Myoelectric Prosthetic Hand Research Articles

Development of Joint Flexion Mechanism with Myoelectric Prosthetic Hand for Stable Grasp in Power Grasp and Precision Grasp

Development of Joint Flexion Mechanism with Myoelectric Prosthetic Hand for Stable Grasp in Power Grasp and Precision Grasp

Development of new cosmetic gloves for myoelectric prosthetic hand using superelastic rubber

This paper reports on the design and development of new cosmetic gloves made of two different superelastic rubbers – thermoplastic styrene elastomer (TSE) and silicone rubber (TSG silicone) – and compares them with gloves made of polyvinyl chloride (PVC) for myoelectric prosthetic hands to realize a realistic appearance and flexible motion. The materials are compared in terms of their appearance, material, mechanical, and sensing properties. Appearance properties include the shape, wrinkles, fingerprints, texture, nail, and color of the hand; these properties are designed so as to produce a prosthetic hand that looks similar to a human hand. The material properties are evaluated in terms of adaptability for daily living without preventing finger motions of the powered hand by performing a tear strength test. Mechanical properties are improved by designing the thickness of the palm to grip an object. The sensing properties are essential for acquiring information about the object and the environment. The overall performance is evaluated through a material engineering test and a pick-and-place test with a powered prosthetic hand. Tear strength comparisons showed that TSE and TSG silicone could respectively withstand 5–7 and 3 times the strain that PVC could withstand before breakage. The TSE glove shows the highest stretching length before breaking and shows high flexibility even after breaking. The electric currents during EMG prosthetic hand motion showed that TSE and TSG silicone gloves successfully reduced energy consumption by around one-third for many hand movements. Flexibility test results for the maximum opening posture showed that the PVC glove greatly restricted the hand opening width. However, the differences between the cases without and with TSE gloves were very small; therefore, both cases show the same range of motion. The flexible TSE facilitated easy fitting and therefore had the lowest fitting time; in fact, it can be worn in one-third the time required for wearing PVC or TSG silicone gloves. In pick-and-place experiments, TSG silicone and TSE gloves both showed similar results for successfully grasping objects. The TSE glove is hard to break and has high elasticity; therefore, nails can be added to it. Furthermore, TSG resin is thermosetting and can be processed at room temperature, making it easy to impart conductivity. Therefore, the TSG silicone material is more suitable for implementing a sensor.

A Cost-Effective Myoelectric Prosthetic Hand

ABSTRACT Introduction A cost-effective myoelectric prosthetic hand for persons with wrist amputations is a need for developing countries. Instead of trying to develop manipulation of all the fingers, which would be complex, expensive, and demanding on battery power, we took an approach to make the design simple but moderately usable. Methods and Materials Because a sizable number of tasks can be done by controlling the thumb only, we anticipate that it would be an efficient design where the user will control only the thumb using electromyographic (EMG) signals. The shape of the hand is such that it will let the user hold small objects, such as a pen. A mannequin's hand was used as the structure, and after specific modification, the necessary circuitry was set up. Surface electrodes were used to acquire the EMG signals, which were then amplified using a bioelectric amplifier specifically designed and developed for this purpose. A microcontroller-based interface detects EMG and controls the DC gear motor that moves the thumb. Results The thumb of the developed unit responded to contraction of the arm muscles as desired. The user can control the thumb using his or her EMG signal and write holding a pen. A box-and-blocks test was conducted in which, on average, the user was able to move 6.6 blocks per minute. Conclusions The design is moderately usable but needs some minor improvements before practical use.

Development of a Parent Wireless Assistive Interface for Myoelectric Prosthetic Hands for Children.

