Since 1972, the Institute of Biomedical Engineering at the University of New Brunswick has been hosting the MEC Symposium in Fredericton, New Brunswick, Canada. Since 2002, the Symposium occurs tri-annually, incorporating all aspects of powered upper limb prosthetics. The Symposium is immediately preceded by courses and workshops geared toward clinicians. The historical conference proceedings have been made available through the generosity of UNB and the Institute of Biomedical Engineering, through a partnership with Duke University and the Open Prosthetics Project, where a list of proceedings from previous Symposia can be found.
Institute of Biomedical Engineering, University of New Brunswick
Creating pressure and thermal tactile sensations in the phantom hand using non-invasive stimulation
Invasive peripheral nerve interfaces have demonstrated the value of restored touch perceptions in the missing hand elicited by electrical stimulation after arm amputation. However, invasive interfaces may not be the preferred option for many prosthesis users. We explored the use of non-invasive mechanical stimulation, targeted transcutaneous electrical nerve stimulation (tTENS), and thermal stimulation of naturally occurring reinnervated nerve sites in the residual limb to restore multiple modalities of touch in the phantom hand. In two individuals with arm amputation, we reported tactile sensations of pressure, elicited by mechanical stimulation and tTENS, and cooling, elicited by thermal stimulation, in the phantom hand. Tactile perceptions and stimulation locations remained stable over multiple years. We observed that the activated regions of the phantom hand may by stimulation modality specific, in that tactile sensations did not always overlap when different stimulation modalities were used at the same location on the residual limb. These results may be useful in helping restore a broad range of touch feedback for prosthesis users through non-invasive stimulation approaches.
Myo Control and Sensory Feedback Implementations
DESIGNED FOR ADDITIVE MANUFACTURING: UPPER LIMB PROSTHESES
To mitigate high levels of prosthesis abandonment for upper limb difference individuals, new manufacturing methods and techniques, including additive manufacturing (AM), and the principles of design for additive manufacturing (DfAM) can be employed to improve the weight, bulk, comfort, and heat dissipation of upper limb prostheses. This project demonstrates how the principles of DfAM can be applied to upper limb prostheses to address each one of these specific areas. This case study of the design, development, and production of a shoulder disarticulation prosthesis not only exemplifies the practical application of AM in creating more effective and user-friendly prosthetic devices but also highlights the importance of material innovation in solving complex challenges. As this technology continues to evolve, it holds the promise of further breakthroughs in prosthetic devices, offering hope and improved quality of life for individuals with amputations. The success of this prosthesis underscores the transformative potential of combining advanced materials like PA12 and TPU with the precision and flexibility of additive manufacturing, paving the way for the next generation of prosthetic development.
FEASIBILITY OF THE GLIDE MYOELECTRIC CONTROL ALGORITHM TO PARTIAL HAND PROSTHESIS CONTROL
While the number of individuals with partial hand limb loss is ten times greater than all other upper limb amputation categories combined, the state of available technology for this underserved patient population is relatively poor. Even though studies report preference towards myoelectric partial hand prostheses, patient adoption of myoelectric devices has been stunted by the limitations of the only control methodology commercially available, direct control. To address this challenge, we suggest the novel application of the Glide myoelectric control strategy to individuals with partial hand limb loss. In this work, we describe preliminary investigations with two individuals with partial hand limb loss, one with standard, intrinsic musculature of the residual hand and another who has undergone state-of-the-art surgical interventions.
BARRIERS AND FACILITATORS TO ADOPTING A CLINICIAN DASHBOARD SUPPORTING UPPER LIMB MYOELECTRIC-CONTROLLED PROSTHESES
Integrating the use of myoelectric-controlled upper limb prostheses into daily life can be complex. A dashboard reporting data and insights on real-world prosthetic use and performance could improve information available for clinicians to inform care, as well as communication among end-users (prosthesis wearers) and their clinical care providers. The purpose of this project is to blend implementation science and user-centered design methodology to inform the development and refinement of a dashboard that improves efficiency and supports clinical decision-making related to prosthesis training and clinical support. We used a mixed methods approach with a stakeholder advisory panel, semi-structured interviews, and surveys from occupational therapists, prosthetists, end-users, and other technology stakeholders. Interviews reveal clinician and end-user concern related to design, adaptability of the platform to meet the needs, capability, and opportunity of the clinicians, as well as to address motivation of end-users. The dashboard may improve communication but also could create work infrastructure challenges that make the dashboard have a relative disadvantage over other forms of communication. Surveys suggest good usability, acceptability, appropriateness, and feasibility. However, occupational therapists, the primary target population, have the lowest perceived usability and feasibility scores at the current point of dashboard design and conceptualization. In summary, mixed methods data from clinicians and prosthesis wearers provides valuable information that can drive improved development of a dashboard.
