As both the proportion of older people and the length of life increases globally, a rise in age-related degenerative diseases, disability, and prolonged dependency is projected. However, more sophisticated biomedical materials, as well as an improved understanding of human disease, is forecast to revolutionize the diagnosis and treatment of conditions ranging from osteoarthritis to Alzheimer’s disease as well as impact disease prevention. Another, albeit quieter, revolution is also taking place within society: human augmentation. In this context, humans seek to improve themselves, metamorphosing through self-discipline or more recently, through use of emerging medical technologies, with the goal of transcending aging and mortality. In this review, and in the pursuit of improved medical care following aging, disease, disability, or injury, we first highlight cutting-edge and emerging materials-based neuroprosthetic technologies designed to restore limb or organ function. We highlight the potential for these technologies to be utilized to augment human performance beyond the range of natural performance. We discuss and explore the growing social movement of human augmentation and the idea that it is possible and desirable to use emerging technologies to push the boundaries of what it means to be a healthy human into the realm of superhuman performance and intelligence. This potential future capability is contrasted with limitations in the right-to-repair legislation, which may create challenges for patients. Now is the time for continued discussion of the ethical strategies for research, implementation, and long-term device sustainability or repair.

The evolution of prosthetic limbs continues to develop, with novel manufacturing techniques being evaluated, including additive manufacturing. Additive manufacturing (AM), or 3D-printing, holds promise for enabling personalized and tailored medical device options. The requirements for personalized medicine, coupled with the limitations of small-batch manufacturing, have made the technique viable for exploration. In this manuscript, an approach is presented for incorporating additive manufacturing for prostheses, both as a final part and in applications as an intermediate manufacturing step. As a result, through the use of these methods a multi-gesture capable electromyographic prosthesis was designed and manufactured, currently being evaluated in clinical trials for pediatric patients. This paper explored the results of this unique method of applying additive manufacturing techniques, and assessed how the blend of different manufacturing techniques improved performance and reduced device weight. Creating unique and aesthetic cosmetic coverings for the device was achieved through using additive manufacturing as an intermediate manufacturing component and, then, applying thermoforming. Cosmesis components saw a 33% reduction in weight from this change in manufacturing. The approach is explored to blend multiple manufacturing techniques to create cosmesis components and structural components for the prosthesis. The techniques serve the design intent to reduce reported challenges with upper limb prosthesis devices and to encourage device retention. Recommendations for manufacturing strategies are discussed, including the limitations.

A novel electromyography (EMG)-based wheelchair interface was developed that uses contractions from the temporalis muscle to control a wheelchair. To aid in the training process for users of this interface, a serious training game, Limbitless Journey, was developed to support patients. Amyotrophic Lateral Sclerosis (ALS) is a condition that causes progressive motor function loss, and while many people with ALS use wheelchairs as mobility devices, a traditional joystick-based wheelchair interface may become inaccessible as the condition progresses. Limbitless Journey simulates the wheelchair interface by utilizing the same temporalis muscle contractions for control of in-game movements, but in a low-stress learning environment. A usability study was conducted to evaluate the serious-game-based training platform. A major outcome of this study was qualitative data gathered through a concurrent think-aloud methodology. Three cohorts of five participants participated in the study. Audio recordings of participants using Limbitless Journey were transcribed, and a sentiment analysis was performed to evaluate user perspectives. The goal of the study was twofold: first, to perform a think-aloud usability study on the game; second, to determine whether accessible controls could be as effective as manual controls. The user comments were coded into the following categories: game environment, user interface interactions, and controller usability. The game environment category had the most positive comments, while the most negative comments were primarily related to usability challenges with the flexion-based controller. Interactions with the user interface were the main topic of feedback for improvement in future game versions. This game will be utilized in subsequent trials conducted at the facility to test its efficacy as a novel training system for the ALS population. The feedback collected will be implemented in future versions of the game to improve the training process.

Eye tracking has extensive applications for accessibility as it can be used in the control of wheelchairs, computer interfaces, gameplay, and more. For ALS patients and individuals with limited motor control, eye tracking may provide an avenue for both mobility and communication with others. While other accessibility technologies exist for ALS patients, interactions may be enhanced through the integration of eye tracking. New electromyography interfaces may have a high learning curve associated with adaptation to their technology. Usage of serious games as a form of training may reduce the time needed to acclimate while improving user engagement. This research seeks to validate the usability of eye tracking controls in Limbitless Journey, a serious video game designed to teach the user how to use a hands-free electromyographic-controlled wheelchair. Results of testing with non-ALS participants validated the eye tracking control interface, finding a non-significant difference in UI usability in comparison to self-controlled mouse and keyboard, as well as researcher-assisted mouse and keyboard. These findings signified that all cohorts experienced similar usability in the game. Eye tracking can then be thought to offer an opportunity to maintain independent gameplay for individuals with limited mobility.

