A Novel Force Sensitive Resistor Wheatstone Bridge for Prosthesis Control

2014 ◽  
Author(s):  
Kyle A. Schroeder ◽  
Juan De La Fuente ◽  
Thomas G. Sugar ◽  
Thierry Flaven
Keyword(s):  
Wheatstone Bridge ◽  
Prosthetic Hand ◽  
Sensor Location ◽  
Prosthetic Devices ◽  
Prosthesis Control ◽  
Intuitive Method ◽  
Opening And Closing

Control of prosthetic devices should be robust and intuitive. In this work a simple, robust, and intuitive method for opening and closing a prosthetic hand for transradial amputations is proposed. The method utilizes force sensitive resistors (FSR) in a sleeve around the residual forearm. Contracting the muscles to open or close the hand changes the shape of the forearm and the force on the FSR sensors. A novel Wheatstone bridge configuration of the sensors simplifies and expediates the calibration. Using all four FSRs as the resistors of the Wheatstone bridge, the system is relatively insensitive to sensor location. To calibrate the sensor, the user opens and closes the hand a few times. The method was demonstrated in simulation on two unamputated individuals opening and closing the hand. To demonstrate the robustness of the method, the sleeve was removed and replaced so that the FSR locations and the calibration is different, but the system is still functional.

scholarly journals Model-based control of individual finger movements for prosthetic hand function

2019 ◽  
Author(s):  
Dimitra Blana ◽  
Antonie J. van den Bogert ◽  
Wendy M. Murray ◽  
Amartya Ganguly ◽  
Agamemnon Krasoulis ◽  
...  
Keyword(s):  
Clinical Success ◽  
Hand Function ◽  
Model Performance ◽  
Prosthetic Hand ◽  
Full Potential ◽  
Robotic Hand ◽  
Real Time Control ◽  
Prosthetic Devices ◽  
Computational Performance ◽  
Prosthesis Control

AbstractProsthetic devices for hand difference have advanced considerably in recent years, to the point where the mechanical dexterity of a state-of-the-art prosthetic hand approaches that of the natural hand. Control options for users, however, have not kept pace, meaning that the new devices are not used to their full potential. Promising developments in control technology reported in the literature have met with limited commercial and clinical success. We have previously described a biomechanical model of the hand that could be used for prosthesis control. In this study, we report on three key elements of the biomechanical simulations relevant to prosthesis control: we show the performance of the model in replicating recorded hand kinematics and find average correlations of 0.89 between modelled and recorded motions; we show that the computational performance of the simulations is fast enough to achieve real-time control with a robotic hand in the loop; and we describe the use of the model for controlling object gripping. Despite some limitations in accessing sufficient driving signals, the model performance shows promise as a controller for prosthetic hands when driven with recorded EMG signals. We identify areas for future work to address these limitations.

Bio-Inspired Techniques in Human-Computer Interface for Control of Assistive Devices

2018 ◽  
pp. 23-46
Author(s):  
P. Geethanjali
Keyword(s):  
Pattern Recognition ◽  
Control Strategy ◽  
External World ◽  
Assistive Devices ◽  
Assistive Device ◽  
Prosthetic Hand ◽  
Orthotic Devices ◽  
Prosthetic Devices ◽  
Navigation Control ◽  
Selection Of

Most of the assistive devices are of user contact based control like body-powered prosthetic hand, joystick control of wheelchair, sip-and-puff, etc. and have a limited number of control movements. The performance of these assistive devices improves using bio-signals/gesture based control embedded in the processor. Gesture based control is widely used in wheelchair navigation control, communication with external world for neuromuscular impaired subjects. On the other hand, bio-signals are used widely in prosthetic devices, wheelchair control, orthotic devices, etc. with pattern recognition based control strategy. The choice and number of features used in pattern recognition for accurate control of assistive device is crucial. Further, these features performance also varies with the classifier. The appropriate selection of combination of pattern recognition will enhance the accuracy. This chapter focuses on bio-inspired techniques in selection of features and classification for the pattern recognition based assistive device control.