In recent years there has been an increasing interest in alternative muscle sensing methods. Optical myography is a relatively new field in muscle sensing techniques, which uses light to detect variation in the shape underlying muscle as it is contracted. We investigated the effect of finger motion and sensor placement on the resulting optical signal and compared it to clinical electromyography. Evidence suggests that the optical signal is strongly correlated to muscle activity and has high spatial accuracy.
SPATIO-TEMPORAL CONVOLUTIONAL NETWORKS FOR MYOELECTRIC CONTROL
Utilising both within-channel temporal and between-channel spatial dependencies of the surface Electromyographic (sEMG) signals improves the accuracy of machine learning-based models of myoelectric control. Here, we introduce the Spatio-Temporal Convolutional Network (STCN) to decode five hand gestures from eight EMG signals recorded from the forearm of eight able-bodied subjects. We compared our proposed STCN model with a combination of a conventional convolutional neural network (CNN) and a Long Short-Term Memory (LSTM) deep learning model, as well as the Linear Discriminant Analysis (LDA). The results show that STCN model can outperform both CNN-LSTM and LDA methods at a much lower computational complexity.
A PORTABLE MYOELECTRIC PATTERN RECOGNITION-DRIVEN VIRTUAL TRAINING SYSTEM FOR PHANTOM LIMB PAIN MANAGEMENT
Individuals with limb absence commonly suffer from phantom limb pain (PLP), a debilitating condition with poorly understood mechanisms and limited treatment options. Current approaches to treat PLP have broadly proven unsafe or ineffective, leaving patients searching for alternative, long-term options. Recent studies have demonstrated the potential of phantom motor execution therapy, aided by myoelectric pattern recognition software and virtual reality systems, in alleviating PLP. However, widespread clinical adoption has been hindered by limited accessibility, especially for the lower limb absent population. This paper presents the design and development of a portable, myoelectric pattern recognition-driven virtual training system, tailored for at-home use by individuals with upper or lower limb absence to safely and effectively manage and reduce PLP.
MEDIUM DENSITY DIGITAL ELECTROMYOGRAPHY SENSING SYSTEM
Surface electromyographic (EMG) signals are widely used for diagnostic and control purposes. Traditional EMG recordings typically use sparse electrode setups, limiting their use in dynamic environments like prosthetics or virtual reality. We propose a medium-density EMG armband design that leverages digital technology to capture EMG data from 21 channels. This system, designed to be more practical for everyday use and research, was tested against traditional single-channel methods for classifying six hand gestures using machine learning. Our results indicate the medium-density EMG system offers superior gesture classification accuracy, making it a valuable tool for real-world applications. We aim to further enhance this EMG recording setup and introduce it an open-source platform to the MEC community at the conference.
PATTERN SEPARABILITY VISUAL FEEDBACK TO IMPROVE PATTERN RECOGNITION DECODING PERFORMANCE
State-of-the-art myoelectric upper limb prostheses control often utilize pattern recognition (PR) systems that translate electromyograph (EMG) activity to a desired movement. As possible prosthesis movements increase, users have difficulty generating sufficiently separable EMG signals that reliably operate all possible degrees of freedom. Current training regimens attempt to increase the separability of a user's EMG signals through trial-and-error, where a therapist prompts a user to generate EMG signals and provides advice based on the strength and channel distribution of the EMG. In this work, we present a novel visual feedback interface that allows users to observe how their EMG signals affect PR output directly.
