FIGURE 3 - uploaded by Julio Fajardo
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Top view of the modular palm sections. (1) The main PCB board controller. (2) Motors driving the index, middle, ring and little fingers. (3) Actuator in charge of the rotation of the thumb

Top view of the modular palm sections. (1) The main PCB board controller. (2) Motors driving the index, middle, ring and little fingers. (3) Actuator in charge of the rotation of the thumb

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Article
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The strict development processes of commercial upper-limb prostheses and the complexity of research projects required for their development makes them expensive for end users, both in terms of acquisition and maintenance. Moreover, many of them possess complex ways to operate and interact with the subjects, influencing patients to not favor these d...

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Context 1
... which perform the flexion/extension movements of the five fingers through an under-tendon-actuated system. The palm has three different sections with individual covers, one for the motors powering all digits but the thumb; another for the actuator that enables the thumb rotation; and the last one for the rest of the components; this is shown in Fig. 3. Such a design allows for easy maintenance without disassembling the whole artificial ...
Context 2
... is illustrated in the FSM in Fig. 12. The muscle contractions subset, Q = {q 0 , q 1 }, corresponding to the hand extension and flexion movements, respectively, and the buttons set, B = {b 0 , b 1 }, are used to operate the prosthesis. Using b 0 and b 1 alters the position forwards or backwards in the menu displayed on a µLCD screen (as shown in Fig. 13), accordingly. These changes are taken place in the state S 1 , indicating that an alteration in the screen's state is occurring. Such changes are blocked to the user the moment an action is active, because the timing for operating the motors differs between actions, so, if an action were changed while another is active, this could lead ...
Context 3
... which perform the flexion/extension movements of the five fingers through an under-tendon-actuated system. The palm has three different sections with individual covers, one for the motors powering all digits but the thumb; another for the actuator that enables the thumb rotation; and the last one for the rest of the components; this is shown in Fig. 3. Such a design allows for easy maintenance without disassembling the whole artificial ...
Context 4
... is illustrated in the FSM in Fig. 12. The muscle contractions subset, Q = {q 0 , q 1 }, corresponding to the hand extension and flexion movements, respectively, and the buttons set, B = {b 0 , b 1 }, are used to operate the prosthesis. Using b 0 and b 1 alters the position forwards or backwards in the menu displayed on a µLCD screen (as shown in Fig. 13), accordingly. These changes are taken place in the state S 1 , indicating that an alteration in the screen's state is occurring. Such changes are blocked to the user the moment an action is active, because the timing for operating the motors differs between actions, so, if an action were changed while another is active, this could lead ...

Citations

... This manuscript reports a control methodology for multiple grasps by prosthetic hand without using pattern recognition algorithms at lower computational cost and with much higher accuracy. Using single channel EMG and an Android application, it reports multiple grasps and individual finger movements by a prosthetic hand with 100% accuracy.Frequently reported grasp patterns performed by prosthetic hands available in the literature include thumb-index finger, tripod, sphere (power), sphere (precision), large diameter and lateral pinch (Abarca et al. 2019;Fajardo et al. 2020;Zhou et al. 2019). In the reported work, the prosthetic hand can execute disk (precision), disk (power), thumb-2 finger, thumb-3 finger, light tool, thumb-4 finger, platform push, adducted thumb, medium wrap, small diameter in addition to the previously reported grasp patterns. ...
Article
Full-text available
Human hand performs multiple grasp types during daily living activities. Adaptation of grasping force to avoid object slippage as employed by human brain has been postulated as an intelligent approach. Recently, research for prosthetic hands with human-like capabilities has been followed by many researchers, but with limited success. Advanced prosthetic hands that can perform different grasp types use multiple electromyogram (EMG) channels. This causes the user to wear more electrodes leading to inconveniences with inadequate grasping accuracy. This manuscript reports a prosthetic hand performing 16 grasp types in real-time using a single channel EMG customized with an Android application called Graspy. An embedded EMG based grasping controller with a network of force sensing resistors and kinematic sensors prevent slipping and breaking of grasping objects. Experiments were conducted with four able-bodied subjects for performing grasp types and individual finger movements. A proportional-integral-derivative algorithm was implemented to regulate finger joint kinematic of the prosthetic hand in relation to the force sensing resistors. Following the grasping intention based on EMG, the control algorithm can prevent slipping and breaking of grasping objects. The hand could perform grasping of objects like tennis ball, cookie, knife, screwdriver, water bottle, egg, pen, plastic container, circular disk etc. while emulating the 16 grasp types and individual finger movements with 100% accuracy.
