Modular Prosthetic Limb Control by an Individual with Congenital Upper-Limb Amputation: A Case Report

The DEKA training protocol we developed includes training in skilled unilateral movements with the prosthesis. This approach represents a shift away from prosthetic rehabilitation programs that focus solely on use of the prosthesis as an assist. our view is that an advanced prosthesis, like the DEKA arm, should assume the role of a nondominant limb, which is capable of skilled activity, though generally less adept than the dominant limb. INTRODUCTION There is a scarcity of literature on rehabilitation training protocols for upper-limb prosthesis use. Seminal textbooks and articles describe protocols for training individuals to use conventional body-powered, myoelectric, or passive prosthetic devices and were written prior to the introduction of multifunction prosthetic hands and more advanced prosthetic technologies [1-3]. Furthermore, detailed descriptions of training protocols for individuals with shoulder disarticulations or forequarter amputations are particularly limited, with little emphasis on this level of amputation [4]. Two case studies described rehabilitation at this amputation level using cosmetic or body-powered prostheses with single degree of freedom (DOF) terminal devices [5-6]. In 2008, Smurr et al. published an updated training protocol for people with upper-limb amputation that highlighted the process of controls training for body-powered and myoelectric prosthesis users by level of amputation [7]. Stubblefield et al. presented an occupational therapy training protocol specific to patients undergoing targeted muscle reinnervation surgery [8]. Previously published prosthetic rehabilitation protocols do not address prosthetic training with the advanced prostheses that have become available and include multifunction hands, powered prostheses with multiple DOFs, and/or powered shoulder joints. The most widely tested advanced, multifunction prosthesis to date is the DEKA Arm, developed through the Defense Advanced Research Projects Agency's (DARPA's) Revolutionizing Prosthetics program. The DEKA Arm is now in its third generation prototype (Gen 3) and was recently approved by the Food and Drug Administration for commercial marketing. The DEKA Arm is modular and can be configured for three levels: radial configuration (RC) for people with transradial amputation, humeral configuration (HC) for people with transhumeral amputation, and shoulder configuration (SC) for people with transhumeral amputation with very short residuum and persons with amputations at the shoulder disarticulation and interscapulothoracic (forequarter) level. Features of the SC DEKA Arm with end-point control and foot controls were described in detail in three articles [9-11]. The SC Arm is shown in figure 1. Briefly, the SC Arm has 10 powered DOFs and additional passive DOFs. Prosthetic movements are customized for the user and can employ a combination of foot controls, myoelectric controls, pneumatic bladders (provided by DEKA), or other commonly available prosthetic input elements. All levels of the DEKA Arm use control inputs for the and wrist. The control scheme has dual modes enabling the user to switch between a hand mode of operation for fine motor use and an arm mode of operation to control larger gross movements of the prosthetic arm. Gross movements are operated through endpoint control, which allows simultaneous, coordinated movement of multiple joints to move the terminal device (the end point) to a desired position in space. The Department of Veterans Affairs (VA) has led a study to optimize the DEKA Arm (Optimization Study) since 2008 and is currently leading a study of home use of the device. A synopsis of the general training protocol used in the Optimization Study was presented in two articles [12-13]. Another article describing the virtual reality environment (VRE) portion of the training protocol for the DEKA Arm second generation prototype (Gen 2) reported that VRE training was particularly valuable for people with upper-limb amputation who must master a large number of controls and for those who need a structured learning environment [14]. …

To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocorticographic (ECoG) and depth electrodes within motor areas. The subjects performed the complex task in 71.4% (20/28) and 67.7% (21/31) of trials after minimal training. Balanced accuracy for detecting movements was 91.1% and 92.9%, significantly greater than chance accuracies (p < 0.05). After BMI-based initiation, the MPL completed the entire task 100% (one object) and 70% (three objects) of the time. The MPL took approximately 12.2 s for task completion after system improvements implemented for the second subject. Our hybrid-BMI design prevented all but one baseline false positive from initiating the system. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs.