In this study, a one-degree-of-freedom myoelectric prosthesis system was proposed using a Parent Wireless Assistive Interface (PWAI) that allowed an external assistant (e. g., the parent of the user) to immediately adjust the parameters of the prosthetic hand controller. In the PWAI, the myoelectric potential of use of the upper limb was plotted on an external terminal in real time. Simultaneously, the assistant adjusted the parameters of the prosthetic hand control device and manually manipulated the prosthetic hand. With these functions, children that have difficulty verbally communicating could obtain properly adjusted prosthetic hands. In addition, non-experts could easily adjust and manually manipulate the prosthesis; therefore, training for the prosthetic hands could be performed at home. Two types of hand motion discrimination methods were constructed in this study of the myoelectric control system: (1) a threshold control based on the myoelectric potential amplitude information and (2) a pattern recognition of the frequency domain features. In an evaluation test of the prosthesis threshold control system, child subjects achieved discrimination rates as high as 89%, compared with 96% achieved by adult subjects. Furthermore, the high discrimination rate was maintained by sequentially updating the threshold value. In addition, a discrimination rate of 82% on average was obtained by recognizing three motions using the pattern recognition method.

Design of a Regression Model for Four Grasping Patterns and Three Grip Force Intensities of a Myoelectric Prosthetic Hand

Design of a Regression Model for Four Grasping Patterns and Three Grip Force Intensities of a Myoelectric Prosthetic Hand

A simple movement based control approach to ease the control of a myoelectric elbow prosthetics in transhumeral amputees

Common myoelectric control strategy remains complex to use and produce unnatural gestures since it does not allow simultaneous movements of several joints. This is especially an issue for the transhumeral amputees who can use an active elbow in addition to active prosthetic wrist and hand. Since most transhumeral amputees have a mobile residual limb, an interesting approach aims at utilizing this mobility to control the elbow. We thus developed a simple electronic device relying on an inertial measurement unit, that can be interfaced with existing commercial myoelectric elbows and which convert chosen displacements of the residual limb into control signals, offering an intuitive way of controlling an active elbow prosthesis whilst allowing a simultaneous myoelectric control of the wrist and hand. We performed an experiment with one transhumeral amputee wearing his myoelectric prosthetic (active elbow, wrist and hand) who was asked to grasp and transfer different objects several times while being recorded with a motion capture system. Theses task were performed twice: with a conventional full myoelectric control and with the proposed movement based control. Several metrics (position error, time, smoothness, along with body compensatory strategies) were used to quantify the performances obtained with these two modes. Analyses showed that the participant was able to perform the tasks in both condition, but that the movement based control approach allowed a reduction in task time and in exhibited body compensatory strategies, by offering an easier access to adjustments of the elbow posture compared to the full myoelectric control approach. This work illustrates the potential of using residual limb mobility in the control of prosthetics, to make it more natural and intuitive, and how this can have an indirect but positive impact on the whole body compensatory strategies exhibited by amputated subjects.

Improving Fine Control of Grasping Force during Hand-Object Interactions for a Soft Synergy-Inspired Myoelectric Prosthetic Hand.

The concept of postural synergies of the human hand has been shown to potentially reduce complexity in the neuromuscular control of grasping. By merging this concept with soft robotics approaches, a multi degrees of freedom soft-synergy prosthetic hand [SoftHand-Pro (SHP)] was created. The mechanical innovation of the SHP enables adaptive and robust functional grasps with simple and intuitive myoelectric control from only two surface electromyogram (sEMG) channels. However, the current myoelectric controller has very limited capability for fine control of grasp forces. We addressed this challenge by designing a hybrid-gain myoelectric controller that switches control gains based on the sensorimotor state of the SHP. This controller was tested against a conventional single-gain (SG) controller, as well as against native hand in able-bodied subjects. We used the following tasks to evaluate the performance of grasp force control: (1) pick and place objects with different size, weight, and fragility levels using power or precision grasp and (2) squeezing objects with different stiffness. Sensory feedback of the grasp forces was provided to the user through a non-invasive, mechanotactile haptic feedback device mounted on the upper arm. We demonstrated that the novel hybrid controller enabled superior task completion speed and fine force control over SG controller in object pick-and-place tasks. We also found that the performance of the hybrid controller qualitatively agrees with the performance of native human hands.