scholarly journals The Development of Body-Powered Prosthetic Hand Controlled by EMG Signals Using DSP Processor with Virtual Prosthesis Implementation

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Fathia H. A. Salem ◽  
Khaled S. Mohamed ◽  
Sundes B. K. Mohamed ◽  
Amal A. El Gehani
Keyword(s):  
High Performance ◽  
State Of The Art ◽  
The Body ◽  
Prosthetic Hand ◽  
Prosthetic Hands ◽  
Emg Signal ◽  
Mechanical Hand ◽  
Fast Processing ◽  
Opening And Closing ◽  
Two Stages

The state of the art in the technology of prosthetic hands is moving rapidly forward. However, there are only two types of prosthetic hands available in Libya: the Passive Hand and the Mechanical Hand. It is very important, therefore, to develop the prosthesis existing in Libya so that the use of the prosthesis is as practical as possible. Considering the case of amputation below the elbow, with two movements: opening and closing the hand, this work discusses two stages: developing the operation of the body-powered prosthetic hand by controlling it via the surface electromyography signal (sEMG) through dsPIC30f4013 processor and a servo motor and a software based on fuzzy logic concept to detect and process the EMG signal of the patient as well as using it to train the patient how to control the movements without having to fit the prosthetic arm. The proposed system has been practically implemented, tested, and gave satisfied results, especially that the used processor provides fast processing with high performance compared to other types of microcontrollers.

scholarly journals Ten guidelines for the design of non-assembly mechanisms: The case of 3D-printed prosthetic hands

2018 ◽  
Vol 232 (9) ◽  
pp. 962-971 ◽  
Author(s):  
Juan Sebastian Cuellar ◽  
Gerwin Smit ◽  
Amir A Zadpoor ◽  
Paul Breedveld
Keyword(s):  
Developing Countries ◽  
3D Printing ◽  
Fused Deposition Modelling ◽  
Prosthetic Hand ◽  
Prosthetic Devices ◽  
Hand Prosthesis ◽  
Fused Deposition ◽  
Prosthetic Hands ◽  
Design Considerations ◽  
3D Printed

In developing countries, prosthetic workshops are limited, difficult to reach, or even non-existent. Especially, fabrication of active, multi-articulated, and personalized hand prosthetic devices is often seen as a time-consuming and demanding process. An active prosthetic hand made through the fused deposition modelling technology and fully assembled right after the end of the 3D printing process will increase accessibility of prosthetic devices by reducing or bypassing the current manufacturing and post-processing steps. In this study, an approach for producing active hand prosthesis that could be fabricated fully assembled by fused deposition modelling technology is developed. By presenting a successful case of non-assembly 3D printing, this article defines a list of design considerations that should be followed in order to achieve fully functional non-assembly devices. Ten design considerations for additive manufacturing of non-assembly mechanisms have been proposed and a design case has been successfully addressed resulting in a fully functional prosthetic hand. The hand prosthesis can be 3D printed with an inexpensive fused deposition modelling machine and is capable of performing different types of grasping. The activation force required to start a pinch grasp, the energy required for closing, and the overall mass are significantly lower than body-powered commercial prosthetic hands. The results suggest that this non-assembly design may be a good alternative for amputees in developing countries.

scholarly journals Control of prosthetic hand by using mechanomyography signals based on support-vector machine classifier

2021 ◽  
Vol 23 (2) ◽  
pp. 1180
Author(s):  
Firas Saaduldeen Ahmed ◽  
Noha Abed-Al-Bary Al-jawady
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machine ◽  
Wavelet Packet ◽  
Support Vector ◽  
Prosthetic Hand ◽  
Hand Gestures ◽  
Prosthetic Devices ◽  
Prosthetic Hands ◽  
Average Accuracy ◽  
And Control