In Colombia, socio-economic barriers disproportionately limit children and adolescents’ access to prosthetic devices. The functionality and potential everyday occupational uses of a low-cost, 3D-printed body-powered upper-limb prosthesis for children and adolescents with congenital limb absence are explored. A multiple-case study was employed within a mixed-methods exploratory sequential design. Four people who had received the prosthesis six weeks earlier, without prior training, were evaluated through direct observation, the Jebsen-Taylor Hand Function Test, and the University of New Brunswick Test of Prosthetic Function. While the prosthesis improved functional grasp patterns and bimanual activity participation, task execution times were prolonged, and spontaneous use was inconsistent. Interaction with the prosthesis was mainly in gross motor tasks. The need for optimized prosthetic design and structured occupational therapy interventions, including prescription, training, and follow-up, is highlighted. The importance of integrating rehabilitation strategies with assistive technology to enhance functional independence and occupational participation in pediatric prosthesis users is emphasized.

Introduction Upper-limb prostheses have undergone substantial technological improvements in the last two decades, but user complaints and abandonment rates have hardly changed. This may follow from the use of laboratory-based tests when assessing new technology instead of measuring prosthesis use in at-home situations. Serious games might be used to assess prosthesis use at home. Objectives The aim of this study was to assess the feasibility, validity, and reliability of using serious games to measure myocontrol in at-home situations. Study Design The design of this study is a cohort study with repeated measurements. Methods All 10 participants (five males; seven transradial and three transhumeral defects) used pattern recognition (PR) to control their prosthesis. Two serious games were included in the PR software: Simon Says and In-The-Zone. Participants were instructed to use their prosthesis as they normally would for 2 weeks with the additional instruction to play at least 18 serious games (±90 minutes in total). Outcome measures for aspects of prosthesis use were wear time, months of prosthesis use, and user experiences, and serious games outcomes were success rate and completion time. Results For feasibility, all but one participant completed the minimum of 18 games, and user experiences were mostly positive. For validity, no relationships were found between the aspects of prosthesis use and performance in the serious games. For reliability, test-retest reliability for success rate was high (intraclass correlation [ICC], 0.79), but low for completion time (ICC, 0.18) in both games. Conclusions Feasibility and reliability of using serious games as an assessment instrument for prosthesis control in at-home situations appeared to be good; however, validity could not be established. The serious games should be improved to increase validity. Furthermore, more outcome measures that reveal aspects of prosthesis use should be considered. Clinical Relevance Statement Assessing myocontrol in at-home situations by using serious games is feasible and reliable, but validity needs further attention.

In Mexico, transhumeral amputation occurs due to traumatic etiology and represents 4% of all the amputations. These amputations occur during an active period of life, which limits work options and increase user demotivation. It is known that pre-prosthetic and post-prosthetic rehabilitation provides the necessary tools to improve range of motion, increase muscle strength and restore the sense of control to perform activities of daily living. For this reason, virtual therapy environments have been developed for myoelectric prostheses, leaving aside body-powered prostheses. This paper presents the design and development of virtual training system for body-powered transhumeral prostheses in pre-prosthetic therapy. The created device has allowed to efficiently simulate the control of the body-powered transhumeral prosthesis for the user and provides a tool with specific routines of occupational therapy to the therapist. Thus, this provide the opportunity to increase the acceptance of a prosthesis at a higher level.