3-STAGE NEURAL NETWORK TRAINING PROTOCOL FOR GENERALISABLE MYOELECTRIC CONTROL
Myoelectric control methods have undergone rapid evolution since the pre-1960s era. However, a longstanding challenge has been the variability of myoelectric signals across individuals, which impedes the development of universally applicable myoelectric control models. Researchers and companies in the field have been active in exploring various aspects such as different control strategies, pattern recognition methods, signal processing, and decoding. For instance, Meta recently reported a common model for a database of 6700 able-bodied participants. Development of such datasets with people with limb difference, in the higher education sector is unrealistic. But what we believe could be helpful is a scheme to guide researchers in addressing different stages of the process, with the aim of collectively developing a general-purpose, pre-trained, and generalisable myoelectric model. In this paper, we propose a 3-stage neural network training paradigm. Experiments were conducted with able-bodied participants to demonstrate the significance and necessity of each stage in the proposed scheme. Work is in progress to further enhance and verify the method. We aim to share this approach at MEC to receive feedback and invite collaborations for standardising data collection and pulling together our resources.
FEASIBILITY OF SPATIO-TEMPORAL LINEAR FEATURE LEARNING FOR MYOELECTRIC CONTROL: A SMALL WINDOW SIZE APPROACH
Numerous research papers have delved into spatio-temporal analysis for myoelectric control, yielding meaningful outcomes, often employing window sizes ranging from 100 to 300 milliseconds. However, the industry is interested in achieving robust performance within smaller window sizes, more applicable to real-world scenarios. This study introduces a novel approach, Spatio-Temporal Linear Feature Learning (STLFL), with a robust trade-off between high performance and compact window size. Our investigation primarily focused on five classes within the state-of-the-art DB5 dataset—rest, abduction of all fingers, pointing index, power sphere grasp, and prismatic pinch grasp. Comparative analyses with two established methods, namely support vector machine (SVM) and convolutional neural network (CNN), revealed that STLFL consistently outperformed, achieving an impressive average accuracy of 84.6±3.9% across 10 subjects within an 80-millisecond window in a 16-channel electromyography signal. These results highlight the efficiency of STLFL in achieving myoelectric control within a limited timeframe, demonstrating promising outcomes for multiclass applications in both future contexts and real-world scenarios.
Exploring user compliance in the training of regression-based myoelectric control
Regression-based myoelectric control is promising because it could enable simultaneous independent control over multiple degrees of freedom. However, limitations in the robustness of online control and the added complexity of acquiring labelled training data have hindered its adoption. The scattered mix of prompting methods, visualization styles, and speeds used in the literature to collect labelled regression training data has obfuscated how these different prompting styles may affect the performance of regression-based myoelectric control. This work thus begins to investigate the potential effects that different visualizations may have on user behaviours and the quality of acquired training data for myoelectric control. Two distinct behaviours, referred to as all or nothing and anticipation, emerged when comparing the training data of three different prompting styles. Subsequently, 6 subjects were coached to emulate each of these behaviours during training and then completed a 10-trial Fitts' Law to assess the online usability of two different support vector regression (SVR) models. Results show that both user behaviours and the choice of regression model can have profound impacts on the usability of regression-based myoelectric control. Notably, real-time performance was severely degraded by the anticipation behaviour when using a linear kernel SVR, resulting in a 70% reduction in completion rate. These preliminary results motivate future work into how best to prompt users when training for supervised regression-based myoelectric control.
KINEMATIC CHANGES WITH POWERED WRIST FLEXION FOR TRANSRADIAL PROSTHETIC USERS COMPLETING THE GAZE AND MOVEMENT ASSESSMENT (GAMA) PASTA BOX TASK
Many assessments used to evaluate prosthetic function primarily emphasize task completion time, overlooking the assessment of movement quality or the specific degree of freedom (DOF) activated during the task. For example, proper functioning of the wrist is crucial for accurate hand positioning, but the addition of this movement would likely add time to task completion. Unfortunately, only a limited number of available prosthetic wrists offer powered flexion and extension. As a result, users often need to rely on compensatory body movements, which can lead to injuries and even lead to abandonment of the device. In our study, we used the Gaze and Movement Assessment (GaMA) metric to compare task timing, endpoint trajectories, and 3D angular joint kinematics between a 1-DOF wrist and a 2-DOF wrist combined with a 1-DOF hand. Our hypothesis was that the 2-DOF wrist, though requiring more time, would yield kinematics closer to normative data and result in fewer compensatory movements compared to the 1-DOF wrist. Preliminary results on 4 individuals completing the Pasta Box task and utilizing a powered wrist flexion extension unit showed some changes in torso movements but large variability among the participants. Contrary to our hypothesis there was not a large difference in timing between the two conditions.