... As a precise and noninvasive way of decoding user's intention of hand movements, the surface electromyography (sEMG)-based hand movement recognition has been extensively investigated in the area of rehabilitation engineering [1,2] and human-computer interaction [3,4]. Having realized that one of the key issues of sEMG-based hand movement recognition is a machine-learning-driven decision-making problem of classifying sequences of sEMG signals, many efforts have been made in improving sEMGbased hand movement recognition by designing more representative features [5], developing more sophisticated machine-learning models [6], and increasing the number of sensors [7]. ...
Article
Full-text available
As a machine-learning-driven decision-making problem, the surface electromyography (sEMG)-based hand movement recognition is one of the key issues in robust control of noninvasive neural interfaces such as myoelectric prosthesis and rehabilitation robot. Despite the recent success in sEMG-based hand movement recognition using end-to-end deep feature learning technologies based on deep learning models, the performance of today’s sEMG-based hand movement recognition system is still limited by the noisy, random, and nonstationary nature of sEMG signals and researchers have come up with a number of methods that improve sEMG-based hand movement via feature engineering. Aiming at achieving higher sEMG-based hand movement recognition accuracies while enabling a trade-off between performance and computational complexity, this study proposed a progressive fusion network (PFNet) framework, which improves sEMG-based hand movement recognition via integration of domain knowledge-guided feature engineering and deep feature learning. In particular, it learns high-level feature representations from raw sEMG signals and engineered time-frequency domain features via a feature learning network and a domain knowledge network, respectively, and then employs a 3-stage progressive fusion strategy to progressively fuse the two networks together and obtain the final decisions. Extensive experiments were conducted on five sEMG datasets to evaluate our proposed PFNet, and the experimental results showed that the proposed PFNet could achieve the average hand movement recognition accuracies of 87.8%, 85.4%, 68.3%, 71.7%, and 90.3% on the five datasets, respectively, which outperformed those achieved by the state of the arts.
... These compensatory motions have been shown to cause frequent shoulder pain and carpal tunnel syndrome along with other secondary impairments [9], eventually leading to rejection of the bionic arm [7]. In order to address these issues, numerous lightweight transradial prostheses with multi-DoF fingers have been developed [10]- [12]. However, very few studies focused on the multi-DoF functional wrist [13]- [15], despite the important role of the active wrist during daily living tasks. ...
Conference Paper
Full-text available
Upper limb prosthesis has a high abandonment rate due to the low function and heavyweight. These two factors are coupled because higher function leads to additional motors, batteries, and other electronics which makes the device heavier. Robotic emulators have been used for lower limb studies to decouple the device weight and high functionality in order to explore human-centered designs and controllers featuring off-board motors. In this study, we designed a prosthetic emulator for transradial (below elbow) prosthesis to identify the optimal design and control of the user. The device only weighs half of the physiological arm which features two active wrist movements with active power grasping. The detailed design of the prosthetic arm and the performance of the system is presented in this study. We envision this emulator can be used as a test-bed to identify the desired specification of transradial prosthesis, human-robot interaction, and human-in-the-loop control.
... For example, one may replace the servo motor actuation with a pneumatic or hydraulic mechanism oriented to soft robotics. The control strategy is also eligible for dexterous bionic prosthetics and robotic manipulators [29], as well as for implementing human-computer interfaces in virtual-reality applications [30]. ...
Article
Full-text available
People taken by upper limb disorders caused by neurological diseases suffer from grip weakening, which affects their quality of life. Researches on soft wearable robotics and advances in sensor technology emerge as promising alternatives to develop assistive and rehabilitative technologies. However, current systems rely on surface electromyography and complex machine learning classifiers to retrieve the user intentions. In addition, the grasp assistance through electromechanical or fluidic actuators is passive and does not contribute to the rehabilitation of upper-limb muscles. Therefore, this paper presents a robotic glove integrated with a force myography sensor. The glove-like orthosis features tendon-driven actuation through servo motors, working as an assistive device for people with hand disabilities. The detection of user intentions employs an optical fiber force myography sensor, simplifying the operation beyond the usual electromyography approach. Moreover, the proposed system applies functional electrical stimulation to activate the grasp collaboratively with the tendon mechanism, providing motion support and assisting rehabilitation.
... Our future work will focus on online evaluation of the proposed multiview deep learning framework. Moreover, in the future, we will investigate the integration of our proposed framework with hardware systems, such as upperlimb prostheses [51,52] and space robots [53,54] that are driven by multichannel sEMG signals. ...
