Low-cost and open-source anthropomorphic prosthetics hand using linear actuators

A robust, low cost, open-source, and low power consumption in the research of prosthetics hand is essential. The purpose of this study is to develop a low-cost, open-source anthropomorphic prosthetics hand using linear actuator based on electromyography (EMG) signal control. The main advantages of this proposed method are the low-cost, lightweight and simplicity of controlling the prosthetic hand using only single channel. This is achieved by evaluating the DC motor and exploring number of locations of the EMG signal. The development of prosthetics hand consists of 3D anthropomorphic hand design, active electrodes, microcontroller, and linear actuator. The active electrodes recorded the EMG signal from extensor carpi radialis longus. The built-in EMG amplifier on the electrode amplified the EMG signal. Further, the A/D converter in the Arduino microcontroller converted the analog signal into digital. A filtering process consisted of bandpass and notch filter was performed before it used as a control signal. The linear actuator controlled each finger for flexion and extension motion. In the assessment of the design, the prosthetic hand capable of grasping ten objects. In this study, the cost and weight of the prosthetics hand are 471.99 US$ and 0.531 kg, respectively. This study has demonstrated the design of low cost and open-source of prosthetics hand with reasonable cost and lightweight. Furthermore, this development could be applied to amputee subjects

A Biomechanical Model for the Development of Myoelectric Hand Prosthesis Control Systems

Advanced myoelectric hand prostheses aim to reproduce as much of the human hand's functionality as possible. Development of the control system of such a prosthesis is strongly connected to its mechanical design; the control system requires accurate information on the prosthesis' structure and the surrounding environment, which can make development difficult without a finalized mechanical prototype. This paper presents a new framework for the development of electromyographic hand control systems, consisting of a prosthesis model based on the biomechanical structure of the human hand. The model's dynamic structure uses an ellipsoidal representation of the phalanges. Other features include underactuation in the fingers and thumb modeled with bond graphs, and a viscoelastic contact model. The model's functions are demonstrated by the execution of lateral and tripod grasps, and evaluated with regard to joint dynamics and applied forces. Finally, additions are suggested with which this model can be of use in mechanical design and patient training as well

A Sustainable & Biologically Inspired Prosthetic Hand for Healthcare

There are many persons in the world affected by amputation. Upper limb amputations require high cost prosthetic devices in order to provide significant motor recovery. We propose a sustainable design and control of a new anthropomorphic prosthetic hand: all components are modular and exchangeable and they can be assembled by non-expert users. Phalanges & articulations of the fingers and the palm are manufactured via a 3D printing process in Acrylonitrile Butadiene Styrene (ABS) or Polyactic Acid (PLA) materials. The design is optimized in order to provide human-like motion and grasping taxonomy through linear actuators and flexion tendon mechanisms, which are embedded within the palm. HardWare (HW) and Software (SW) open sourced units for ElectroMyography (EMG) input and control can be combined with a user-friendly and intuitive Graphical User Interface (GUI) to enable amputees handling the prosthesis. To reduce the environmental impact of the device lifetime cycle, the material and energy consumption were optimized by adopting: simple design & manufacturing, high dexterity, open source HW and SW, low cost components, anthropomorphic design

Human-centered Electric Prosthetic (HELP) Hand

In developing countries such as India, there is a higher rate of amputations among the population but a lack of viable, low cost solutions. Through a partnership with Indian non-profit Bhagwan Mahaveer Viklang Sahayata Samiti (BMVSS), the team designed a functional, robust, and low cost electrically powered prosthetic hand that communicates with people with unilateral, transradial amputations in urban India through a biointerface. The device uses compliant tendon actuation, small linear servos, and a wearable sleeve outfitted with electromyography (EMG) sensors to produce a device that, once placed inside a prosthetic glove, is anthropomorphic in both look and feel. The hand is capable of forming three grips through the use of a manually adjustable opposable thumb: the key, pinch, and wrap grips. The hand also provides vibrotactile user feedback upon completion of a grip. The design includes a prosthetic gel liner to provide a layer of cushion and comfort for safe use by the user. These results show that it is possible to create a low cost, electrically powered prosthetic hand for users in developing countries without sacrificing functionality. In order for this design to be truly adjustable to each user, the creation of an easily navigable graphical user interface (GUI) will have to be a future goal. The prosthesis prototype was developed such that future groups can design for manufacturing and distribution in India