Background: Pediatric electromyographic prostheses have limited accessibility due to factors including device weight, training challenges, and aesthetics. When prescribed, high rejection rates still remain. In an effort to reduce rejection, a video game training platform was developed to improve training outcomes. Utilizing this platform shifts training to a low stress virtual environment and can include structured and free-play modes. Due to the unique nature of the interface, validating the level’s effectiveness in reaching a training goal can be difficult by traditional observation methods. Level design and the challenges of learning the interface have pressed for new methods to validate the training prior to deployment. This research seeks to determine if machine learning agents can be used to validate design decisions in a training game for teaching children to utilize electromyographic prostheses, to ensure the game is a more accurate measure of participant abilities and to avoid negative training. Methods: This research explores integration of a customized AI training program to aid in improvement of game design and efficacy. The Program for Autonomous Unity Learning (PAUL) is a machine learning agent that uses reinforcement learning to optimize its path through a prescribed obstacle course by determining the best electromyographic biosensing input level and timing for each obstacle in the character’s path. Results: Training reward value begins approaching the end of its improvement at around 1 million steps. The average reward was approximately 140 out of a possible maximum score of 150 or approximately 96.67% of optimal play. Conclusion: PAUL can determine whether obstacles within the game are technically possible for completion by players. Through this method, the game becomes more accurate as a measure of participant evaluation. Future uses of PAUL include validation of other training games developed to ensure games are playable.

BACKGROUND Lack of functionality is one factor that contributes to prosthetic rejection rates. Electromyographic upper limb prostheses are controlled through muscle contractions in the user’s residual limb. The incorporation of multigesture controls into a novel, in-house developed upper limb prosthesis requires users to differentiate between the strengths of muscle contractions to trigger programmed gestures. Little research exists on the limitations of expanding device capabilities. This expansion may lead to a decline in accuracy and perceived usability or an increase in training time and cognitive workload. OBJECTIVE This study aimed to determine the feasibility of implementing multiple gestures when learning electromyographic controls during a single training session. METHODS Participants with full upper extremity control were fitted with a Flex Controller, a surface electromyography device that measures muscle contraction. Contractions were visualized as peaks and calibrated through an adjustable scale on a tablet. A training app was developed in-house to test novice users on an electromyography control system. Users interacted with 1, 3, or 5 zones on the screen. Each horizontal zone represented a threshold required to trigger a distinct gesture on the prosthesis. The cohorts were labeled A1 (n=9), A2 (n=10), A3 (n=9), and B1 (n=26). Every participant completed 3 trials per arm, and each trial consisted of 15 randomized cues. Each cue was represented by a green color change, with 1 point earned after a successful peak. Collected outcomes included performance, the System Usability Scale, and the National Aeronautics and Space Administration Task Load Index. RESULTS Scores decreased significantly as zones increased (Kruskal-Wallis H3=24.9, P<.001). The mean scores were 15.0 (SD 0.0) for 1 zone, 9.1 (SD 1.1) for 3 zones, and 5.5 (SD 1.1) for 5 zones. Perceived usability, measured by System Usability Scale, showed modest omnibus difference across cohorts (Kruskal-Wallis H3=5.22, P=.16); however, a pairwise comparison showed the 5-gesture cohort rated usability lower than the progressive cohort (2-tailed Welch t11=–2.19, P=.05). The 5-gesture cohort rated the system lowest (mean 63.3, SD 16.2). Cognitive workload, assessed through the National Aeronautics and Space Administration Task Load Index, increased with the number of gestures. The performance subscale showed a significant omnibus difference across cohorts (Kruskal-Wallis H3=21.4, P<.001). Mean performance subscale scores were 84.4 (SD 14.7) for the single-gesture condition, 30.6 (SD 21.7) for the 5-gesture condition, and 44.2 (SD 21.2) for the progressive cohort, reflecting increasing perceived difficulty with more gestures. The sample size for quantitative analysis was 54. CONCLUSIONS These findings support the implementation of progressive training for 3 gestures. Usability perceptions were the highest among the more complicated progressive cohort, which is likely related to perceived improvement. Progressively learning 3 gestures enables a balance between device capability, user intention, perceived usability, and cognitive workload.