A system to promote diffusion of myoelectric prosthetic hands for trans-radial amputees provided by the Workers’ Compensation Insurance Law

A system to promote diffusion of myoelectric prosthetic hands for trans-radial amputees provided by the Workers’ Compensation Insurance Law

Real-Time Segmentation and Feature Extraction of Electromyography: Towards Control of a Prosthetic Hand

Surface electromyographic (sEMG) signals can be used as inputs to control a myoelectric prosthetic hand. This requires the discrimination of sEMG associated with different hand movements. The signals for key unilateral hand movements such as wrist extension, wrist flexion, and power grip are similar, making the classification and control of these hand movements challenging. This preliminary study explores the viability of classifying the sEMG signals of these hand movements in real-time, in order to control a software simulation of a prosthetic hand. sEMG data was recorded from two bipolar electrodes for offline classifier training purposes. A novel segmentation technique was used to separate the muscle contraction and rest periods of the sEMG time-series data. A time-frequency algorithm was then applied for the first time to extract sEMG-based features from the segmented data. Features were used to train four support vector machines offline, in a one-versus-all architecture. The classification system was tested offline and in real-time. The system yielded accuracies of 89.39% and 84.93% for offline and real-time testing respectively. Real-time classification was done in 10.2 ms when processing 0.5 s of input sEMG data. This shows that the system is accurate and computationally efficient, even with limited electrodes. Thus, it can serve as a foundation for further work in the implemented segmentation and feature extraction methods, for an inexpensive alternative to commercial myoelectric prosthetic devices.

Experimental Study of Real-Time Classification of 17 Voluntary Movements for Multi-Degree Myoelectric Prosthetic Hand

The myoelectric prosthetic hand is a powerful tool developed to help people with upper limb loss restore the functions of a biological hand. Recognizing multiple hand motions from only a few electromyography (EMG) sensors is one of the requirements for the development of prosthetic hands with high level of usability. This task is highly challenging because both classification rate and misclassification rate worsen with additional hand motions. This paper presents a signal processing technique that uses spectral features and an artificial neural network to classify 17 voluntary movements from EMG signals. The main highlight will be on the use of a small set of low-cost EMG sensor for classification of a reasonably large number of hand movements. The aim of this work is to extend the capabilities to recognize and produce multiple movements beyond what is currently feasible. This work will also show and discuss about how tailoring the number of hand motions for a specific task can help develop a more reliable prosthetic hand system. Online classification experiments have been conducted on seven male and five female participants to evaluate the validity of the proposed method. The proposed algorithm achieves an overall correct classification rate of up to 83%, thus, demonstrating the potential to classify 17 movements from 6 EMG sensors. Furthermore, classifying 9 motions using this method could achieve an accuracy of up to 92%. These results show that if the prosthetic hand is intended for a specific task, limiting the number of motions can significantly increase the performance and usability.

Assessment of multigrip prosthetic hand by a crossover longitudinal study

The major function of the hand that the upper limb prosthesis tries to replicate is grasp. New myoelectric devices with multiarticulated hands are available and are reimbursed by French health care system. These devices propose several types of grip, which are getting closer to human hand movements. Few studies evaluated the impact of these devices on prehensile function. It is important for the rehabilitation team to know how upper limb amputees manage these devices in their daily lives to improve rehabilitation. The aim of this study was to evaluate the functional use of these new myoelectric prosthetic hand (NMPH) compared to tridigital myoelectric hand (TDMH). Eight unilateral transradial upper limb amputees (7 male, 1 female, age 39.5 ± 14 years old) wearing usually a myoelectric prosthesis participated in this prospective crossover study. Functional assessment was realized by each participant with their own tridigital myoelectric prosthesis and a multigrip prosthesis after specific rehabilitation and one month of home use. Self-report questionnaires included Quick DASH, Orthotics and Prosthetics Users Survey (OPUS) and the French version of the Quebec Users Evaluation Satisfaction with Assistive Technology (QUEST). Clinical tests included Box and Blocks Test (BBT) in sitting position, Southampton Hand Assessment Procedure (SHAP) and the third phase of Hand 400 points. These values were compared for each participant between both myoelectric prosthesis. There is no significant difference between both devices for manual dexterity (BBT), gripping capabilities (SHAP) and grasping capabilities, as assessed by the third phase of Hand 400 points which also visually evaluates shoulder and trunk compensations during the movement of the objects, and technology satisfaction (QUEST). In this study, the use of these new multiarticulated prosthetic hands did not influence the gripping capabilities at 1 month of home use. In the literature, an increased manual dexterity was found at 3 months of home use (Luchetti et al., JRRD 2015). However, these new devices providing several types of grip, may be of interest in decreasing shoulder or trunk compensations during grasp in upper limb amputees. Further studies are mandatory to objectively evaluate these compensations using optoelectronic systems and could be used for assessment of rehabilitation programs.