Thanks to the progress in the robotic and mechatronic fields, upper limb prostheses became more and more dexterous, getting closer to closing the gap between the natural limb and the medical device replacing it. However, the standard prosthesis controller spread on the market is still limited in the number of functions it can efficiently control and constitute a bottleneck in prosthesis use. Two leading solutions are currently under research to tackle this discrepancy: machine learning algorithms and autonomous controllers. Myo-controllers based on machine learning algorithms enable the user to control a high but still limited number of functions directly. On the other hand, autonomous controllers are based on a diversification of the sensor modalities to integrate the context and the user's intention in control and automatise part of the grasping process, relieving the user from the physical and potentially cognitive workload associated with it. This thesis focuses on the impact of the combination of these two solutions. Therefore, two studies investigated the gain of performance gain and the range of applicability of an association of a semi-autonomous system to a machine learning myo-controller for upper limb prostheses. This dissertation introduces first a method to preshape the prosthesis based on the prediction of the user’s intended grasping strategy. This method system supports the user in real-time by preshaping the prosthetic device's hand and wrist during a reaching phase of a prehensile action. This result is achieved by merging data from inertial measurement units, computer vision, and positions and pressure sensors to reproduce artificial proprioception, artificial exteroception, and short-term memory. The autonomous controller developed was designed to support the user in dynamic objects-crowded conditions. In a second phase, it has been assessed whether a semi-autonomous system associated with a machine learning myo-controller could improve the performance compared to the same machine learning controller alone. To assess this system, eight able-bodied and two amputee participants performed a newly developed test featuring a sequence of re-localisation tasks in a scene with multiple objects. The two amputee participants completed a standard rehabilitation test as well. The results revealed that the semi-autonomous system increased the time performance and reduced the physical effort for the total duration of the trial, and more specifically, in the preshaping phase of the task. The semi-autonomous system also reduced the need to control the prosthetic wrist manually. Eventually, the last part of this dissertation focuses on the interactions between the autonomous controller and the user. It investigates the impact, through the addition of artificial error on the autonomous system output, of different shared control modalities which define the application, according to the user’s commands, of the decisions of the autonomous controller on the prosthesis. Ten able-bodied participants performed a dual-task combining a reach-and-grasp task and an auditory reaction task. Time performance, physical and cognitive workload were recorded. This test implemented different control-sharing modalities at different levels of error. The results revealed that the shared control modalities significantly impact the task performance and the physical effort required to complete the task. The effect of the level of added errors on the three different outcomes varies between the control-sharing modalities. These results, therefore, provide valuable information to design and compare semi-autonomous upper limb prosthesis systems. In conclusion, by demonstrating the benefit of semi-autonomous systems and the investigation of their application conditions, this thesis advances the development of a new generation of prosthesis controllers that automatize parts of the actuation of the prosthesis. This research enlarges the current control bottleneck, which prevents prosthesis users from taking full advantage of their highly developed device. Therefore, it can contribute to increasing the support and the autonomy that upper limb prostheses can provide to their users in daily life.

The goal of this study was to investigate the use of cognitive modeling to assess the usability of an upper-limb prosthesis with a focus on mental workload responses. Prior studies have investigated usability of upper-limb prostheses with subjective surveys and physiological measures. However, these approaches have limitations, including subject recall of conditions and physiological response contamination by head and body movements and user speech during task performance as well as sensitivity to physical fatigue and room lighting conditions. Cognitive modeling was used to assess mental workload in use of transradial upper-limb prosthesis. A case study was conducted with a participant with upper-limb amputation using two different types of electromyography-based control schemes, including conventional direct control (DC) and pattern recognition (PR) control in order to compare cognitive model outcomes with mental workload assessment using eye-tracking measures. Cognitive models time estimates were also compared with actual task completion time results from the case study to further assess the validity of cognitive modeling as an analytical tool for evaluating upper limb prosthesis usability. Findings of both the cognitive models and case study revealed the PR mode to be more intuitive, reduce cognitive load, and increase efficiency in prosthetic control as compared to the DC mode. Results of the present study revealed that cognitive modeling can be used as an analytical approach for assessing upper-limb prosthetic device usability in terms of workload outcomes. Future studies should validate the present findings with more precise time estimations and a larger user sample size.

Surface Electromyography (EMG)-based pattern recognition methods have been investigated over the past years as a means of controlling upper limb prostheses. Despite the very good reported performance of myoelectric controlled prosthetic hands in lab conditions, real-time performance in everyday life conditions is not as robust and reliable, explaining the limited clinical use of pattern recognition control. The main reason behind the instability of myoelectric pattern recognition control is that EMG signals are non-stationary in real-life environments and present a lot of variability over time and across subjects, hence affecting the system's performance. This can be the result of one or many combined changes, such as muscle fatigue, electrode displacement, difference in arm posture, user adaptation on the device over time and inter-subject singularity. In this paper an extensive literature review is performed to present the causes of the drift of EMG signals, ways of detecting them and possible techniques to counteract for their effects in the application of upper limb prostheses. The suggested techniques are organized in a table that can be used to recognize possible problems in the clinical application of EMG-based pattern recognition methods for upper limb prosthesis applications and state-of-the-art methods to deal with such problems.

