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Hand gesture classification is crucial for the control of many modern technologies, ranging from virtual and augmented reality systems to assistive mechatronic devices. A prominent control technique employs surface electromyography (EMG) and pattern recognition algorithms to identify specific patterns in muscle electrical activity and translate these to device commands. While being well established in consumer, clinical, and research applications, this technique suffers from misclassification errors caused by limb movements and the weight of manipulated objects, both vital aspects of how we use our hands in daily life. An emerging alternative control technique is force myography (FMG) which uses pattern recognition algorithms to predict hand gestures from the axial forces present at the skin’s surface created by contractions of the underlying muscles. As EMG and FMG capture different physiological signals associated with muscle contraction, we hypothesized that each may offer unique additional information for gesture classification, potentially improving classification accuracy in the presence of limb position and object loading effects. Thus, we tested the effect of limb position and grasped load on 3 different sensing modalities: EMG, FMG, and the fused combination of the two. 27 able-bodied participants performed a grasp and release task with 4 hand gestures at 8 positions and under 5 object weight conditions. We then examined the effects of limb position and grasped load on gesture classification accuracy across each sensing modality. It was found that position and grasped load had statistically significant effects on the classification performance of the 3 sensing modalities and that the combination of EMG and FMG provided the highest classification accuracy of hand gesture, limb position, and grasped load combinations (97.34%) followed by FMG (92.27%) and then EMG (82.84%). This points to the fact that the addition of FMG to traditional EMG control systems offers unique additional data for more effective device control and can help accommodate different limb positions and grasped object loads. 

Wearable technologies for hand gesture classification are becoming increasingly prominent due to the growing need for more natural, human-centered control of complex devices. This need is particularly evident in emerging fields such as virtual reality and bionic prostheses, which require precise control with minimal delay. One method used for hand gesture recognition is force myography (FMG), which utilizes non-invasive pressure sensors to measure radial muscle forces on the skin’s surface of the forearm during hand movements. These sensors, typically force-sensitive resistors (FSRs), require additional circuitry to generate analog output signals, which are then classified using machine learning to derive corresponding control signals for the device. The performance of hand gesture classification can be influenced by the characteristics of this output signal, which may vary depending on the circuitry used. Our study examined three commonly used circuits in FMG systems: the voltage divider (VD), unity gain amplifier (UGA), and transimpedance amplifier (TIA). We first conducted benchtop testing of FSRs to characterize the impact of this circuitry on linearity, deadband, hysteresis, and drift, all metrics with the potential to influence an FMG system’s performance. To evaluate the circuit’s performance in hand gesture classification, we constructed an FMG band with 8 FSRs, using an adjustable Velcro strap and interchangeable circuitry. Wearing the FMG band, participants (N = 15) were instructed to perform 10 hand gestures commonly used in daily living. Our findings indicated that the UGA circuit outperformed others in minimizing hysteresis, drift and deadband with comparable results to the VD, while the TIA circuit excelled in ensuring linearity. Further, contemporary machine learning algorithms used to detect hand gestures were unaffected by the circuitry employed. These results suggest that applications of FMG requiring precise sensing of force values would likely benefit from use of the UGA. Alternatively, if hand gesture state classification is the only use case, developers can take advantage of benefits offered from using less complex circuitry such as the VD. 

Abstract In recent years, commercially available dexterous upper limb prostheses for children have begun to emerge. These devices derive control signals from surface electromyography (measure of affected muscle electrical activity, sEMG) to drive a variety of grasping motions. However, the ability for children with congenital upper limb deficiency to actuate their affected muscles to achieve naturalistic prosthetic control is not well understood, as compared to adults or children with acquired hand loss. To address this gap, we collected sEMG data from 9 congenital one-handed participants ages 8–20 years as they envisioned and attempted to perform 10 different movements with their missing hands. Seven sEMG electrodes were adhered circumferentially around the participant’s affected and unaffected limbs and participants mirrored the attempted missing hand motions with their intact side. To analyze the collected sEMG data, we used time and frequency domain analyses. We found that for the majority of participants, attempted hand movements produced detectable and consistent muscle activity, and the capacity to achieve this was not dissimilar across the affected and unaffected sides. These data suggest that children with congenital hand absence retain a degree of control over their affected muscles, which has important implications for translating and refining advanced prosthetic control technologies for children. 

