Detection of fingertips in human hand movement sequences

Author(s):  
Claudia Nölker ◽  
Helge Ritter
2021 ◽  
Vol 12 (1) ◽  
pp. 69-83
Author(s):  
Saygin Siddiq Ahmed ◽  
Ahmed R. J. Almusawi ◽  
Bülent Yilmaz ◽  
Nuran Dogru

Abstract. This study introduces a new control method for electromyography (EMG) in a prosthetic hand application with a practical design of the whole system. The hand is controlled by a motor (which regulates a significant part of the hand movement) and a microcontroller board, which is responsible for receiving and analyzing signals acquired by a Myoware muscle device. The Myoware device accepts muscle signals and sends them to the controller. The controller interprets the received signals based on the designed artificial neural network. In this design, the muscle signals are read and saved in a MATLAB system file. After neural network program processing by MATLAB, they are then applied online to the prosthetic hand. The obtained signal, i.e., electromyogram, is programmed to control the motion of the prosthetic hand with similar behavior to a real human hand. The designed system is tested on seven individuals at Gaziantep University. Due to the sufficient signal of the Mayo armband compared to Myoware sensors, Mayo armband muscle is applied in the proposed system. The discussed results have been shown to be satisfactory in the final proposed system. This system was a feasible, useful, and cost-effective solution for the handless or amputated individuals. They have used the system in their day-to-day activities that allowed them to move freely, easily, and comfortably.


2013 ◽  
Vol 284-287 ◽  
pp. 3126-3130 ◽  
Author(s):  
Ching Yee Yong ◽  
Rubita Sudirman ◽  
Nasrul Humaimi Mahmood ◽  
Kim Mey Chew

This study investigates and acts as a trial clinical outcome for human motion and behavior analysis in order to investigate human arm movement during jogging and walking. It was developed to analyze and access the quality of human motion that can be used in hospitals, clinics and human motion researches. It aims to establish how widespread the movement and motion of arm will bring to effect of human in life. An experiment was set up in a laboratory environment with conjunction of analyzing human motion and its behavior. The instruments demonstrate adequate internal consistency of optimum scatter plot in gyroscope and accelerometer for pattern classification. PCA used in this study was successfully differentiate and classify


1990 ◽  
Vol 63 (1) ◽  
pp. 161-172 ◽  
Author(s):  
P. J. Cordo

1. The individual joint rotations of a movement sequence might be controlled either by a central motor plan or by motion-dependent (i.e., kinesthetic) sensory input. Most previous research has focused on how the nervous system uses central motor plans to control movement sequences. This study examined how the nervous system uses kinesthetic input to control a multijoint movement sequence. 2. Human subjects were trained to extend the elbow horizontally at 22 degrees/s and to open the hand as the elbow passed through a 2 degrees-wide target zone. Different distances to the target zone were used to examine a wide range of movement times of the elbow to target zone (i.e., 150-1,500 ms). 3. A hydraulic apparatus simulated a spring resistance to the elbow extension. In some trials, the spring constant was unexpectedly increased or decreased just before the subject initiated the elbow extension, causing the elbow to slow down or speed up. Because these changes in spring constant were randomly imposed and because no visual feedback was available, subjects had to use kinesthetic input to control this motor task. 4. The experimental subjects employed two different strategies for the use of kinesthetic input to control this motor task. In the first strategy, the subjects used kinesthetic input related to the elbow rotation to detect and correct velocity errors caused by the changes in spring constant. The onset of error correction varied between 92 and 196 ms after the appearance of velocity errors. The proportion of the error corrected by the time the elbow reached the target zone varied between 31 and 78%, depending on the movement time to the target zone. However, because this correction for velocity errors was neither instantaneous nor complete, the changes in spring constant caused leads and lags in the time that the elbow reached the target zone. 5. In the second strategy, subjects used kinesthetic input related to the elbow rotation to advance or delay the onset of the hand movement, thereby compensating for leads and lags in the arrival of the elbow at the target zone. These adjustments in the triggering time of the hand movement allowed subjects to open the hand while the elbow was in the target zone. This kinesthetic triggering mechanism was effective for elbow rotations reaching the target zone within 150-1,500 ms. 6. These results suggest that, to fully understand how multijoint movement sequences are controlled by the nervous system, sensory mechanisms must be considered in addition to central mechanisms.