Prosthetic hands help patients gain greater confidence in daily life, and enabling automatic control of prosthetic hands using real biosignals is an essential task. This paper proposes a prosthetic hand control system based on surface electromyography (sEMG) signals. In particular, the sEMG signals are collected in real time corresponding to each hand gesture and pre-processed to eliminate noise components. A deep learning network is then employed to recognize hand gestures from the pre-processed sEMG signals. Recognized gestures are subsequently used to control the prosthetic hand to perform the corresponding movements. The proposed deep learning network achieves a recognition accuracy of 98.15 % for hand gestures from sEMG signals. Furthermore, the experimental results demonstrate that the proposed system can control the prosthetic hand with an accuracy of up to 96.97 % and a variance of 3.2194 across multiple subjects. These results suggest that the proposed system holds great potential for real-time prosthetic hand control based on sEMG signals, thereby supporting patients in gaining confidence in social interactions.

Advanced upper limb prosthetics, such as the Johns Hopkins Applied Physics Lab Modular Prosthetic Limb (MPL), are now available for research and preliminary clinical applications. Research attention has shifted to developing means of controlling these prostheses. Penetrating microelectrode arrays are often used in animal and human models to decode action potentials for cortical control. These arrays may suffer signal loss over the long-term and therefore should not be the only implant type investigated for chronic BMI use. Electrocorticographic (ECoG) signals from electrodes on the cortical surface may provide more stable long-term recordings. Several studies have demonstrated ECoG's potential for decoding cortical activity. As a result, clinical studies are investigating ECoG encoding of limb movement, as well as its use for interfacing with and controlling advanced prosthetic arms. This overview presents the technical state of the art in the use of ECoG in controlling prostheses. Technical limitations of the current approach and future directions are also presented.

In this paper, we discuss the potential contribution of affective touch to the user experience and robot performance in human-robot interaction, with an in-depth look into upper-limb prosthesis use as a well-suited example. Research on providing haptic feedback in human-robot interaction has worked to relay discriminative information during functional activities of daily living, like grasping a cup of tea. However, this approach neglects to recognize the affective information our bodies give and receive during social activities of daily living, like shaking hands. The discussion covers the emotional dimensions of affective touch and its role in conveying distinct emotions. In this work, we provide a human needs-centered approach to human-robot interaction design and argue for an equal emphasis to be placed on providing affective haptic feedback channels to meet the social tactile needs and interactions of human agents. We suggest incorporating affective touch to enhance user experience when interacting with and through semi-autonomous systems such as prosthetic limbs, particularly in fostering trust. Real-time analysis of trust as a dynamic phenomenon can pave the way towards adaptive shared autonomy strategies and consequently enhance the acceptance of prosthetic limbs. Here we highlight certain feasibility considerations, emphasizing practical designs and multi-sensory approaches for the effective implementation of affective touch interfaces.

Purpose: Upper limb prostheses are part of a rapidly changing market place. Despite development in device design, surveys report low levels of uptake and dissatisfaction with current prosthetic design. In this study, we present the results of a survey conducted with people with upper limb difference in Australia on their use of current prostheses and preferences in a prosthetic in order to inform future prosthetic hand design.Methods: An online survey was conducted on upper limb amputees, with 27 respondents that completed the survey. The survey was a mixture of open-ended questions, ranking design features and quantitative questions on problems experienced and desired attributes of future prosthesis designs.Results: Common key issues and concerns were isolated in the survey related to the weight, manipulation and dexterity, aesthetics, sensory feedback and financial cost; each of which could be addressed by additive manufacturing and soft robotics techniques.Conclusions: The adaptability of additive manufacturing and soft robotics to the highlighted concerns of participants shows that further research into these techniques is a feasible method to improve patient satisfaction and acceptance in prosthetic hands.Implications for rehabilitationEven with recent developments and advances in prosthetic design, the needs and desires of prosthetic users are not being met with current products.The desires and needs of those with upper limb difference are diverse.Using additive manufacturing to produce prosthetics allows for mass customization of prosthetics to meet these diverse needs while reducing costs.A soft robotic approach to prosthetics can help meet the desires of reducing weight and costs, while maintaining functionality.