Article
Full-text available
Hand gesture recognition based on surface electromyography (sEMG) plays an important role in the field of biomedical and rehabilitation engineering. Recently, there is a remarkable progress in gesture recognition using high-density surface electromyography (HD-sEMG) recorded by sensor arrays. On the other hand, robust gesture recognition using multichannel sEMG recorded by sparsely placed sensors remains a major challenge. In the context of multiview deep learning, this paper presents a hierarchical view pooling network (HVPN) framework, which improves multichannel sEMG-based gesture recognition by learning not only view-specific deep features but also view-shared deep features from hierarchically pooled multiview feature spaces. Extensive intrasubject and intersubject evaluations were conducted on the large-scale noninvasive adaptive prosthetics (NinaPro) database to comprehensively evaluate our proposed HVPN framework. Results showed that when using 200 ms sliding windows to segment data, the proposed HVPN framework could achieve the intrasubject gesture recognition accuracy of 88.4%, 85.8%, 68.2%, 72.9%, and 90.3% and the intersubject gesture recognition accuracy of 84.9%, 82.0%, 65.6%, 70.2%, and 88.9% on the first five subdatabases of NinaPro, respectively, which outperformed the state-of-the-art methods.
... reports 8,000 recipients of their prosthetics, which were built by volunteers around the world. Open source RPHs also enable users to alter the design to meet their unique needs; for example, the Galileo Hand (23) allows easy customization of the types of movements and number of electromyography (EMG) electrodes. ...
Article
The desire for functional replacement of a missing hand is an ancient one. Historically, humans have replaced a missing limb with a prosthesis for cosmetic, vocational, or personal autonomy reasons. The hand is a powerful tool, and its loss causes severe physical and often mental debilitation. Technological advancements have allowed the development of increasingly effective artificial hands, which can improve the quality of life of people who suffered a hand amputation. Here, we review the state of the art of robotic prosthetic hands (RPHs), with particular attention to the potential and current limits of their main building blocks: the hand itself, approaches to decoding voluntary commands and controlling the hand, and systems and methods for providing sensory feedback to the user. We also briefly describe existing approaches to characterizing the performance of subjects using RPHs for grasping tasks and provide perspectives on the future of different components and the overall field of RPH development. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 4 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
... Recently, another alternative paradigm to the fully-actuated, rigid robotic mechanisms that are heavy and expensive has been proposed. Adaptive (i.e., underactuated and compliant) robotic hardware is of low complexity, weight, cost, and is being designed to simplify the execution of complex operations and tasks such as grasping and dexterous manipulation [12]- [21]. The passive adaptability of these mechanisms that arises from the under-actuation and the structural compliance offers excellent conformability to the shape of the grasped objects maximizing grasp stability and robustness [22], [23]. ...
Conference Paper
Full-text available
Lecturers of Engineering courses around the world are struggling to increase the engagement of students through the introduction of appropriate hands-on activities and assignments. In Biomechatronics and Robotics courses these assignments typically focus on how certain devices are designed, modelled, fabricated, or controlled. The hardware for these assignments is usually purchased by some external vendor and the students only get the chance to analyze it or program it, so as to execute a useful task (e.g., programming mobile robots to perform path following tasks). Student engagement can be increased by instructing the students to prepare the hardware for their assignment. This also increases the sense of ownership of the project outcomes. In this paper, we present how a robotic gripper / hand design project and the introduction of a grasping and manipulation competition as a course assignment, can significantly increase the student engagement and their understanding of the taught concepts. The presented best practices have been trialed over the last four years in two different courses (one undergraduate and one postgraduate) of the Department of Mechanical Engineering at the University of Auckland in New Zealand. For the particular assignment the students were asked to fully develop a robotic gripper or hand from scratch using a single actuator (only the actuator and the power electronics were provided). The performance of the developed devices was assessed through the participation in a grasping and manipulation competition. All the details of the proposed assignment are presented, hoping that they could help other lecturers and teachers to prepare similar activities.
... It is actuated by miniature DC geared micromotors and is able to perform most of the biological hand's grasp patterns (Nisal et al., 2017). Similarly, an anthropomorphic prosthetic hand (Galileo Hand) that is based on 3D-printed parts was presented in Fajardo et al. (2020) (Fig. 18.1B). It incorporates brushed DC motors to execute finger movements through a tendon system. ...
