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Abstract Despite significant technological progress in prosthetic hands, a device with functionality akin to a biological extremity is far from realization. To better support the development of next-generation technologies, we investigated the grasping capabilities of clinically prescribable and commercially available (CPCA) prosthetic hands against those that are 3D-printed, which offer cost-effective and customizable solutions. Our investigation utilized the Anthropomorphic Hand Assessment Protocol (AHAP) as a benchtop evaluation of the multi-grasp performance of 3D-printed devices against CPCA prosthetic hands. Our comparison sample included three open-source 3D-printed prosthetic hands (HACKberry Hand, HANDi Hand, and BEAR PAW) and three CPCA prosthetic hands (Össur i-Limb Quantum, RSL Steeper BeBionic Hand V3, and Psyonic Ability Hand), along with including previously published AHAP data for four additional 3D-printed hands (Dextrus v2.0, IMMA, InMoov, and Limbitless). Our findings revealed a notable grasping performance disparity, with 3D-printed prostheses generally underperforming compared to their CPCA counterparts, specifically in cylindrical, diagonal volar, extension, and spherical grips. We propose that the observed performance shortfalls are likely attributed to the design or build quality of the 3D-printed prostheses, owing to the fact that 3D-printed hands often have a lower technology readiness level for widespread use. Addressing the limitations highlighted in this work and subsequent research will play a crucial role in refining the design and functionality of both 3D-printed and CPCA prosthetic devices.

Despite significant technological progress in prosthetic hands, a device with functionality akin to a biological extremity is far from realization. To better support the development of next-generation technologies, we investigated the grasping capabilities of clinically prescribable and commercially available (CPCA) prosthetic hands against those that are 3D-printed, which offer cost-effective and customizable solutions. Our investigation utilized the Anthropomorphic Hand Assessment Protocol (AHAP) as a benchtop evaluation of the multi-grasp performance of 3D-printed devices against CPCA prosthetic hands. Our comparison sample included three open-source 3D-printed prosthetic hands (HACKberry Hand, HANDi Hand, and BEAR PAW) and three CPCA prosthetic hands (Össur i-Limb Quantum, RSL Steeper BeBionic Hand V3, and Psyonic Ability Hand), along with including previously published AHAP data for four additional 3D-printed hands (Dextrus v2.0, IMMA, InMoov, and Limbitless). Our findings revealed a notable grasping performance disparity, with 3D-printed prostheses generally underperforming compared to their CPCA counterparts, specifically in cylindrical, diagonal volar, extension, and spherical grips. We propose that the observed performance shortfalls are likely attributed to the design or build quality of the 3D-printed prostheses, owing to the fact that 3D-printed hands often have a lower technology readiness level for widespread use. Addressing the limitations highlighted in this work and subsequent research will play a crucial role in refining the design and functionality of both 3D-printed and CPCA prosthetic devices. [ABSTRACT FROM AUTHOR]

Objective: Evaluation of the hand function affected when replacing a malfunctioning hand by a bionic hand. Design: Case report. Subjects: One individual that wished for a better quality of life after unsatisfying hand function following a replantation. Methods: A quantitative and qualitative evaluation of body functions as well as activity performance and participation before and after a planned amputation and prosthetic fitting is presented. Results: Improvements were seen in the patient-reported outcome measures (PROMs) that were used regarding activity (Disability of the Arm, Shoulder and Hand [DASH] and Canadian Occupational Performance Measure [COPM]), pain (Neuropathic Pain Symptom Inventory [NPSI], Brief Pain Inventory [BPI], Visual Analogue Scale [VAS]), cold intolerance (CISS) and health related quality of life (SF-36), as well as in the standardised grip function test, Southampton Hand Assessment Procedure (SHAP). No referred sensations were seen but the discriminative touch on the forearm was improved. In the qualitative interview, a relief of pain, a lack of cold intolerance, improved appearance, better grip function and overall emotional wellbeing were expressed. Conclusions: The planned amputation and subsequent fitting and usage of a hand prosthesis were satisfying for the individual with positive effects on activity and participation. Clinical relevance: When the hand function after a hand replantation does not reach satisfactory levels, a planned amputation and a prosthetic hand can be the right solution.