BioPoint: Single-site, Multi-sensor Compound Gesture Recognition
EMG-based gesture recognition tasks have received a lot of attention in recent years, mostly focused on multi-channel EMG sensors, leading to issues in ease of use and computational requirements of such systems. The present study leveraged the BioPoint, a smartwatch-like device, to proceed to multi-sensor deep-learning based gesture recognition. 3 hand gestures in 3 wrist orientations were targeted by measuring the EMG, PPG and IMU waveforms on able-bodied subjects (n=10). Preprocessing and feature extraction allowed the modalities to be used in a two-head neural network trained for the simultaneous classification of hand gestures and wrist rotation. During evaluation, the model obtained an average classification accuracy for hand gestures of 83.5 ± 12.4% and 94.3 ± 9.7% for wrist position. Overall, this study showed the potential of single-site, multi-sensor approaches for compound gesture recognition.
FUNCTIONAL OUTCOMES OF A TRANSRADIAL PROSTHESIS WITH AND WITHOUT WRIST FLEXION AND EXTENSION
This study explores the function of a 2-degree-of-freedom (DOF) prosthetic wrist compared to a single-degree-of-freedom wrist for people with below-elbow amputation. The study involves five participants who wore a custom-made 2DOF wrist system and an Ottobock Transcarpal hand, using pattern recognition-based control. Participants did in-lab tests in two conditions: wrist rotation only and wrist rotation with flexion/extension. Functional outcomes, including the Southampton Hand Assessment Procedure, Box and Blocks Test, Jebsen Taylor Test, Activity Measures for Upper Limb Amputees, Clothespin Relocation Task, and Assessment for Capacity of Myoelectric Control, were measured. Preliminary results show little difference between the two conditions, possibly due to the additional addition length, mass, or control complexity.
ENABLING MYOELECTRIC CONTROL TRAINING USING CONTINUOUS DATA THROUGH SELF-SUPERVISED REPRESENTATION LEARNING
In this work, we explore the potential of integrating continuous transition data into the training process for pattern recognition-based myoelectric control. We use a set of steady-state and continuous transition performance metrics to compare the performance of classifiers trained with continuous data versus the traditional ramp contraction approach. We further compare the performance of the popular LDA classifier with that of a deep gated recurrent unit (GRU) classifier capable of leveraging the temporal dynamics. We also introduce a novel self-supervised contrastive representation learning approach with augmentations that significantly improves the offline steady-state and transition performance. This work provides compelling early evidence of the potential for semi-supervised learning approaches to leverage temporal dynamics in continuous training data to improve the performance of pattern recognition-based myoelectric control.
EFFECT OF BIOMIMICRY ON PERCEIVED INTENSITY, NATURALNESS, AND PLEASANTNESS USING NON-INVASIVE ELECTRICAL STIMULATION
This study focused on testing different non-invasive electrical stimulations for their perceived intensity, naturalness, and pleasantness. The eventual goal is to develop natural feeling, non-invasive electrical stimulation for prostheses. We found no difference between naturalness and pleasantness ratings; however, the 1st order biomimetic algorithms (amplitude or frequency modulation) felt stronger than the 2nd order (frequency modulation) stimulus while keeping the total charge constant.
Comparison Of Differential Surface Emg Circuits And Interelectrode Spacing For Use With Regenerative Peripheral Nerve Interfaces
Surface electromyography (sEMG) studies how to detect electrical signals produced by muscles from the surface of the skin [1]. However, the noise and crosstalk from nearby muscles are some of the drawbacks to using a surface EMG system [1]. Single and double differential (SD and DD) surface electrode systems were designed to minimize these drawbacks. Still, sEMG systems have not yet been optimized to detect deep and/or small signals from within the body [1]. This is particularly an issue when considering regenerative peripheral nerve interfaces (RPNIs) for controlling a myoelectric prosthesis because EMG signals from RPNIs are usually too small for surface level detection (approximately 100µV - 2mV) [4]. Since, interelectrode spacing and pick-up volume have a direct relationship [6] these therefore pose an interesting avenue for designing a surface electrode system that can detect EMG signals from RPNIs. Through a model in Maxwell 3D by Ansys, we aim to explore how varying the interelectrode distance from 8mm-40mm can change the spatial pick-up volume of electrodes, and further examine how the SD and DD circuits process the information to determine the best approach for surface detection of an RPNI. In the future, these results will then be validated through human subject testing.
