Upper-limb Prostheses Research Articles

Nonfinancial Barriers to Prosthetic Use Following Upper-Limb Amputation: A Prospective Observational Study in India

ABSTRACT Introduction Upper-limb traumatic amputation is a common and poorly managed situation. Despite the huge burden, no study from the developing countries has evaluated rates of prosthesis acceptance and reasons for rejecting prostheses. Materials and Methods This was a longitudinal prospective observational study with a consecutive sampling technique. Participants were assessed after a period of 6 months of isolated upper-limb amputation. Patients were evaluated for reasons of nonfitment of upper-limb prosthesis despite their willingness to be fitted with an appropriate prosthesis. We conducted semistructured interviews to identify the potential hindrances. Results A total of 44 patients were recruited, out of which 38 did not get the prosthesis. A total of 57.8% reported a nonfinancial reason for prosthesis nonfitment. Nonfinancial is further classified into medical and nonmedical categories as per verbatim remarks by the patient. Among the nonmedical reasons, the most common reason was apprehension toward the use of the prosthesis, followed by satisfaction without the prosthesis and the weight of the prosthesis. In terms of medical reasons, the most common reason was persistent phantom limb pain, followed by residual limb infection. Conclusions Identification of “potentially correctable” hindrances to prosthesis acceptance is an essential step in the rehabilitation of persons with limb loss. All efforts should be made toward these hindrances so that persons with limb difference can be better rehabilitated. Clinical Relevance Given that some persons with limb difference need to be fitted with prostheses for optimal rehabilitation and to return as an able contributor to society, the identification of hindrances to prosthesis fitting is imperative to make further inroads toward improving prosthesis acceptance rate.

Multiplexed Piezoelectric Electronic Skin with Haptic Feedback for Upper Limb Prosthesis

AbstractUpper limb amputation severely impairs tactile perception, limiting daily activities. Developing a near‐natural replacement with prosthetic devices requires improving user sensory experiences during object interactions. The ideal upper limb prosthesis should provide real‐time sensory feedback, mirroring natural experiences. Current prostheses struggle with providing adequate tactile feedback due to sensory limitations. Inspired by the sensory properties of skin, we present a micro‐fabricated, multiplexed electronic skin (e‐skin) with actuators for sensory feedback in upper limb amputation. The piezoelectric‐capacitive sensor array detects static pressure, temperature, vibration, and texture, with integrated actuators stimulating the skin to provide real‐time feedback. The sensors integrate with actuators via readout electronics, making the system standalone and easy to use. The flexible, compact sensor array design (two pixels within a 1 cm² footprint) detects a wide range of pressure (0.5–10 kPa), temperature (22–60 °C), vibration (35–100 Hz), and texture (2.5–45 Hz), suitable for daily use. The e‐skin, attached to a prosthetic finger, is tested for feasibility on human volunteers with wrist‐mounted actuators. Statistics are used to quantitatively assess system performance. The integration of multiplexed sensors and actuators enhances tactile feedback, improving the quality of life for people with upper limb amputations.

Electromyographically controlled prosthetic wrist improves dexterity and reduces compensatory movements without added cognitive load

Wrist function is a top priority for transradial amputees. However, the combined functional, biomechanical, and cognitive impact of using a powered prosthetic wrist is unclear. Here, we quantify task performance, compensatory movements, and cognitive load while three transradial amputees performed a modified Clothespin Relocation Task using two myoelectric prostheses with and without the wrists. The two myoelectric prostheses include a commercial prosthesis with a built-in powered wrist, and a newly developed inexpensive prosthetic wrist for research purposes, called the “Utah wrist”, that can be adapted to work with various sockets and prostheses. For these three participants, task failure rate decreased significantly from 66% ± 12% without the wrist to 39% ± 9% with the Utah wrist. Compensatory forward leaning movements also decreased significantly, from 24.2° ± 2.5 without the wrist to 12.6° ± 1.0 with the Utah wrist, and from 23.6° ± 7.6 to 15.3° ± 7.2 with the commercial prosthesis with an integrated wrist. Compensatory leftward bending movements also significantly decreased, from 20.8° ± 8.6 to 12.3° ± 5.3, for the commercial with an integrated wrist. Importantly, simultaneous myoelectric control of either prosthetic wrist had no significant impact on cognitive load, as assessed by the NASA Task Load Index survey and a secondary detection response task. This work suggests that functional prosthetic wrists can improve dexterity and reduce compensation without significantly increasing cognitive effort. These results, and the introduction of a new inexpensive prosthetic wrist for research purposes, can aid future research and development and guide the prescription of upper-limb prostheses.