Introduction: Muscular activation sequences have been shown to be suitable time-domain features for classification of motion gestures. However, their clinical application in myoelectric prosthesis control was never investigated so far. The aim of the paper is to evaluate the robustness of these features extracted from the EMG signal in transient state, on the forearm, for classifying common hand tasks. Methods: The signal associated to four hand gestures and the rest condition were acquired from ten healthy people and two persons with trans-radial amputation. A feature extraction algorithm allowed for encoding the EMG signals into muscular activation sequences, which were used to train four commonly used classifiers, namely Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Non-linear Logistic Regression (NLR) and Artificial Neural Network (ANN). The offline performances were assessed with the entire sample of recruited people. The online performances were assessed with the amputee subjects. Moreover, a comparison of the proposed method with approaches based on the signal envelope in the transient state and in the steady state was conducted. Results: The highest performance were obtained with the NLR classifier. Using the sequences, the offline classification accuracy was higher than 93% for healthy and amputee subjects and always higher than the approach with the signal envelope in transient state. As regards the comparison with the steady state, the performances obtained with the proposed method are slightly lower (<4%), but the classification occurred at least 200 ms earlier. In the online application, the motion completion rate reached up to 85% of the total classification attempts, with a motion selection time that never exceeded 218 ms. Discussion: Muscular activation sequences are suitable alternatives to the time-domain features commonly used in classification problems belonging to the sole EMG transient state and could be potentially exploited in control strategies of myoelectric prosthesis hands. [ABSTRACT FROM AUTHOR]

There are various studies on the structural and functional constructions of hand prostheses inspired by human biomechanics, and there are different kinds of prostheses available on the market. This paper aims to present the relevant stages of designing a hand prosthesis prototype that is innovative due to its mechanical structure and, therefore, the prosthesis fingers' DOF and mobility. The prosthesis is designed to have independent finger motion with the rotations of each of the three phalanges and, most importantly, rotation for each of the fingers relative to the palm. All these motions are generated and controlled by micromotors, a microcontroller, and sensors. A reverse engineering technique was applied for obtaining the exterior surface dimensions of the prosthesis and this consequently ensures that this prosthesis looks as realistic as possible. Small, light mechanical parts were designed as components of the mechanical system for the motions of finger phalanges and most of them (gears, levers, shells) were made using 3D-printing technologies (digital light processing (DLP) and/or selective laser sintering (SLS)). Aspects of some technical problems which arose during the prototype assembly are also recorded in the paper. Further research development will focus on the tests conducted on the prosthesis and the consequent adjustments of the prototype. [ABSTRACT FROM AUTHOR]

Abstract Background Machine-learning-based myocontrol of prosthetic devices suffers from a high rate of abandonment due to dissatisfaction with the training procedure and with the reliability of day-to-day control. Incremental myocontrol is a promising approach as it allows on-demand updating of the system, thus enforcing continuous interaction with the user. Nevertheless, a long-term study assessing the efficacy of incremental myocontrol is still missing, partially due to the lack of an adequate tool to do so. In this work we close this gap and report about a person with upper-limb absence who learned to control a dexterous hand prosthesis using incremental myocontrol through a novel functional assessment protocol called SATMC (Simultaneous Assessment and Training of Myoelectric Control). Methods The participant was fitted with a custom-made prosthetic setup with a controller based on Ridge Regression with Random Fourier Features (RR-RFF), a non-linear, incremental machine learning method, used to build and progressively update the myocontrol system. During a 13-month user study, the participant performed increasingly complex daily-living tasks, requiring fine bimanual coordination and manipulation with a multi-fingered hand prosthesis, in a realistic laboratory setup. The SATMC was used both to compose the tasks and continually assess the participant’s progress. Patient satisfaction was measured using Visual Analog Scales. Results Over the course of the study, the participant progressively improved his performance both objectively, e.g., the time required to complete each task became shorter, and subjectively, meaning that his satisfaction improved. The SATMC actively supported the improvement of the participant by progressively increasing the difficulty of the tasks in a structured way. In combination with the incremental RR-RFF allowing for small adjustments when required, the participant was capable of reliably using four actions of the prosthetic hand to perform all required tasks at the end of the study. Conclusions Incremental myocontrol enabled an upper-limb amputee to reliably control a dexterous hand prosthesis while providing a subjectively satisfactory experience. The SATMC can be an effective tool to this aim.