FIRST EVALUATION OF AN INTEGRATED SONOMYOGRAPHIC PROSTHESIS IN INDIVIDUALS WITH CONGENITAL LIMB DIFFERENCE
Sonomyography (SMG) or ultrasound-based sensing of muscle deformation is an emerging modality for enhancing upper limb prosthesis control, offering the ability to spatially resolve muscle activity in both superficial and deep layers. Prior studies from our group as well as other groups have demonstrated the feasibility of SMG for prosthetic control using clinical ultrasound imaging systems and as well as miniaturized wearable ultrasound systems with several single-element ultrasound transducers for tracking muscle interfaces. However, the performance of SMG using miniaturized ultrasound sensors incorporated into a prosthetic socket has not been evaluated thus far. In this study, we incorporated single element ultrasound sensors into custom designed prosthetic sockets for two individuals with congenital transradial limb difference and evaluated the performance of classification and proportional control. Our work demonstrates the potential for SMG as well as highlights some challenges that need to be overcome.
EXPLORING THE PERSPECTIVES OF DIFFERENT PROFESSIONS ON TASK-BASED UPPER-LIMB PROSTHESIS ASSESSMENT TECHNIQUES
Upper limb prostheses can significantly enhance independence and functionality for individuals with limb differences, offering them renewed independence, functionality, and quality of life. Task-based evaluation measures, which involve patients directly manipulating objects with their prosthesis, are crucial for accurately assessing performance. However, these measures must meet the needs of various stakeholders, including researchers, clinicians, and insurers, who rely on this data for patient care and technological advancements. Yet, the specific demands of these groups can vary widely and remain underexplored, creating a gap in developing universally applicable evaluation methods. Our study aims to investigate these differences by conducting an online survey targeting a broad spectrum of professionals, including physical and occupational therapists, certified prosthetists/orthotists, medical practitioners, and academic researchers. This approach aims to gather a comprehensive understanding of current evaluation practices and identify areas in need of refinement, ultimately contributing to enhanced precision in prosthetic evaluations and improved patient outcomes.
ENHANCING UPPER LIMB PROSTHETIC FABRICATION WITH 3D PRINTING TECHNOLOGY: OPPORTUNITIES AND APPLICATIONS
This study explores the transformative impact of 3D printing technology in the field of upper limb prosthetics, highlighting its pivotal role in customizing prosthetic devices to meet individual needs with precision and efficiency. Through detailed examination of practical applications ranging from musical playing devices to recreational implementation, we showcase the versatility of additive manufacturing in enhancing patient outcomes. Despite potential hurdles such as cost and the necessity for specialized expertise, the evidence underscores the substantial benefits of 3D printing, including significant improvements in prosthetic functionality, patient satisfaction, and overall efficiency and effectiveness. Our findings advocate for the integration of 3D printing into prosthetic fabrication, emphasizing its potential to revolutionize patient care by offering tailored solutions that cater to the unique requirements of individuals with upper limb differences.
DEVELOPMENT AND ASSESSMENT OF AN AUGMENTED REALITY FEEDBACK SYSTEM FOR PROSTHESIS USERS
Users of upper limb prostheses face a challenge when attempting to grasp fragile objects due to an impairment of naturalistic sensory feedback regarding grip strength. Augmented Reality (AR) has shown promise as a candidate in solving this issue. Here we present the implementation and testing of a novel AR application for the Microsoft HoloLens 2 and test it using an upper limb prosthesis simulator. Our novel AR software application displays a color overlay on top of a user's prosthetic hand that changes based on grip strength. Using a mechanical egg grasp-and-hold task, we compared grasp performance and learning between prosthesis users with and without AR grip force feedback. Using a mixed ANOVA, we observed no statistically significant difference in performance with and without AR feedback (F(1,15) = 0.87, p = 0.37) , though we did see improvement across three blocks of grasp trials (F(2,30) = 6.58, p = 0.004). The AR implementation as presented was not effective which could be explained by limitations of the HoloLens 2, our overlay visualization, a need for more user training, or the simplicity of the task. Emerging AR systems may be better suited to display useful sensory feedback for upper limb prostheses with reduced latency, higher resolution graphics, and improved hand tracking to better localize the visual overlay.
