and natural way to play games by using electroencephalogram (EEG) signals in a practical environment. The novelty of this research is reflected in integrating the characteristics of game rules and the BCI system when designing BCI games and paradigms. Moreover, a simplified Bayesian convolutional neural network (SBCNN) algorithm is introduced to achieve high accuracy on limited training samples. To prove the reliability of the proposed algorithm and system control, 10 subjects were selected to participate in two online control experiments. The experimental results showed that all subjects successfully completed the game control with an average accuracy of 90.7% and played the MindGomoku an average of more than 11 min. These findings fully demonstrate the stability and effectiveness of the proposed system. This BCI system not only provides a form of entertainment for users, particularly the disabled, but also provides more possibilities for games.

scholarly journals Human Hand Anatomy-Based Prosthetic Hand

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 137
Author(s):  
Larisa Dunai ◽  
Martin Novak ◽  
Carmen García Espert
Keyword(s):  
3D Printing ◽  
Degrees Of Freedom ◽  
Use Of Force ◽  
Hand Movements ◽  
Human Hand ◽  
Prosthetic Hand ◽  
Object Surface ◽  
Hand Phalanges ◽  
High Level

The present paper describes the development of a prosthetic hand based on human hand anatomy. The hand phalanges are printed with 3D printing with Polylactic Acid material. One of the main contributions is the investigation on the prosthetic hand joins; the proposed design enables one to create personalized joins that provide the prosthetic hand a high level of movement by increasing the degrees of freedom of the fingers. Moreover, the driven wire tendons show a progressive grasping movement, being the friction of the tendons with the phalanges very low. Another important point is the use of force sensitive resistors (FSR) for simulating the hand touch pressure. These are used for the grasping stop simulating touch pressure of the fingers. Surface Electromyogram (EMG) sensors allow the user to control the prosthetic hand-grasping start. Their use may provide the prosthetic hand the possibility of the classification of the hand movements. The practical results included in the paper prove the importance of the soft joins for the object manipulation and to get adapted to the object surface. Finally, the force sensitive sensors allow the prosthesis to actuate more naturally by adding conditions and classifications to the Electromyogram sensor.

scholarly journals Induction of Neural Plasticity Using a Low-Cost Open Source Brain-Computer Interface and a 3D-Printed Wrist Exoskeleton

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 572
Author(s):  
Mads Jochumsen ◽  
Taha Al Muhammadee Janjua ◽  
Juan Carlos Arceo ◽  
Jimmy Lauber ◽  
Emilie Simoneau Buessinger ◽  
...  
Keyword(s):  
Open Source ◽  
Neural Plasticity ◽  
Motor Evoked Potentials ◽  
Low Cost ◽  
True Positive Rate ◽  
Positive Rate ◽  
3D Printed ◽  
Major Factors ◽  
And Control ◽  
Bci System

Brain-computer interfaces (BCIs) have been proven to be useful for stroke rehabilitation, but there are a number of factors that impede the use of this technology in rehabilitation clinics and in home-use, the major factors including the usability and costs of the BCI system. The aims of this study were to develop a cheap 3D-printed wrist exoskeleton that can be controlled by a cheap open source BCI (OpenViBE), and to determine if training with such a setup could induce neural plasticity. Eleven healthy volunteers imagined wrist extensions, which were detected from single-trial electroencephalography (EEG), and in response to this, the wrist exoskeleton replicated the intended movement. Motor-evoked potentials (MEPs) elicited using transcranial magnetic stimulation were measured before, immediately after, and 30 min after BCI training with the exoskeleton. The BCI system had a true positive rate of 86 ± 12% with 1.20 ± 0.57 false detections per minute. Compared to the measurement before the BCI training, the MEPs increased by 35 ± 60% immediately after and 67 ± 60% 30 min after the BCI training. There was no association between the BCI performance and the induction of plasticity. In conclusion, it is possible to detect imaginary movements using an open-source BCI setup and control a cheap 3D-printed exoskeleton that when combined with the BCI can induce neural plasticity. These findings may promote the availability of BCI technology for rehabilitation clinics and home-use. However, the usability must be improved, and further tests are needed with stroke patients.

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