The adoption of prosthetic hands by children with congenital upper limb deficiency from a very early age has benefits in terms of reducing prosthesis abandonment, improving body image, and mitigating developmental and physiological complications that arise from the underuse of the deficit arm. However, current hand prostheses present drawbacks that make them not suitable for very young children. This study investigates the design requirements of a prosthesis for very young children with congenital below-elbow deficiency using a user-centered design (UCD) approach. The UCD puts the child at the center of the design process, with parents as mediators between the child and the design team. The study was conducted with an interdisciplinary team over three years, with several prototypes developed for different growth stages of the child. This paper presents the adopted UCD approach and its timeline, the iterative prototypes of the prosthesis developed throughout the study, and the final prototype with a focus on its child-friendly features. Finally, the paper describes the design requirements concluded from the study and the authors’ recommendations for implementing prosthetic hands in below-elbow early intervention.

For transradial amputees, especially those with insufficient residual muscle activity, it is challenging to quickly obtain an appropriate grasping pattern for a multigrasp prosthesis. To address this problem, this study proposed a fingertip proximity sensor and a grasping pattern prediction method base on it. Rather than exclusively utilizing the EMG of the subject for the grasping pattern recognition, the proposed method used fingertip proximity sensing to predict the appropriate grasping pattern automatically. We established a five-fingertip proximity training dataset for five common classes of grasping patterns (spherical grip, cylindrical grip, tripod pinch, lateral pinch, and hook). A neural network-based classifier was proposed and got a high accuracy (96%) within the training dataset. We assessed the combined EMG/proximity-based method (PS-EMG) on six non-disabled subjects and one transradial amputee subject while performing the “reach-and-pick up” tasks for novel objects. The assessments compared the performance of this method with the typical pure EMG methods. Results indicated that non-disabled subjects could reach the object and initiate prosthesis grasping with the desired grasping pattern on average within 1.93 s and complete the tasks 7.30% faster on average with the PS-EMG method, relative to the pattern recognition-based EMG method. And the amputee subject was, on average, 25.58% faster in completing tasks with the proposed PS-EMG method relative to the switch-based EMG method. The results showed that the proposed method allowed the user to obtain the desired grasping pattern quickly and reduced the requirement for EMG sources.

For transradial amputees, especially those with insufficient residual muscle activity, it is challenging to quickly obtain an appropriate grasping pattern for a multigrasp prosthesis. To address this problem, this study proposed a fingertip proximity sensor and a grasping pattern prediction method base on it. Rather than exclusively utilizing the EMG of the subject for the grasping pattern recognition, the proposed method used fingertip proximity sensing to predict the appropriate grasping pattern automatically. We established a five-fingertip proximity training dataset for five common classes of grasping patterns (spherical grip, cylindrical grip, tripod pinch, lateral pinch, and hook). A neural network-based classifier was proposed and got a high accuracy (96%) within the training dataset. We assessed the combined EMG/proximity-based method (PS-EMG) on six non-disabled subjects and one transradial amputee subject while performing the “reach-and-pick up” tasks for novel objects. The assessments compared the performance of this method with the typical pure EMG methods. Results indicated that non-disabled subjects could reach the object and initiate prosthesis grasping with the desired grasping pattern on average within 1.93 s and complete the tasks 7.30% faster on average with the PS-EMG method, relative to the pattern recognition-based EMG method. And the amputee subject was, on average, 25.58% faster in completing tasks with the proposed PS-EMG method relative to the switch-based EMG method. The results showed that the proposed method allowed the user to obtain the desired grasping pattern quickly and reduced the requirement for EMG sources. [ABSTRACT FROM AUTHOR]