User perspectives on features of upper limb prostheses: A qualitative cross-case comparison
Researchers and prosthesis developers aim to add or modify the functional features of upper limb prosthetic devices, such as increasing the number of available movements, developing intuitive control schemes, and providing sensory feedback. However, user experiences with these features across currently available prosthetic devices are not well understood, nor do we know what the needs and perspectives of users are regarding prosthesis features. In this study, we collected in depth interviews with sixteen prosthesis users who had experience with a wide range of prosthesis types, including body-powered, single degree of freedom myoelectric, multi-degree of freedom myoelectric, and sensory augmentation. We used a qualitative case series design to examine experiences with and perspectives on the prosthesis features of movements, controls, terminal devices, and sensation. Study findings help to elucidate the current needs and preferences of upper limb prosthesis users and provide directions for future technology development.
Improving User-in-the-Loop Myoelectric Control Using Context Informed Incremental Learning
Screen or prosthesis guided training is typically used to train pattern recognition-based myoelectric control by providing controlled calibration samples with known labels. When these models are used with a user-in-the-loop, however, the observed patterns are much more variable, resulting in poor model extrapolation to these conditions and thus poor usability. Incremental and reinforcement learning approaches can continue learning from user-in-the-loop settings, but are limited in their reliability due to the lack of supervised labels and increased training times. In this work, we propose context informed incremental learning (CIIL), which adapts by drawing contextual information from the control task, to solve these issues. We test our claims across two conditions: a short training data scenario and a simulated electrode shift scenario. With only one second of initial training data per class, CIIL achieves similar throughput as SGT in a Fitts' law-style usability test after only two minutes of adaptation (the same amount of time taken for SGT). In the harder electrode shift scenario, CIIL significantly outperformed the pre-shifted SGT model after 5 minutes of adaptation, offering a promising direction for future clinical validation of user-in-the-loop training.
A RESPONSIVE MYOELECTRIC CONTROL SIGNAL PROCESSING TECHNIQUE USING MUSCLE EXCITATION-CONTRACTION MODELING
Existing myoelectric controllers operate in a sequential fashion and use a state machine architecture to select grip postures. Direct control interfaces that seek to map individual muscles with joints in a prosthesis can provide a greater ability to perform individuated movements but require users to selectively activate their muscles to prevent unintended motion from natural muscle co-activations. Musculoskeletal modeling offers a possibility to estimate joint motion from muscle co-activation patterns themselves but require significant computational resources to run in real time. A major source of delay in a myoelectric system that reduces the amount of time available for advanced signal decoding schemes such as a musculoskeletal model is the low-pass filtering of rectified EMG signals. To minimize these delays, we explore the low-pass filtering properties of skeletal muscle using a simplified excitation-contraction dynamics model, applying a thresholded EMG signal and the rectified EMG profile itself as model inputs. Our results indicate that passing these signals through a biomechanical model of muscle can produce a usable myoelectric control signal while introducing a physiologically appropriate amount of delay between EMG onset and muscle force estimation.
ICE IS NICE: A MODULAR GAMIFIED RESEARCH AND TRAINING PLATFORM FOR PEDIATRIC UPPER LIMB PROSTHETIC CONTROL
Training for children who are prescribed myoelectric upper limb prostheses presents unique challenges in maintaining attention, motivation, and ultimately providing an enjoyable experience that is effective in developing the core motor skills required for device operation. From a clinical perspective, patient engagement is critical for maximizing functional outcomes, and from a research perspective, it can be vital to ensuring the quality of collected data. Therefore, our goal was to develop a training and research platform designed to both collect high-quality data from actively engaged participants and to provide them with a fun and engaging way to practice actuating the muscles relevant to myoelectric prosthetic control. “Ice is Nice” is a side scrolling video game that prompts children to perform a variety of movements with their missing hand, and the game is controlled using real-time measurement of their muscular activity. Our system is agnostic to muscle measurement systems, capable of using electromyography, force myography, and ultrasound-based control, among many others. As the game is played, data is logged to capture metrics relevant to game proficiency, human motor learning, and machine learning performance. Therefore, we suggest “Ice is Nice” provides a research and training platform with significant potential to support numerous follow-on studies conducted with children and adults. These studies aim to develop robust prosthetic control strategies, understand the effects of motor learning on prosthetic operation, and examine the functional capabilities of individuals operating upper limb prostheses.