Implementation of Supervised Machine Learning on Embedded Raspberry Pi System to Recognize Hand Motion as Preliminary Study for Smart Prosthetic Hand

EMG signals have random, non-linear, and non-stationary characteristics that require the selection of the suitable feature extraction and classifier for application to prosthetic hands based on EMG pattern recognition. This research aims to implement EMG pattern recognition on an embedded Raspberry Pi system to recognize hand motion as a preliminary study for a smart prosthetic hand. The contribution of this research is that the time domain feature extraction model and classifier machine can be implemented into the Raspberry Pi embedded system. In addition, the machine learning training and evaluation process is carried out online on the Raspberry Pi system. The online training process is carried out by integrating EMG data acquisition hardware devices, time domain features, classifiers, and motor control on embedded machine learning using Python programming. This study involved ten respondents in good health. EMG signals are collected at two lead flexor carpi radialis and extensor digitorum muscles. EMG signals are extracted using time domain features (TDF) mean absolute value (MAV), root mean square (RMS), variance (VAR) using a window length of 100 ms. Supervised machine learning decision tree (DT), support vector machine (SVM), and k-nearest neighbor (KNN) are chosen because they have a simple algorithm structure and less computation. Finally, the TDF and classifier are embedded in the Raspberry Pi 3 Model B+ microcomputer. Experimental results show that the highest accuracy is obtained in the open class, 97.03%. Furthermore, the additional datasets show a significant difference in accuracy (p-value <0.05). Based on the evaluation results obtained, the embedded system can be implemented for prosthetic hands based on EMG pattern recognition

Development of Real-Time Electromyography Controlled 3D Printed Robot Hand Prototype

Developing an anthropomorphic robotic hand (ARH) has become a relevant research field due to the need to help the amputees live their life as normal people. However, the current state of research is unsatisfactory, especially in terms of structural design and the robot control method. This paper, which proposes a 3D printed ARH structure that follows the average size of an adult human hand, consists of five fingers with a tendon-driven actuator mechanism embedded in each finger structure. Besides that, the movement capability of the developed 3D printed robot hand validated by using motion capture analysis to ensure the similarity to the expected motion range in structural design is achieved. Its system functionality test was conducted in three stages: (1) muscular activity detection, (2) object detection for individual finger movement control, and (3) integration of both stages in one algorithm. Finally, an ARH was developed, which resembles human hand features, as well as a reliable system that can perform opened hand palm and some grasping postures for daily use

Principal components analysis based control of a multi-dof underactuated prosthetic hand

<p>Abstract</p> <p>Background</p> <p>Functionality, controllability and cosmetics are the key issues to be addressed in order to accomplish a successful functional substitution of the human hand by means of a prosthesis. Not only the prosthesis should duplicate the human hand in shape, functionality, sensorization, perception and sense of body-belonging, but it should also be controlled as the natural one, in the most intuitive and undemanding way. At present, prosthetic hands are controlled by means of non-invasive interfaces based on electromyography (EMG). Driving a multi degrees of freedom (DoF) hand for achieving hand dexterity implies to selectively modulate many different EMG signals in order to make each joint move independently, and this could require significant cognitive effort to the user.</p> <p>Methods</p> <p>A Principal Components Analysis (PCA) based algorithm is used to drive a 16 DoFs underactuated prosthetic hand prototype (called CyberHand) with a two dimensional control input, in order to perform the three prehensile forms mostly used in Activities of Daily Living (ADLs). Such Principal Components set has been derived directly from the artificial hand by collecting its sensory data while performing 50 different grasps, and subsequently used for control.</p> <p>Results</p> <p>Trials have shown that two independent input signals can be successfully used to control the posture of a real robotic hand and that correct grasps (in terms of involved fingers, stability and posture) may be achieved.</p> <p>Conclusions</p> <p>This work demonstrates the effectiveness of a bio-inspired system successfully conjugating the advantages of an underactuated, anthropomorphic hand with a PCA-based control strategy, and opens up promising possibilities for the development of an intuitively controllable hand prosthesis.</p