This study reports the preliminary results of a technique for redistributing muscles at the wrist in the stump of hand amputees by suturing the tendons to the dermis. The technique has the potential to improve control of hand prostheses by detecting movement intentions. [ABSTRACT FROM AUTHOR]

Background: Machine-learning-based myocontrol of prosthetic devices suffers from a high rate of abandonment due to dissatisfaction with the training procedure and with the reliability of day-to-day control. Incremental myocontrol is a promising approach as it allows on-demand updating of the system, thus enforcing continuous interaction with the user. Nevertheless, a long-term study assessing the efficacy of incremental myocontrol is still missing, partially due to the lack of an adequate tool to do so. In this work we close this gap and report about a person with upper-limb absence who learned to control a dexterous hand prosthesis using incremental myocontrol through a novel functional assessment protocol called SATMC (Simultaneous Assessment and Training of Myoelectric Control). Methods: The participant was fitted with a custom-made prosthetic setup with a controller based on Ridge Regression with Random Fourier Features (RR-RFF), a non-linear, incremental machine learning method, used to build and progressively update the myocontrol system. During a 13-month user study, the participant performed increasingly complex daily-living tasks, requiring fine bimanual coordination and manipulation with a multi-fingered hand prosthesis, in a realistic laboratory setup. The SATMC was used both to compose the tasks and continually assess the participant's progress. Patient satisfaction was measured using Visual Analog Scales. Results: Over the course of the study, the participant progressively improved his performance both objectively, e.g., the time required to complete each task became shorter, and subjectively, meaning that his satisfaction improved. The SATMC actively supported the improvement of the participant by progressively increasing the difficulty of the tasks in a structured way. In combination with the incremental RR-RFF allowing for small adjustments when required, the participant was capable of reliably using four actions of the prosthetic hand to perform all required tasks at the end of the study. Conclusions: Incremental myocontrol enabled an upper-limb amputee to reliably control a dexterous hand prosthesis while providing a subjectively satisfactory experience. The SATMC can be an effective tool to this aim. [ABSTRACT FROM AUTHOR]

Soft robotics have broken the rigid wall of interaction between humans and robots due to their own definition and manufacturing principles, allowing robotic systems to adapt to humans and enhance or restore their capabilities. In this research we propose a dexterous bioinspired soft active hand prosthesis based in the skeletal architecture of the human hand. The design includes the imitation of the musculoskeletal components and morphology of the human hand, allowing the prosthesis to emulate the biomechanical properties of the hand, which results in better grips and a natural design. CAD models for each of the bones were developed and 3D printing was used to manufacture the skeletal structure of the prosthesis, also soft materials were used for the musculoskeletal components. A myoelectric control system was developed using a recurrent neural network (RNN) to classify the hand gestures using electromyography signals; the RNN model achieved an accuracy of 87% during real time testing. Objects with different size, texture and shape were tested to validate the grasping performance of the prosthesis, showing good adaptability, soft grasping and mechanical compliance to object of the daily life. [ABSTRACT FROM AUTHOR]

There are various studies on the structural and functional constructions of hand prostheses inspired by human biomechanics, and there are different kinds of prostheses available on the market. This paper aims to present the relevant stages of designing a hand prosthesis prototype that is innovative due to its mechanical structure and, therefore, the prosthesis fingers’ DOF and mobility. The prosthesis is designed to have independent finger motion with the rotations of each of the three phalanges and, most importantly, rotation for each of the fingers relative to the palm. All these motions are generated and controlled by micromotors, a microcontroller, and sensors. A reverse engineering technique was applied for obtaining the exterior surface dimensions of the prosthesis and this consequently ensures that this prosthesis looks as realistic as possible. Small, light mechanical parts were designed as components of the mechanical system for the motions of finger phalanges and most of them (gears, levers, shells) were made using 3D-printing technologies (digital light processing (DLP) and/or selective laser sintering (SLS)). Aspects of some technical problems which arose during the prototype assembly are also recorded in the paper. Further research development will focus on the tests conducted on the prosthesis and the consequent adjustments of the prototype.