ABSTRACTBACKGROUND:The aim of this study was to compare gait parameters, balance, weight-bearing symmetry, functional capacity, functional mobility, prosthesis satisfaction, and quality of life between individuals with diabetes mellitus-induced and traumatic transtibial amputations (TTAs).METHODS:Ten individuals with traumatic transtibial amputation and 10 individuals with diabetes mellitus-induced transtibial amputation were included in the study. All participants in both the trauma and diabetes groups used a vacuum-assisted suction suspension system (VASS) and a carbon composite foot transtibial prosthesis. Gait analysis and weight-bearing symmetry were assessed using a computerized gait analysis system. Balance was evaluated with the Biodex Balance System (BBS), functional capacity with the Six-Minute Walk Test (6MWT), functional mobility with the Timed Up and Go Test (TUG) and the Stair Climbing Test (SCT), prosthesis satisfaction with the Trinity Amputation and Prosthesis Experience Scale (TAPES), and quality of life with the Short Form-36 (SF-36).RESULTS:When comparing the traumatic and diabetic groups, significant differences favoring the trauma group were found in the following parameters: stride length (SL) (p=0.004), amputated limb step length (ASL) (p=0.019), non-affected limb step length (NSL) (p=0.005), balance assessment parameters of general postural stability (p=0.000), anteroposterior (A-P) postural stability (p=0.000), mediolateral (M-L) postural stability (p=0.007), SCT performance (p=0.000), and the activity restriction subsection of TAPES (p=0.029). No significant differences were observed in gait velocity, cadence, step width, weight-bearing percentage of the amputated and non-affected limbs, TUG performance, SF-36 scores, or the psychosocial adjustment, prosthesis satisfaction, and daily use time subsections of the TAPES.CONCLUSION:In this study, the use of a VASS prosthesis in both traumatic and diabetic amputees had a positive effect on outcomes in the diabetic group, resulting in comparable results to those of the traumatic group. The fact that diabetic amputees used their prostheses as frequently as traumatic amputees, remained active, and benefited from the choice of prosthesis and suspension system provides valuable insights for healthcare professionals as a facilitating factor in rehabilitation.

This paper presents a five-fingered underactuated prosthetic hand controlled by surface electromyographic (EMG) signals. The prosthetic hand control part is based on an EMG motion pattern classifier which combines variable learning rate (VLR) based neural network with parametric autoregressive (AR) model and wavelet transform. This motion pattern classifier can successfully identify flexion and extension of the thumb, the index finger and the middle finger, by measuring the surface EMG signals through three electrodes mounted on the flexor digitorum profundus, flexor pollicis longus and extensor digitorum. Furthermore, via continuously controlling single finger's motion, the five-fingered underactuated prosthetic hand can achieve more prehensile postures such as power grasp, centralized grip, fingertip grasp, cylindrical grasp, etc. The experimental results show that the classifier has a great potential application to the control of bionic man-machine systems because of its fast learning speed, high recognition capability

Human muscles can generate force and stiffness during contraction. When in contact with objects, human hands can achieve compliant grasping by adjusting the grasping force and the muscle stiffness based on the object's characteristics. To realize humanoid-compliant grasping, most prosthetic hands obtain the stiffness parameter of the compliant controller according to the environmental stiffness, which may be inconsistent with the amputee's intention. To address this issue, this paper proposes a compliant grasp control method for an underactuated prosthetic hand that can directly obtain the control signals for compliant grasping from surface electromyography (sEMG) signals. First, an estimation method of the grasping force is established based on the Huxley muscle model. Then, muscle stiffness is estimated based on the muscle contraction principle. Subsequently, a relationship between the muscle stiffness of the human hand and the stiffness parameters of the prosthetic hand controller is established based on fuzzy logic to realize compliant grasp control for the underactuated prosthetic hand. Experimental results indicate that the prosthetic hand can adjust the desired force and stiffness parameters of the impedance controller based on sEMG, achieving a quick and stable grasp as well as a slow and gentle grasp on different objects.