Toward Impedance Control in Human-Machine Interfaces for Upper-Limb Prostheses.

Adaptation of upper-limb impedance (stiffness, damping, inertia) is crucial for humans to physically interact with the external environment during grasping and manipulation tasks. Here, we present a novel framework for Adaptive Impedance Control of Upper-limb Prosthesis (AIC-UP) based on surface electromyography (sEMG) signals. AIC-UP uses muscle-tendon models driven by sEMG signals from agonist-antagonist muscle groups to estimate the human motor intent as joint kinematics, stiffness and damping. These estimates are used to implement a variable impedance controller on a simulated robot. Designed for use by amputees, joint torque or stiffness measurements are not used for model calibration. AIC-UP was evaluated with eight able-bodied subjects and a transradial amputee performing target-reaching tasks in simulation through wrist flexion-extension. The control performance was tested in free space and in the presence of unexpected perturbations. We show that AIC-UP outperformed a neural network that regresses the desired kinematics from sEMG signals, in terms of robustness to muscle coactivations needed to complete the task. These results were in agreement with the qualitative feedback from the participants. Additionally, we observed that AIC-UP enables the user to adapt the stiffness and damping to the tasks at hand.

Implementing social and affective touch to enhance user experience in human-robot interaction.

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.

Perspectives on the comparative benefits of body-powered and myoelectric upper limb prostheses

BackgroundPatient access to body-powered and myoelectric upper limb prostheses in the United States is often restricted by a healthcare system that prioritizes prosthesis prescription based on cost and perceived value. Although this system operates on an underlying assumption that design differences between these prostheses leads to relative advantages and disadvantages of each device, there is limited empirical evidence to support this view.Main textThis commentary article will review a series of studies conducted by our research team with the goal of differentiating how prosthesis design might impact user performance on a variety of interrelated domains. Our central hypothesis is that the design and actuation method of body-powered and myoelectric prostheses might affect users’ ability to access sensory feedback and account for device properties when planning movements. Accordingly, other domains that depend on these abilities may also be affected. While our work demonstrated some differences in availability of sensory feedback based on prosthesis design, this did not result in consistent differences in prosthesis embodiment, movement accuracy, movement quality, and overall kinematic patterns.ConclusionCollectively, our findings suggest that performance may not necessarily depend on prosthesis design, allowing users to be successful with either device type depending on the circumstances. Prescription practices should rely more on individual needs and preferences than cost or prosthesis design. However, we acknowledge that there remains a dearth of evidence to inform decision-making and that an expanded research focus in this area will be beneficial.

Congenital malformations of the hand and fingers

Congenital anomalies of the upper limb are in Denmark estimated to have an incidence of around 20 in 10,000 live births. This covers a wide array of conditions summarised in this review. At the time of referral, the patient is thoroughly examined, and a treatment plan is discussed with the family. In some cases, no treatment is needed, in others there might be a need for surgery, night splinting, or an upper limb prosthesis. In case an underlying syndromatic cause is suspected, the patient is referred for paediatric evaluation at specialized centre.

Tactile Perception in Upper Limb Prostheses: Mechanical Characterization, Human Experiments, and Computational Findings.

Tactile feedback is essential for upper-limb prostheses functionality and embodiment, yet its practical implementation presents challenges. Users must adapt to non-physiological signals, increasing cognitive load. However, some prosthetic devices transmit tactile information through socket vibrations, even to untrained individuals. Our experiments validated this observation, demonstrating a user's surprising ability to identify contacted fingers with a purely passive, cosmetic hand. Further experiments with advanced soft articulated hands revealed decreased performance in tactile information relayed by socket vibrations as hand complexity increased. To understand the underlying mechanisms, we conducted numerical and mechanical vibration tests on four prostheses of varying complexity. Additionally, a machine-learning classifier identified the contacted finger based on measured socket signals. Quantitative results confirmed that rigid hands facilitated contact discrimination, achieving 83% accuracy in distinguishing index finger contacts from others. While human discrimination decreased with advanced hands, machine learning surpassed human performance. These findings suggest that rigid prostheses provide natural vibration transmission, potentially reducing the need for tactile feedback devices, which advanced hands may require. Nonetheless, the possibility of machine learning algorithms outperforming human discrimination indicates potential to enhance socket vibrations through active sensing and actuation, bridging the gap in vibration-transmitted tactile discrimination between rigid and advanced hands.