<div>Prosthetic devices are necessary to help amputees achieve their daily activity in the natural way possible. The prosthetic hand has controlled by type of signals such as electromyography (EMG) and mechanomyography (MMG). The MMG signals have represented mechanical signals that generate during muscle contraction. These signals can be detected by accelerometers or microphones and any kind of sensors that can detect muscle vibrations. The contribution of the current paper is classifying hand gestures and control prosthetic hands depends on pattern recognition through accelerometer and microphone are to detect MMG signals. In addition to the cost of prosthetic hand less than other designs. Six subjects are involved. In this present work is the devices. In this study, two of them are amputee subjects. Each subject performs seven classes of movements. Pattern recognition (PR) is used to classify hand gestures. The wavelet packet transform (WPT) and root mean square (RMS) as features extracted from the signals and support vector machine (SVM) as a classifier. The average accuracy is 88.94% for offline tests and 84.45% for online tests. 3D printing technology is used in this study to build prosthetic hands.</div>

ANALYZING UPPER LIMB REFLEXIVE RESPONSES FOR PROSTHETIC APPLICATIONS

2012 ◽  
Vol 12 (05) ◽  
pp. 1240022 ◽  
Author(s):  
WENWEI YU ◽  
HIROKAZU SOMA ◽  
JOSE GONZALEZ
Keyword(s):  
Upper Limb ◽  
Daily Living ◽  
Efficient Solution ◽  
Joint Angle ◽  
Daily Life ◽  
Prosthetic Hand ◽  
Prosthetic Devices ◽  
Hand Control ◽  
Force Perturbation ◽  
Human Upper Limb

Recently, there has been an increasing interest in upper limb prosthetic hand control, but most of these studies focused on the detection of exact motion intentions. Few results have been reported on the perturbation resistance of prostheses, which is extremely important for their uses in daily life. Understanding the human upper limb reflexive mechanism would provide an efficient solution to improve the perturbation resistance of prostheses. In this study, upper limb reflexive responses to force perturbation were measured in the conditions as determined from activities of daily living. Specifically, not only muscle activities, but also joint angle changes of the reflexive responses to multi-direction, multi-site perturbation during a reaching motion were recorded and analyzed. Results showed a direction-dependency of the upper limb reflexive mechanism and the different roles of shoulder, elbow, and wrist during the responses. The results could be applied to the control system of prosthetic devices for improving their perturbation resistance.

scholarly journals THE DIRECT CURRENT RESISTANCE OF NITELLA

1930 ◽  
Vol 13 (4) ◽  
pp. 495-508 ◽  
Author(s):  
L. R. Blinks
Keyword(s):  
Direct Current ◽  
Tap Water ◽  
High Mobility ◽  
Wheatstone Bridge ◽  
Living Cells ◽  
Transient Effects ◽  
Intact Cells ◽  
Direct Current Resistance ◽  
Opening And Closing ◽  
String Galvanometer

The electrical resistance of Nitella cells to direct current is determined in a Wheatstone bridge, using a vacuum-tube detector, and string galvanometer. Very small currents are passed through the cells, to avoid stimulation. The galvanometer record shows typical transient effects in the living cells at opening and closing of the circuit, due to the development of back E.M.F. With 1 cm. contacts of tap water, and 1 cm. between contacts the resistances of living cells are usually between 1,000,000 and 2,000,000 ohms. They go as high as 3,500,000 ohms when the cells are in the best condition. The resistance falls to about 50,000 ohms immediately after killing. Leakage around the cell is small because the wall is imbibed with tap water. By measuring the resistance of the isolated wall (air-filled), and by varying the areas of contact with intact cells, the effective protoplasmic resistance is calculated. This varies from 100,000 to 700,000 ohms per square centimeter of surface, with a typical value of about 250,000 ohms per square centimeter. This high resistance represents a low permeability for most ions, since the values are nearly as high with contacts of 0.01 M NaCl, CaCl2, LiCl, NH4Cl, and MgSO4. The resistances are greatly reduced however by solutions of KCl, which is correlated with a high mobility of the K+ ion in the protoplasm. Electrical stimulation causes a marked reduction of resistance, which may be due to exomosis of KCl.

scholarly journals Vector Autoregressive Hierarchical Hidden Markov Models for Extracting Finger Movements Using Multichannel Surface EMG Signals