2009 ◽  
Vol 101 (2) ◽  
pp. 1002-1015 ◽  
Author(s):  
Uri Maoz ◽  
Alain Berthoz ◽  
Tamar Flash

One long-established simplifying principle behind the large repertoire and high versatility of human hand movements is the two-thirds power law—an empirical law stating a relationship between local geometry and kinematics of human hand trajectories during planar curved movements. It was further generalized not only to various types of human movements, but also to motion perception and prediction, although it was unsuccessful in explaining unconstrained three-dimensional (3D) movements. Recently, movement obeying the power law was proved to be equivalent to moving with constant planar equi-affine speed. Generalizing such motion to 3D space—i.e., to movement at constant spatial equi-affine speed—predicts the emergence of a new power law, whose utility for describing spatial scribbling movements we have previously demonstrated. In this empirical investigation of the new power law, subjects repetitively traced six different 3D geometrical shapes with their hand. We show that the 3D power law explains the data consistently better than both the two-thirds power law and an additional power law that was previously suggested for spatial hand movements. We also found small yet systematic modifications of the power-law's exponents across the various shapes, which further scrutiny suggested to be correlated with global geometric factors of the traced shape. Nevertheless, averaging over all subjects and shapes, the power-law exponents are generally in accordance with constant spatial equi-affine speed. Taken together, our findings provide evidence for the potential role of non-Euclidean geometry in motion planning and control. Moreover, these results seem to imply a relationship between geometry and kinematics that is more complex than the simple local one stipulated by the two-thirds power law and similar models.


1995 ◽  
Vol 106 (3) ◽  
Author(s):  
B. Okuda ◽  
H. Tanaka ◽  
Y. Tomino ◽  
K. Kawabata ◽  
H. Tachibana ◽  
...  

Robotic Arms are generally a programmable type of mechanical arm with functions similar to human arm which is either the sum of total mechanism or may be a complex robot part. These robotic arms are employed in assembly line of industries performing complex process like drilling, painting and painting etc. It is possible to fabricate gesture controlled Industrial robot arms. The robot is easily accessible and requires lesser controlling effects. In this work a glove attached to human hand is incorporated with flex sensors and transceiver. The flex sensor resistance can be varied by hand movement which is transferred to the axis of robot. The resistance of glove can make robot rotate either angular or in a linear motion about its axis. A transceiver circuit is employed for signal control which is capable of transmitting and receiving signal between human hand and robotic arm.The flex sensor senses and gives corresponding signals. The analog signal from the flex sensor given to Arduino, it will work according to the Arduino program. The signal is transmitted from Arduino to Zigbee for wireless communication. The driver circuit put together with transistor to control the relay. The relay output is connected directly to motor joined with the robot. With this arrangement arm can be used for pick and place application. The robotic arm delivers the programmed movement and the proposed model have widespread application for people working in hazardous areas.


Author(s):  
Hasan Smajic ◽  
Toni Duspara

During a current project, a fully functioning prototype of a 3D printed bionic hand was developed. This paper explains principles such as: bionic hand movement, working rules of sensors and actuators etc. Design of all parts are performed, including the wiring of control system. The project includes two types of sensor control systems for bionic hand. One is with stretch sensors that replicates movement of human hand onto the bionic model. Other type is using machine learning (AI) and a camera. The average amputee cost is $30.000,00 for a new custom-built arm/hand. With the advancement of technology through time, manufacturing processes became cheaper and more accessible. Technical innovation of this project was the fact, that a functional prosthetic hand prototype was built for price lower than $50,00. The prototype does not have all the functions and capabilities as the full priced custom prosthetic hand, but it can replicate altogether the movements as the real device. All the fingers are capable of moving individually, sideways and with the work on the new version, gripping function could be perfected. Further work on materials, could help find the adequate material to increase friction and thusly enhance the grasp strength. The new challenge would involve testing with different kinds of materials to improve the working stability. As it was already unfavorable, this project was mostly based onto the actuation part, or rather the hand itself. Second part of research would involve exploring of different sensor systems. Two control solutions were designed and tested. Next steps would involve neurotransmission sensors, where arm would be controlled using brainwaves as signals that are transformed in movement.


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