Influence of end-effector on the risk of shoulder pathologies after forearm amputation: Use of the active shoulder abduction measurement

After upper limb amputation, the use of prosthesis usually causes compensation movements for trunk and stump. These compensations can lead to musculoskeletal pain or overuse syndromes. In the context of occupational pathologies, stress levels were defined as at risk for shoulder concerning abduction amplitude (60°) and duration of exposure (2/24 h or 8.5% of the time beyond 60°). The objective of this study is to evaluate the risk of shoulder complications in a population of below-elbow amputees when using different myoelectric prosthetic end-effectors. Eight below-elbow amputees fitted with tridigital myoelectric prosthetic hand potential users of non-morphometric end-effectors in professional or leisure time are evaluated. Shoulder abduction (mean abduction and percentage of time spent beyond 60° of abduction) is measured during a Box and Block Test in a sitting position with a VICON optoelectronic system. The measurements are performed for amputated limb in 2 or 3 conditions: non-morphometric end-effector with or without wrist flexion-extension ± with tridigital myoelectric prosthetic hand (for 3 amputees) and for healthy limb. There is no significant difference in dexterity between end-effectors but significant difference between all end-effectors and healthy limb. The mean shoulder abduction is greater than 60° and the median percentage of time beyond 60° abduction is greater than 8.5% only when using a non-morphometric end-effector without wrist flexion-extension. Moreover, there is a significant difference between the 2 limbs for the mean abduction only with the non-morphometric effector without wrist flexion-extension. There are no differences between the other end-effectors and healthy limb, nor between the end-effectors between them. Prosthetic grip and mobility capacity of prosthetic wrist also influence shoulder kinematics. For some amputees, shoulder abduction is not at risk for musculoskeletal disorders but other compensation movements (internal shoulder rotation, trunk torso…) remain to be studied. The quantification of stresses on residual limb and trunk related to these compensations could allow to detect risk of musculoskeletal complications. A personalized analysis of compensations during common task could guide the choice of prosthetic end-effector and its adjustment and allow to quantify the influence of rehabilitation techniques on these compensations.

A virtual myoelectric prosthesis training system capable of providing instructions on hand operations

This article proposes a virtual hand and a virtual training system for controlling the MyoBock—the most commonly used myoelectric prosthetic hand worldwide. As the virtual hand is controlled using the method also adopted for the MyoBock hand, the proposed system provides upper-limb amputees with operation sensibilities similar to those experienced in MyoBock control. It can also display an additional virtual hand for the provision of instructions on hand operation, such as the recommended posture for object grasping and the trajectory desirable to reach a target. In virtual hand control experiments conducted with an amputee to evaluate the proposed virtual hand’s operability, the subject successfully performed stable opening and closing with high discrimination rates (89.3±6.65%), thanks to the virtual hand’s incorporation of the MyoBock’s operational characteristics. A training experiment using the proposed system was also conducted with eight healthy participants over a period of 5 days. The participants were asked to perform the box and block test using the MyoBock hand in a real environment on the first and final days. The results showed that the number of blocks transported in 1 min significantly increased and that the participants using the instruction virtual hand changed the orientation of the hand approaching blocks from vertical to lateral. The outcomes of the experiment indicate that the proposed system can be used to improve MyoBock hand control operation both quantitatively and qualitatively.