This study examines the possibility of implementing intelligent artificial limbs for patients after injuries or amputations. Brain-computer technology allows signals to be acquired and sent between the brain and an external device. Upper limb prostheses, however, are quite a complicated tool, because the hand itself has a very complex structure and consists of several joints. The most complicated joint is undoubtedly the saddle joint, which is located at the base of the thumb. You need to demonstrate adequate anatomical knowledge to construct a prosthesis that will be easy to use and resemble a human hand as much as possible. It is also important to create the right control system with the right software that will easily work together with the brain-computer interface. Therefore, the proposed solution in this work consists of three parts, which are: the Emotiv EPOC + Neuroheadsets, a control system made of a servo and an Arduino UNO board (with dedicated software), and a hand prosthesis model made in the three-dimensional graphic program Blender and printed using a 3D printer. Such a hand prosthesis controlled by a signal from the brain could help people with disabilities after amputations and people who have damaged innervation at the stump site. [ABSTRACT FROM AUTHOR]

The popularization of 3D-printing has allowed enhancing affordable prostheses for persons with amputations in developing countries, yet manufacturers are not subjected to any control from any medical regulatory authority. Adopted evaluation protocols seem to cherish optimistic expectations. A reduced performance test, derived from the Southampton Hand Assessment Procedure and two bench tests, to evaluate the mechanical advantage in the fingers and the slip resistance, are proposed to assess affordable tendon-driven devices. Ultimately, five models amongst those most commonly found in the scientific literature and the Internet have been evaluated. Three subjects participated with the aid of an able-bodied adaptor. The reduced test of performance provides consistent results but with a more direct interpretation of the failed patterns of prehension. All these models create far more expectations than the results deliver. With the supplementary material provided, an affordable benchmarking can be established with this reduced performance test and the two bench tests. They can lead to improved designs, prescriptions and regulations.

Recently, myoelectric hand prostheses produced by the combination of 3D-CAD and printer have gained attention. 3D printing of hand prosthesis has resulted in cost reduction. However, when an electric actuator with reduction gears is used as the driving source of the hand prosthesis, the joint rigidity becomes high; therefore, compliance control is required to grasp soft target objects. In this study, we propose a pneumatically driven hand prosthesis using a flexible bellows actuator. The hand prosthesis is lightweight and inexpensive because it is self-powered and generates compressed air through the supination motion of the user's forearm instead of an external compressor, which is essential for conventional pneumatic systems. Stable flexible grasping of the target object was achieved by driving a five-finger hand using this system. [ABSTRACT FROM AUTHOR]

Swinging-generating high impact by maximizing the speed of motion-is essential in sports activities. However, designing prosthetic hands suitable for swinging is still a challenge. Herein, in this article, we propose a swing-dedicated prosthetic hand that adopts the anatomical features of humans for an efficient swing. Our design, inspired by the joint arthrokinematics and tendon routing of fingers, enables a diagonal power “squeeze” grip robust to impact while increasing the reach of the clubhead. The swing speed is further increased by radio/ulnar deviation of the wrist and its nonlinear stiffness change achieved by the passive clutch mechanism. We evaluated our prosthetic hand through golf and found that it sustained high impact with diagonal grip, and clubhead speed increased by 19% at 90 r/min, with the radio/ulnar deviation nonlinearly correlated with arm angle. Our prosthetic hand design will contribute to improving amputees’ quality of life by allowing them to participate in sports activities. [ABSTRACT FROM AUTHOR]

This study presents a new approach for an sEMG hand prosthesis based on a 3D printed model with a fully embedded computer vision (CV) system in a hybrid version. A modified 5-layer Smaller Visual Geometry Group (VGG) convolutional neural network (CNN), running on a Raspberry Pi 3 microcomputer connected to a webcam, recognizes the shape of daily use objects, and defines the pattern of the prosthetic grasp/gesture among five classes: Palmar Neutral, Palmar Pronated, Tripod Pinch, Key Grasp, and Index Finger Extension. Using the Myoware board and a finite state machine, the user's intention, depicted by a myoelectric signal, starts the process, photographing the object, proceeding to the grasp/gesture classification, and commands the prosthetic motors to execute the movements. Keras software was used as an application programming interface and TensorFlow as numerical computing software. The proposed system obtained 99% accuracy, 97% sensitivity, and 99% specificity, showing that the CV system is a promising technology to assist the definition of the grasp pattern in prosthetic devices.