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. 

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. 

Abstract Children with a unilateral congenital below elbow deficiency (UCBED) have one typical upper limb and one that lacks a hand, ending below the elbow at the proximal/mid forearm. UCBED is an isolated condition, and affected children otherwise develop normal sensorimotor control. Unlike adults with upper limb absence, the majority of whom have an acquired loss, children with UCBED never developed a hand, so their residual muscles have never actuated an intact limb. Their ability to purposefully modulate affected muscle activity is often assumed to be limited, and this assumption has influenced prosthetic design and prescription practices for this population as many modern devices derive control signals from affected muscle activity. To better understand the motor capabilities of the affected muscles, we used ultrasound imaging to study 6 children with UCBED. We examined the extent to which subjects activate their affected muscles when performing mirrored movements with their typical and missing hands. We demonstrate that all subjects could intentionally and consistently enact at least five distinct muscle patterns when attempting different missing hand movements (e.g., power grasp) and found similar performance across affected and typically developed limbs. These results suggest that although participants had never actuated the missing hand they could distinctively and consistently activate the residual muscle patterns associated with actions on the unaffected side. These findings indicate that motor control still develops in the absence of the normal effector, and can serve as a guide for developing prostheses that leverage the full extent of these children’s motor control capabilities. 

This article provides a comprehensive narrative review of physical task-based assessments used to evaluate the multi-grasp dexterity and functional impact of varying control systems in pediatric and adult upper-limb prostheses. Our search returned 1,442 research articles from online databases, of which 25 tests—selected for their scientific rigor, evaluation metrics, and psychometric properties—met our review criteria. We observed that despite significant advancements in the mechatronics of upper-limb prostheses, these 25 assessments are the only validated evaluation methods that have emerged since the first measure in 1948. This not only underscores the lack of a consistently updated, standardized assessment protocol for new innovations, but also reveals an unsettling trend: as technology outpaces standardized evaluation measures, developers will often support their novel devices through custom, study-specific tests. These boutique assessments can potentially introduce bias and jeopardize validity. Furthermore, our analysis revealed that current validated evaluation methods often overlook the influence of competing interests on test success. Clinical settings and research laboratories differ in their time constraints, access to specialized equipment, and testing objectives, all of which significantly influence assessment selection and consistent use. Therefore, we propose a dual testing approach to address the varied demands of these distinct environments. Additionally, we found that almost all existing task-based assessments lack an integrated mechanism for collecting patient feedback, which we assert is essential for a holistic evaluation of upper-limb prostheses. Our review underscores the pressing need for a standardized evaluation protocol capable of objectively assessing the rapidly advancing prosthetic technologies across all testing domains. 

Abstract There have been significant advances in biosignal extraction techniques to drive external biomechatronic devices or to use as inputs to sophisticated human machine interfaces. The control signals are typically derived from biological signals such as myoelectric measurements made either from the surface of the skin or subcutaneously. Other biosignal sensing modalities are emerging. With improvements in sensing modalities and control algorithms, it is becoming possible to robustly control the target position of an end-effector. It remains largely unknown to what extent these improvements can lead to naturalistic human-like movement. In this paper, we sought to answer this question. We utilized a sensing paradigm called sonomyography based on continuous ultrasound imaging of forearm muscles. Unlike myoelectric control strategies which measure electrical activation and use the extracted signals to determine the velocity of an end-effector; sonomyography measures muscle deformation directly with ultrasound and uses the extracted signals to proportionally control the position of an end-effector. Previously, we showed that users were able to accurately and precisely perform a virtual target acquisition task using sonomyography. In this work, we investigate the time course of the control trajectories derived from sonomyography. We show that the time course of the sonomyography-derived trajectories that users take to reach virtual targets reflect the trajectories shown to be typical for kinematic characteristics observed in biological limbs. Specifically, during a target acquisition task, the velocity profiles followed a minimum jerk trajectory shown for point-to-point arm reaching movements, with similar time to target. In addition, the trajectories based on ultrasound imaging result in a systematic delay and scaling of peak movement velocity as the movement distance increased. We believe this is the first evaluation of similarities in control policies in coordinated movements in jointed limbs, and those based on position control signals extracted at the individual muscle level. These results have strong implications for the future development of control paradigms for assistive technologies.