Toward an Enhanced Human–Machine Interface for Upper-Limb Prosthesis Control With Combined EMG and NIRS Signals

Advanced myoelectric prosthetic hands are currently limited due to the lack of sufficient signal sources on amputation residual muscles and inadequate real-time control performance. This paper presents a novel human–machine interface for prosthetic manipulation that combines the advantages of surface electromyography (EMG) and near-infrared spectroscopy (NIRS) to overcome the limitations of myoelectric control. Experiments including 13 able-bodied and three amputee subjects were carried out to evaluate both offline classification accuracy (CA) and online performance of the forearm motion recognition system based on three types of sensors (EMG-only, NIRS-only, and hybrid EMG-NIRS). The experimental results showed that both the offline CA and real-time performance for controlling a virtual prosthetic hand were significantly ( p $ 0.05) improved by combining EMG and NIRS. These findings suggest that fusion of EMG and NIRS is feasible to improve the control of upper-limb prostheses, without increasing the number of sensor nodes or complexity of signal processing. The outcomes of this study have great potential to promote the development of dexterous prosthetic hands for transradial amputees.

Design of an Accessible, Powered Myoelectrically Controlled Hand Prosthesis

In this paper we describe accessible myoelectric prosthetic hand design based on modification of existing mechanical prosthesis and off-the-shelf parts and components. Despite significant advances in myoelectric prosthetics, such existing devices remain out of reach of the majority of the patients needing them due to high costs and complexity. We describe a simple design that can be assembled based on existing or readily acquirable parts at approximately 1/100 of the cheapest commercially available alternative. Our design offers wrist disarticulation patients in developing countries an affordable myoelectric prosthesis with significant capacity for improving their quality of life.

The influence of environment

Background:Prostheses are used to varying degrees; however, little is known about how environmental aspects influence this use.Objectives:To describe users’ experiences of how environmental factors influence their use of a myoelectric arm prosthesis.Study design:Qualitative and descriptive.Methods:A total of 13 patients previously provided with a myoelectric prosthetic hand participated. Their age, sex, deficiency level, etiology, current prosthesis use, and experience varied. Semi-structured interviews were audiotaped, transcribed, and analyzed through inductive content analysis.Results:Four categories were created from the data: “Prosthesis function,” “Other people’s attitudes,” “Support from family and healthcare,” and “Individual’s attitude and strategies.” The overarching theme, “Various degrees of embodiment lead to different experiences of environmental barriers and facilitators,” emerged from differences in individual responses depending on whether the individual was a daily or a non-daily prosthesis user. Environmental facilitators such as support from family and healthcare and good function and fit of the prosthesis seemed to help the embodiment of the prosthesis, leading to daily use. This embodiment seemed to reduce the influence of environmental barriers, for example, climate, attitudes, and technical shortcomings.Conclusion:Embodiment of prostheses seems to reduce the impact of environmental barriers. Support and training may facilitate the embodiment of myoelectric prosthesis use.Clinical relevanceFor successful prosthetic rehabilitation, environmental factors such as support and information to the patient and their social network about the benefits of prosthesis use are important. Local access to training in myoelectric control gives more people the opportunity to adapt to prosthesis use and experience less environmental barriers.