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Nebojša Malešević ◽  
Dimitrije Marković ◽  
Gunter Kanitz ◽  
Marco Controzzi ◽  
Christian Cipriani ◽  
...  
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Surface Emg ◽  
User Preferences ◽  
Computational Technique ◽  
Prosthetic Hand ◽  
Vector Autoregressive ◽  
Emg Signal ◽  
Prosthesis Control

We present a novel computational technique intended for the robust and adaptable control of a multifunctional prosthetic hand using multichannel surface electromyography. The initial processing of the input data was oriented towards extracting relevant time domain features of the EMG signal. Following the feature calculation, a piecewise modeling of the multidimensional EMG feature dynamics using vector autoregressive models was performed. The next step included the implementation of hierarchical hidden semi-Markov models to capture transitions between piecewise segments of movements and between different movements. Lastly, inversion of the model using an approximate Bayesian inference scheme served as the classifier. The effectiveness of the novel algorithms was assessed versus methods commonly used for real-time classification of EMGs in a prosthesis control application. The obtained results show that using hidden semi-Markov models as the top layer, instead of the hidden Markov models, ranks top in all the relevant metrics among the tested combinations. The choice of the presented methodology for the control of prosthetic hand is also supported by the equal or lower computational complexity required, compared to other algorithms, which enables the implementation on low-power microcontrollers, and the ability to adapt to user preferences of executing individual movements during activities of daily living.

Bio-Inspired Techniques in Human-Computer Interface for Control of Assistive Devices

2018 ◽  
pp. 377-396
Author(s):  
P. Geethanjali
Keyword(s):  
Pattern Recognition ◽  
Control Strategy ◽  
External World ◽  
Assistive Devices ◽  
Assistive Device ◽  
Prosthetic Hand ◽  
Orthotic Devices ◽  
Prosthetic Devices ◽  
Navigation Control ◽  
Selection Of

Most of the assistive devices are of user contact based control like body-powered prosthetic hand, joystick control of wheelchair, sip-and-puff, etc. and have a limited number of control movements. The performance of these assistive devices improves using bio-signals/gesture based control embedded in the processor. Gesture based control is widely used in wheelchair navigation control, communication with external world for neuromuscular impaired subjects. On the other hand, bio-signals are used widely in prosthetic devices, wheelchair control, orthotic devices, etc. with pattern recognition based control strategy. The choice and number of features used in pattern recognition for accurate control of assistive device is crucial. Further, these features performance also varies with the classifier. The appropriate selection of combination of pattern recognition will enhance the accuracy. This chapter focuses on bio-inspired techniques in selection of features and classification for the pattern recognition based assistive device control.

scholarly journals Multi-grip classification-based prosthesis control with two EMG-IMU sensors

2019 ◽  
Author(s):  
Agamemnon Krasoulis ◽  
Sethu Vijayakumar ◽  
Kianoush Nazarpour
Keyword(s):  
Machine Learning ◽  
Upper Limb ◽  
Prosthetic Hand ◽  
Myoelectric Prosthesis ◽  
Inertial Measurement ◽  
Machine Learning Methods ◽  
Pick And Place ◽  
Measurement Units ◽  
Prosthesis Control ◽  
Emg Electrodes

AbstractIn the field of upper-limb myoelectric prosthesis control, the use of statistical and machine learning methods has been long proposed as a means of enabling intuitive grip selection and actuation. Recently, this paradigm has found its way toward commercial adoption. Machine learning-based prosthesis control typically relies on the use of a large number of electrodes. Here, we propose an end-to-end strategy for multi-grip, classification-based prosthesis control using only two sensors, comprising electromyography (EMG) electrodes and inertial measurement units (IMUs). We emphasize the importance of accurately estimating posterior class probabilities and rejecting predictions made with low confidence, so as to minimize the rate of unintended prosthesis activations. To that end, we propose a confidence-based error rejection strategy using grip-specific thresholds. We evaluate the efficacy of the proposed system with real-time pick and place experiments using a commercial multi-articulated prosthetic hand and involving 12 able-bodied and two transradial (i.e., below-elbow) amputee participants. Results promise the potential for deploying intuitive, classification-based multi-grip control in existing upper-limb prosthetic systems subject to small modifications.

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