This study presents a new approach for an sEMG hand prosthesis based on a 3D printed model with a fully embedded computer vision (CV) system in a hybrid version. A modified 5-layer Smaller Visual Geometry Group (VGG) convolutional neural network (CNN), running on a Raspberry Pi 3 microcomputer connected to a webcam, recognizes the shape of daily use objects, and defines the pattern of the prosthetic grasp/gesture among five classes: Palmar Neutral, Palmar Pronated, Tripod Pinch, Key Grasp, and Index Finger Extension. Using the Myoware board and a finite state machine, the user's intention, depicted by a myoelectric signal, starts the process, photographing the object, proceeding to the grasp/gesture classification, and commands the prosthetic motors to execute the movements. Keras software was used as an application programming interface and TensorFlow as numerical computing software. The proposed system obtained 99% accuracy, 97% sensitivity, and 99% specificity, showing that the CV system is a promising technology to assist the definition of the grasp pattern in prosthetic devices. [ABSTRACT FROM AUTHOR]

In this study, the static, kinematic and dynamic behaviors of a semi-active hand prosthesis were analyzed numerically. Finite Elements method was used in static analysis and analytical method was used in kinematic and dynamic analysis. The mathematical model of the hand was created in kinematic and dynamic analysis. Using the mathematical model obtained, torque values of 0, 15, 30, 45, 60, 75, 90 degrees were calculated according to the different position angles of the fingers. Examination was performed for 4 fingers (index finger, middle finger, ring finger, little finger) and 5 kg of force was applied to the fingertips perpendicular to the finger plane. In this examination, the forces are divided into 25% for the index finger, 35% for the middle finger, 25% for the ring finger and 15% for the little finger. The results obtained for forces at different angles under the specified conditions were explained. As a result of all these stages, the prosthetic hand was designed. The design was calculated as linear statically by the finite element method. As a result of the study, a semi-active prosthetic hand was produced considering the calculation findings. Clear resin was used as material. [ABSTRACT FROM AUTHOR]

Abstract Objective We investigated for the first time the presence of chronic changes in the functional organization of sensorimotor brain areas induced by prolonged training with a bidirectional hand prosthesis. Methods A multimodal neurophysiological and neuroimaging evaluation of brain functional changes occurring during training in five consecutive amputees participating to experimental trials with robotic hands over a period of 10 years was carried out. In particular, modifications to the functional anatomy of sensorimotor brain areas under resting conditions were explored in order to check for eventual changes with respect to baseline. Results Full evidence is provided to demonstrate brain functional changes, and some of them in both the hemispheres and others restricted to the hemisphere contralateral to the amputation/prosthetic hand. Conclusions The study describes a unique experimental experience showing that brain reactions to the prolonged use of an artificial hand can be tracked for a tailored approach to a fully embedded artificial upper limb for future chronic uses in daily activities.

Purpose Develop a technology of manufacturing silicone sockets for functional hand prosthesis. Methods The technology of fabricating sockets for functional hand prosthesis made of high temperature vulcanization (HTV) silicone was developed and tested for the first time in the practice of domestic prosthetics, including the traditional stages of molding, positive reproduction, and manufacturing of sockets. However, there are technological peculiarities in each of the stages that enable to manufacture a comfortable individual full-contact socket for hand prosthesis made of silicone that was successfully used in active and functional prostheses and prostheses with external power in patients with congenital and amputation hand defects. Results The technology has a number of advantages over traditional ones, as it provides comfortable conditions for full contact and partial loading of the residual segment of a clubshaped hand stump in the liner socket of the prosthesis. These properties of the liner socket allow the production of the most skeletonized carrying socket made of composite material for reliable attachment of the components of functional hand prostheses to it. Conclusion Successful trials of the liner sockets of the hand prosthesis made from HTV silicone created the conditions for a wider application of this silicone brand in the manufacture of a new generation of sockets for upper and lower limb prosthesis.