Unravelling Influence Factors in Pattern Recognition Myoelectric Control Systems: The Impact of Limb Positions and Electrode Shifts.

Pattern recognition (PR)-based myoelectric control systems can naturally provide multifunctional and intuitive control of upper limb prostheses and restore lost limb function, but understanding their robustness remains an open scientific question. This study investigates how limb positions and electrode shifts-two factors that have been suggested to cause classification deterioration-affect classifiers' performance by quantifying changes in the class distribution using each factor as a class and computing the repeatability and modified separability indices. Ten intact-limb participants took part in the study. Linear discriminant analysis (LDA) was used as the classifier. The results confirmed previous studies that limb positions and electrode shifts deteriorate classification performance (14-21% decrease) with no difference between factors (p > 0.05). When considering limb positions and electrode shifts as classes, we could classify them with an accuracy of 96.13 ± 1.44% and 65.40 ± 8.23% for single and all motions, respectively. Testing on five amputees corroborated the above findings. We have demonstrated that each factor introduces changes in the feature space that are statistically new class instances. Thus, the feature space contains two statistically classifiable clusters when the same motion is collected in two different limb positions or electrode shifts. Our results are a step forward in understanding PR schemes' challenges for myoelectric control of prostheses and further validation needs be conducted on more amputee-related datasets.

Touch and slippage detection in robotic hands with spiking neural networks

Significant research efforts have focused on advancing upper limb prostheses, addressing issues such as dexterity, embodiment, weight, and resistance. These key features require enhancements to mitigate prosthesis abandonment. Sensory feedback is a vital feature for improving the dexterity and embodiment of prostheses for patients. Current techniques utilize electronic signals for feedback to control systems and to partially restore tactile sensations to amputees. However, it has already been demonstrated that, in order to elicit responses from biological neurons signals must be converted into neuromorphic tactile information. Most of those demonstrations relied on algorithmic implementations of mechanoreceptor and neuron models running on classic processor cores. However, integrating real-time encoding and control strategies into such architectures is challenging due to computational complexity and high power usage. A prospective solution is to increasingly incorporate neuromorphic technologies for prosthesis sensing and control. In this work, we demonstrate the feasibility of using spiking neural networks for performing two key functions in controlling upper limb prosthesis: touch and slippage detection. We found that a simple two-layered architecture with a few tens of neurons is sufficient to identify slip and touch with nearly 100% accuracy. The first layer consists of a heterogeneous population of neurons with their parameters adjusted to model slowly and fast-adapting type 1 mechanoreceptors. An output layer classifies touch or slip events. Furthermore, simulations showed that implementing the network in ultra-low-power neuromorphic hardware, such as Loihi, has the potential to reduce power consumption by up to 162 times, with respect to a von Neumann processor.

A novel approach for predicting usability of upper limb prostheses

Limb amputation can lead to significant functional challenges in daily activities, prompting amputees to use prosthetic devices (PDs). However, the cognitive demands of PDs and usability issues have resulted in user rejections. This study aimed to create a Human Performance Model for Upper-Limb Prosthetic Devices (HPM-UP). The model used formulations of learnability, error rate, memory load, efficiency, and satisfaction to assess usability. The model was validated in an experiment with 30 healthy participants using a bypass prosthetic device. Findings indicated that the HPM-UP successfully predicted the usability of prosthetic devices, aligning with human subject data. This research proposes a quantitative approach to predict upper limb prosthetic device usability by quantifying each dimension and computationally connecting them. The model, available on Github and executable with Rstudio, could enable clinicians to assess and analyze the human performance of various commercial prostheses, aiding in recommending optimal devices for patients.

Review of the foreign market for bioelectrically controlled upper limb prostheses

Review of the foreign market for bioelectrically controlled upper limb prostheses

Prosthesis Usage and Functional Status in Upper Limb Amputees: A Prospective Cross-Sectional Study.