Deep learning-based artificial vision for grasp classification in myoelectric hands

Objective. Computer vision-based assistive technology solutions can revolutionise the quality of care for people with sensorimotor disorders. The goal of this work was to enable trans-radial amputees to use a simple, yet efficient, computer vision system to grasp and move common household objects with a two-channel myoelectric prosthetic hand. Approach. We developed a deep learning-based artificial vision system to augment the grasp functionality of a commercial prosthesis. Our main conceptual novelty is that we classify objects with regards to the grasp pattern without explicitly identifying them or measuring their dimensions. A convolutional neural network (CNN) structure was trained with images of over 500 graspable objects. For each object, 72 images, at intervals, were available. Objects were categorised into four grasp classes, namely: pinch, tripod, palmar wrist neutral and palmar wrist pronated. The CNN setting was first tuned and tested offline and then in realtime with objects or object views that were not included in the training set. Main results. The classification accuracy in the offline tests reached for the seen and for the novel objects; reflecting the generalisability of grasp classification. We then implemented the proposed framework in realtime on a standard laptop computer and achieved an overall score of in classifying a set of novel as well as seen but randomly-rotated objects. Finally, the system was tested with two trans-radial amputee volunteers controlling an i-limb UltraTM prosthetic hand and a motion controlTM prosthetic wrist; augmented with a webcam. After training, subjects successfully picked up and moved the target objects with an overall success of up to . In addition, we show that with training, subjects’ performance improved in terms of time required to accomplish a block of 24 trials despite a decreasing level of visual feedback. Significance. The proposed design constitutes a substantial conceptual improvement for the control of multi-functional prosthetic hands. We show for the first time that deep-learning based computer vision systems can enhance the grip functionality of myoelectric hands considerably.

Recognizing the Gradual Changes in sEMG Characteristics Based on Incremental Learning of Wavelet Neural Network Ensemble

Most myoelectric prosthetic hands use a fixed pattern recognition model to identify the user's hand motion commands. Since surface electromyogram (sEMG) characteristics vary with time, it is difficult to employ the fixed pattern recognition model in identifying hand motion commands stably for a long period of time. In order to adapt to the gradual changes in sEMG characteristics, we utilized incremental learning based on the wavelet neural network (WNN) ensemble, and used negative correlation learning (NCL) to train it. To verify the effect of the proposed method, a group of subjects executed six hand motions in a continual experiment for more than 2 h. Compared with the fixed pattern recognition model, the classification accuracy rate of incremental learning with nonintegration becomes substantially improved. In addition, the results of the WNN ensemble with the fixed-size mode are more stable than those of the WNN ensemble with the growth mode. The experimental results demonstrate that our method can recognize the gradual changes in sEMG characteristics stably. Using the proposed method, the average accuracy rate is found to be 92.17%, even after a long period of time. Moreover, since the update time is short, the proposed method can be successfully applied in myoelectric prosthetic hands.

Feedback device of temperature sensation for a myoelectric prosthetic hand

Feedback device of temperature sensation for a myoelectric prosthetic hand

SEMG Sensor Using Polypyrrole-Coated Nonwoven Fabric Sheet for Practical Control of Prosthetic Hand.

One of the greatest challenges of using a myoelectric prosthetic hand in daily life is to conveniently measure stable myoelectric signals. This study proposes a novel surface electromyography (sEMG) sensor using polypyrrole-coated nonwoven fabric sheet as electrodes (PPy electrodes) to allow people with disabilities to control prosthetic limbs. The PPy electrodes are sewn on an elastic band to guarantee close contact with the skin and thus reduce the contact electrical impedance between the electrodes and the skin. The sensor is highly customizable to fit the size and the shape of the stump so that people with disabilities can attach the sensor by themselves. The performance of the proposed sensor was investigated experimentally by comparing measurements of Ag/AgCl electrodes with electrolytic gel and the sEMG from the same muscle fibers. The high correlation coefficient (0.87) between the two types of sensors suggests the effectiveness of the proposed sensor. Another experiment of sEMG pattern recognition to control myoelectric prosthetic hands showed that the PPy electrodes are as effective as Ag/AgCl electrodes for measuring sEMG signals for practical myoelectric control. We also investigated the relation between the myoelectric signals' signal-to-noise ratio and the source impedances by simultaneously measuring the source impedances and the myoelectric signals with a switching circuit. The results showed that differences in both the norm and the phase of the source impedance greatly affect the common mode noise in the signal.