THE EFFECTS OF LIMB POSITION AND APPLIED LOAD ON HAND GESTURE CLASSIFICATION ACCURACY USING ELECTROMYOGRAPHY AND FORCE MYOGRAPHY
Modern mechatronic upper limb prostheses are controlled using surface electromyography sensors (EMG) that are typically embedded in the prosthetic socket. However, when the user moves their device in space or interacts with an object, changes in electrode contact pressure can occur that work to the detriment of consistent and effective prosthesis control. Yet, we suggest that these pressure changes offer unique information that can be captured using force myography (FMG) and decoded to help classify intended prosthesis movements. Thus, the goal of this work was to investigate the feasibility of combining FMG with EMG to classify hand grasping movements in an able-bodied cohort and compare this combination to EMG and FMG alone. We hypothesized that FMG will capture complimentary information to the EMG data and when combined, will produce more robust classification accuracies when the user's limb moves in space or grasps objects of varying loads. We used a custom EMG+FMG armband and instructed N=21 participants to grasp objects of different weights at a variety of different positions using 4 different hand grasp movements. The results demonstrated that the average classification accuracy of EMG+FMG was statistically different and of higher classification accuracy when compared to EMG and FMG. It was also found that position and load affect classification accuracy together suggesting that control techniques that adapt to these changes are likely to produce more effective prosthetic control performance.
A MODULAR SOLUTION TO A UNIQUE DESIGN REQUEST FOR A SHOULDER DISSARTICULATION PROSTHESIS: A CASE STUDY
This case study details the development of a customized myoelectric prosthesis for a patient with a modified shoulder disarticulation level amputation, focusing on the integration of a novel detachable arm feature to enhance user comfort and functionality. By employing additive manufacturing technology and innovative design strategies, a prosthetic solution was crafted to meet the specific needs of the patient, including prolonged wear and ease of use with the most notable quick-disconnect mechanisms. The resulting prosthesis not only fulfilled the patient's request for a modular, lightweight arm but also significantly improved his quality of life by allowing for greater flexibility, versatility, and adaptability in daily activities. This work demonstrates the potential for tailored prosthetic solutions to address complex challenges, highlighting the importance of patient-catered design choices in the field of prosthetics.
AFFECTED MUSCLES RETAIN DEXTROUS MOTOR CAPABILITIES IN CHILDREN BORN WITH UPPER-LIMB DEFICIENCIES
Children with Unilateral Congenital Below-Elbow Deficiencies (born without a hand, UCBED) have a high rate of prosthetic abandonment, pointing to unresolved challenges that may be distinct from those faced by adults with limb loss. There is limited knowledge of the motor control these children have over their affected muscles, a highly relevant question for effective dextrous prosthetic control. Our research aims to measure the extent of volitional muscle activation that exists in the residuum when children attempt moving their missing hand, with the goal of creating highly functional pediatric-specific prosthetic devices. In this work, we recruited 28 pediatric UCBED patients across four Shriners Hospital locations. We measured muscle activity using ultrasound imaging and surface electromyography while children attempted 10 missing-hand movements, then used machine learning to analyze the patterns of the affected and unaffected sides. Our algorithms predicted hand movements from residual muscle activity at over 80% accuracy in most cases, and well above chance in all participants. This indicates inherent muscular control which may be leveraged to develop more functional prosthetic devices tailored towards pediatric UCBED patients.
POWER CONSUMPTION, LATENCY, AND MAXIMUM NUMBER OF SUPPORTED NODES FOR BLE BIOSENSOR APPLICATIONS
Wearable wireless physiological monitoring devices have emerged as powerful tools in healthcare, facilitating continuous monitoring of vital signs and physiological parameters in real-time. Many of these applications require small profile, and low-power battery operated devices. Bluetooth Low Energy (BLE) is a commonly selected wireless communication protocol as it provides fast data transfer, low cost, and low power. Physiological monitoring applications do not have as strict real-time latency requirements as control applications. For control use-cases, such as prosthesis control, real-time latency has a direct impact on user experience. In this work we explored various BLE parameter configurations in a multi-channel sensor node system to determine their relationship to power consumption, the number of supported peripheral nodes, and latency. Data collected during these experiments indicates that using longer connection intervals leads to a decrease in power consumption and that shorter event lengths allows for support of more peripheral sensor nodes for a given connection interval. It was observed that the typical latency between the application of an input signal at a peripheral node and its detection on the central node is approximately one connection interval. Future work should continue to investigate techniques to optimize power consumption, extend the number of supported peripheral nodes, and minimize latency through the system specifically for control applications.