Introduction Amputation poses significant challenges encompassing psychological, physical, and socio-economic dimensions, impacting individuals and society at large. In India, a substantial portion of the population faces loco-motor disabilities, with amputees forming a notable segment. Prosthetic rehabilitation plays a crucial role in mitigating the consequences of limb loss, aiming to restore autonomy post-amputation. Methods A prospective cross-sectional observational study was conducted over 18 months, from November 2018 to May 2020, involving unilateral upper limb amputees (ULAs) aged over 18 years. A consecutive cohort of 33 patients, predominantly male (30 males and three females), with a mean age of 43 ± 12 years (median: 43 years; range: 20-67 years), was prospectively enrolled in the study. Participants had completed at least one month of post-prosthetic fitment and were actively attending outpatient or prosthetic checkout clinics. The study utilized the Manipal Prosthetic Rehabilitation Success (PRS) score and the Orthotics and Prosthetics User Survey Upper Extremity Functional Status (OPUS UEFS) score to assess prosthesis usage and functional status. Statistical analyses included descriptive statistics, Chi-square tests, Mann-Whitney U tests, Kruskal-Wallis tests, and multivariate logistic regression analysis. Results The study revealed insights into upper limb prosthesis usage in India, highlighting factors influencing the success and challenges faced by ULAs. Associations were found between the success of prosthesis usage and several factors: occupation type (p=0.012), the side of amputation involving non-dominant limbs (p=0.033), comfort level (p=0.002), and prosthesis weight (p=0.029). Comfort level emerged as a primary predictor of usage success. The OPUS UEFS scores indicated varying levels of satisfaction and usage patterns among participants, with some utilizing prostheses for specific tasks while others for broader activities. Comfort level demonstrated a statistically significant difference in OPUS UEFS scores, favoring comfortable prostheses (p=0.020). Additionally, the mean OPUS UEFS score for patients with satisfactory or good prosthesis use was 53 ± 11 (median: 55; range: 22-64), compared to 45 ± 13 (median: 43; range: 18 - 64) in those with poor prosthesis use, with the difference nearing statistical significance (p=0.058). Conclusion The study sheds light on the landscape of upper limb prosthesis usage in India, emphasizing the need for tailored interventions based on individual needs and cultural contexts. The findings underscore the importance of comfort, side of amputation involving non-dominant limbs, occupation type, and prosthesis weight in determining the success of prosthesis usage. Opportunities exist to enhance upper limb prosthetic care in India by addressing cultural nuances and refining assessment tools to better suit the Indian population.

Prosthesis usability experience is associated with extent of upper limb prosthesis adoption: A Structural Equation Modeling (SEM) analysis.

Factors associated with upper limb prosthesis adoption are not well understood. In this study, we explored how prosthesis usability experience relates to the extent of prosthesis adoption through the development of a structural equation model (SEM). First, items related to prosthesis usability were developed and refined using cognitive testing and pilot testing and employed in a survey of 402 prosthesis users (mean age 61.7 (sd 14.4), 77.1% Veterans). The SEM examined two unidimensional latent constructs: Prosthesis Usability Experience and Prosthesis Adoption-and each had multiple measured indicators. SEMs tested direct as well as moderating and mediating effects between the latent constructs and covariates related to demographics and prosthesis type. SEM found a significant positive association between Prosthesis Usability Experience and Extent of Prosthesis Adoption. Several covariates had direct effects on prosthesis adoption: 1) Extent of Prosthesis Adoption was lower for those with transhumeral and shoulder amputation, and higher for those with bilateral amputation, compared to the reference group with unilateral transradial amputation and 2) Myoelectric multiple degree of freedom (multi-DOF) prosthesis use was associated with lower Extent of Prosthesis Adoption, compared to body-powered prosthesis use. Myoelectric multi-DOF use also modified the effect of Prosthesis Usability Experience on Extent of Prosthesis Adoption. For those with bilateral ULA, the strength of the relationship between Prosthesis Usability Experience and Extent of Prosthesis Adoption was reduced. Findings suggest that in order to increase prosthesis adoption, prosthetics developers and rehabilitation providers should focus on implementing strategies to improve prosthesis usability experience. New Prosthesis Usability Experience measures could be used to identify persons at greater risk for poor prosthesis adoption and target interventions to increase prosthesis use.

A GAN based PID controller for highly adaptive control of a pneumatic-artificial-muscle driven antagonistic joint

Upper limb prostheses are commonly propelled by pneumatic artificial muscles organized in an antagonistic arrangement. Nonetheless, the control of upper limb prostheses under changing/unknown situations is difficult and necessary for a variety of real-world applications. Adaptive control, learning-based control, and robust control have been studied to deal with such challenges. However, their adaptability is insufficient for prostheses used in daily life, which are exposed to variable task levels, user motor characteristics, and prosthetic features. This paper introduces a highly adaptive controller for the first time based on Generative Adversarial Nets and proportional–integral–derivative controller (G-PID controller). G-PID controller comprises a generator for generating compensation actions to enhance PID responsiveness when controlling the unknown/changing system. Moreover, it incorporates a discriminator that receives responses from both a user-preselected reference system and the compensated changing/unknown system, and simultaneously determines the source of these responses. Through continuous updates, the compensator modifies the response of unknown/changing system to align with the reference system, thereby facilitating adaptive control. The G-PID controller’s effectiveness is evaluated through 1-degree of freedom (DoF) joint and 2-DoF shoulder prostheses in simulation experiments, and further validated in prototype experiments focusing on online learning for unknown and time-varying payload. The results demonstrate its ability to deal with diverse types of unknowns/changes, marking a significant advancement towards incorporating prostheses seamlessly into daily life.