Chapter
Conventional prostheses are incapable of reproducing full functionality of biological limbs. Continuous advancements in robotics and materials science have led to the development of soft and anthropomorphic prosthetic hands and legs. Mimicking the compliance and structure of biological limbs provides dexterity to upper limb prosthetic users, and natural gait to lower limb prosthetic users. Although soft and anthropomorphic prosthetic technology has reached a certain maturity level, technologies for restoring somatosensation still face significant challenges. Providing somatosensory feedback can improve the quality of life of amputees by augmenting the functionality of prostheses. Advanced prosthetic sensors obtain various sensory information, while ensuring compliant interaction with the environment. The development of electronic skins that combine multiple sensors and mimic functionalities of biological skin is possible with the recent advancements in materials technology. This chapter reviews soft and anthropomorphic upper and lower limb prostheses, prosthetic sensors, electronic skins, and applications of prosthetic interfaces.
... Their selection was established based on different interaction processes, akin price ranges and physical characteristics. That is why the same hardware, the Galileo Hand [18], (shown in Fig. 1) was adapted to fit each rendition and the same array of sensors, Thalmic Labs' Myo armband, was used on the patient's forearm, where the stump for transradial amputees is located, to create a natural operational mode. ...
... The hardware selected, the Galileo Hand [11,18], consists in a lightweight (under 350g), affordable (under $350), anthropomorphic, modular and intrinsic 3D-printed, ABS shell. It encases 5 metal geared micro DC motors, one for each finger, plus an additional one with encoder for the thumb. ...
Conference Paper
Full-text available
Numerous assistive devices possess complex ways to operate and interact with the subjects, influencing patients to shed them from their activities of daily living. With the purpose of presenting a better solution to mitigate issues generated by complex or expensive alternatives, a test comparing different user-prosthesis interfaces was elaborated to determine the effects of diverse aspects in their user-friendliness, including that of a version created for this work. A simplistic, anthropomorphic and 3D-printed upper-limb prosthesis was adapted to evaluate all the renditions considered. The chosen design facilitates the modification of its operational mode, facilitating running the tests. Additionally, the selected prosthetic device can easily be adapted to the amputees’ lifestyle in a successful way, as shown by experimental results, providing validity to the study. For the interaction process, a wireless third party device was elected to gather the user intent and, in some renditions, to work in tandem with some sort of visual feedback or with a multimodal alternative to verify their impact on the user.
... This work proposes a method to obtain the H ∞ observer gain matrix through the use of linear matrix inequalities (LMIs) methodologies [30] in order to estimate the full state of the discrete-time model of a brushed DC motor actuating the fingers of an assistive device for transradial amputees, in this case, the Galileo Hand, an intrinsic, under-tendondriven (UTD), upper-limb prosthesis [31,32]. In addition to that, the position and velocity of the fingers can also be estimated by measuring the current demanded by the actuators operating each finger on the artificial hand. ...
... The Galileo Hand is an affordable, open-source, anthropomorphic and UTD myoelectric upper-limb prosthesis for transradial amputees, whose intrinsic design allows for individual finger control [31,32]. These digits are conformed by three phalanges: distal, proximal and middle; as well as three joints: distal and proximal interphalangeal (DIP and PIP) and the metacarpophalangeal (MCP) one (illustrated in Fig. 1). ...
... The experiments to test and validate the methods proposed in Sections III and IV were carried out using the index finger of the Galileo Hand, which is controlled by a customized board located on the inside of the palm of the artificial hand, with its volar side in a supine position [31,32]. Additionally, to design the robust H ∞ observer-based filter and to solve the convex optimization problems subjected to the LMIs described in Eqs. ...
Conference Paper
Full-text available
Controlling different characteristics like force, speed and position is a relevant aspect in assistive robotics, because their interaction with diverse, common, everyday objects is divergent. Usual approaches to solve this issue involve the implementation of sensors; however, the unnecessary use of such devices increases the prosthetics’ prices in a significant manner. Thus, this work focuses on the design of an H∞ full-state observer to estimate the angular position and velocity of the motor’s gearhead in order to determine parameters such as the joints’ torque, fingertip force and the generalized coordinates of the digits of an under-tendon-driven system to replace the transductors. This is achieved by measuring the current demanded by the brushed DC motors operating the fingers of an open-source, 3D-printed and intrinsic prosthetic hand. Besides, the proposed method guarantees disturbance attenuation, as well as the asymptotic stability of the error estimation. In addition to that, the theoretical model was validated through its implementation on a prosthetic finger, showing successful results.