A wearable sensor was fabricated to provide simultaneous electromyography (EMG) and force myography (FMG) information, from the same muscle location for controlling multi-functional hand prosthesis. The typical mechanical arrangement of the sensor constitutes silver electrodes and a force-sensitive resistor embedded inside the sensor chassis for detecting the electrical and mechanical activity of muscles. The sensor incorporates specific conditioning units for producing quantifiable EMG and FMG outputs from electrodes and force-sensitive resistor. The sensor exhibited excellent sensitivity and repeatability characteristics in recording force signals. The dry electrodes used in the sensor showed a comparable electrode-skin impedance with conventional Ag-AgCl electrodes. The EMG signals obtained with the sensor for ten subjects showed a good correlation and similar signal-to-noise ratio to signals with a traditional EMG sensor. Further, the nature of the measured EMG and FMG signals for the subjects by the sensor were analyzed by determining features such as signal stability and response time. The simultaneously recorded EMG-FMG signals from the sensor for different muscular contractions can be fused to generate a more reliable control for dexterous operation of hand prosthesis. [ABSTRACT FROM AUTHOR]

This paper presents a dual-channel, noninvasive force myography (FMG) sensor to extract muscle contraction information for controlling hand prostheses. The sensor was prepared using a pair of force-sensitive resistors (FSRs) mounted inside a rigid base for sensing the force exerted by contracting muscles through polydimethylsiloxane (PDMS) couplers. The device employs a dedicated signal conditioning circuitry for producing an output voltage proportional to the muscular contractile force. The static and dynamic characteristics of the sensor (i.e., sensitivity, drift, precision, hysteresis, and frequency response) were determined and analyzed using the recorded measurements to demonstrate its effectiveness. The frequency response of the designed sensor was sufficiently large to detect the rapidly varying FMG signals. The output assessment for the simultaneous acquisition of electromyography (EMG) and FMG from flexor muscles of subjects was performed using a two-tailed paired t-test, which showed a high correlation coefficient (r > 0.87) with a p-value less than 0.0001. Furthermore, a successful trial of the FMG sensor was made on five subjects to control a prosthetic hand in real-time, employing the proportional strategy. These experiments revealed that the designed sensor may provide an alternative to the EMG device. [ABSTRACT FROM AUTHOR]

A robust algorithm to classify various hand postures using EMG signals is needed for the EMG-based electric hand prosthesis with the multiple degrees of freedom. In this study, an armband-type multi-channel EMG module was designed, and an algorithm for classifying seven different types of hand postures was developed using the artificial neural network (ANN). The classification accuracy was evaluated for ten normal volunteers, according to the independence of the EMG feature groups, donning and doffing training data size, and whether or not majority voting was applied. The results revealed an optimized accuracy of 97.49 ± 3.87% when majority voting was applied after using high independence feature group (HIFG) to perform classification training for seven or more sessions. The algorithm was successfully applied to provide seven different hand postures in a 5-finger myoelectric hand prosthesis. Confusion matrices and separability indexes of ANN classifiers showed that the major misclassifications, in spite of a good accuracy, were found to be lateral pinch versus palmar pinch, and index versus thumb-up. However, with the classification training for seven or more sessions, the probability of misclassification significantly decreased. [ABSTRACT FROM AUTHOR]

Human rely profoundly on tactile feedback from fingertips to interact with the environment, whereas most hand prostheses used in clinics provide no tactile feedback. In this study we demonstrate the feasibility to use a tactile display glove that can be worn by a unilateral hand amputee on the remaining healthy hand to display tactile feedback from a hand prosthesis. The main benefit is that users could easily distinguish the feedback for each finger, even without training. The claimed advantage is supported by preliminary tests with healthy subjects. This approach may lead to the development of effective and affordable tactile display devices that provide tactile feedback for individual fingertip of hand prostheses.