WITHDRAWN: Development of a low cost active upper-limb prosthesis using design thinking methodology for improvement user suitability

WITHDRAWN: Development of a low cost active upper-limb prosthesis using design thinking methodology for improvement user suitability

Electromyography signal based hand gesture classification system using Hilbert Huang transform and deep neural networks

This research aims to provide the groundwork for smartly categorizing hand movements for use with prosthetic hands. The hand motions are classified using surface electromyography (sEMG) data. In reaction to a predetermined sequence of fibre activation, every single one of our muscles contracts. They could be useful in developing control protocols for bio-control systems, such human-computer interaction and upper limb prostheses. When focusing on hand gestures, data gloves and vision-based approaches are often used. The data glove technique requires tedious and unnatural user engagement, whereas the vision-based solution requires significantly more expensive sensors. This research offered a Deep Neural Network (DNN) automated hand gesticulation recognition system based on electromyography to circumvent these restrictions. This work primarily aims to augment the concert of the hand gesture recognition system via the use of an artificial classifier. To advance the recognition system's classification accuracy, this study explains how to build models of neural networks and how to use signal processing methods. By locating the Hilbert Huang Transform (HHT), one may get the essential properties of the signal. When training a DNN classifier, these characteristics are sent into it. The investigational results reveal that the suggested technique accomplishes a better categorization rate (98.5 % vs. the alternatives).

A geometric algebra-based approach for myoelectric pattern recognition control and faster prosthesis recalibration

ObjectiveAlthough many advancements have been made on myoelectric pattern-recognition, the control of poly-articulated upper-limb prostheses remains insufficiently robust. Electrode-shift, sweat or fatigue degrade the performance of classifiers over time, resulting in unfruitful device usage and frequent re-calibration. To tackle this issue, here we introduce two models − µP6 and µP8 − that combine Geometric Algebra with nearest-neighbor classification. We aim at reducing both the necessary training data and training time and, unlike most current state-of-the-art algorithms, we exploit an alternative geometric representation (and visualization) of the EMG signal as different polygons for different types of gestures, facilitating the explanation of the decision-making process to a layman. Moreover, we explore four abstention strategies to reduce the number of misclassifications. MethodsWe perform an offline analysis on two datasets, alongside two other standard models: nonlinear logistic regression (NLR) and linear discriminant analysis (LDA). ResultsEven with few training data, the proposed algorithms achieve high F1-scores (>0.95), significantly higher or non-significantly different from the values obtained with NLR and LDA, while maintaining relatively low abstentions rates and training times (<2 ms). ConclusionThe proposed algorithms allow to reduce the amount of training data and training times without compromising recognition rates. SignificanceThe proposed algorithms may contribute for a faster prosthesis re-calibration procedure while allowing to re-gain high recognition rates. Furthermore, the decision-making process is explainable and interpretable, potentially improving user trust and acceptance.

Review of the Russian market for bioelectrically controlled upper limb prostheses

Review of the Russian market for bioelectrically controlled upper limb prostheses

PVA/PANI-DBSA Nanomesh Tactile Sensor for Force Feedback.

Touch serves as an important medium for human-environment interaction. The piezoresistive tactile sensor has attracted much attention due to its convenient technology, simple principle, and convenient signal acquisition and analysis. In this paper, conductive beads-on-string polyvinyl alcohol (PVA)/polyaniline doped with dodecyl benzene sulfonic acid (PANI-DBSA) nanofibers were fabricated via the electrospinning technique. Due to the special nanostructure of PVA-coated PANI-DBSA, the tactile sensor presented a wide measuring range of 12 Pa-121 kPa and appreciable sensitivity of 8.576 kPa-1 at 12 Pa~484 Pa. In addition, the response time and recovery time of the sensor were approximately 500 ms, demonstrating promising prospects in the field of tactile sensing for active upper limb prostheses.