A multifaceted suite of metrics for comparative myoelectric prosthesis controller research.

Upper limb robotic (myoelectric) prostheses are technologically advanced, but challenging to use. In response, substantial research is being done to develop person-specific prosthesis controllers that can predict a user's intended movements. Most studies that test and compare new controllers rely on simple assessment measures such as task scores (e.g., number of objects moved across a barrier) or duration-based measures (e.g., overall task completion time). These assessment measures, however, fail to capture valuable details about: the quality of device arm movements; whether these movements match users' intentions; the timing of specific wrist and hand control functions; and users' opinions regarding overall device reliability and controller training requirements. In this work, we present a comprehensive and novel suite of myoelectric prosthesis control evaluation metrics that better facilitates analysis of device movement details-spanning measures of task performance, control characteristics, and user experience. As a case example of their use and research viability, we applied these metrics in real-time control experimentation. Here, eight participants without upper limb impairment compared device control offered by a deep learning-based controller (recurrent convolutional neural network-based classification with transfer learning, or RCNN-TL) to that of a commonly used controller (linear discriminant analysis, or LDA). The participants wore a simulated prosthesis and performed complex functional tasks across multiple limb positions. Analysis resulting from our suite of metrics identified 16 instances of a user-facing problem known as the "limb position effect". We determined that RCNN-TL performed the same as or significantly better than LDA in four such problem instances. We also confirmed that transfer learning can minimize user training burden. Overall, this study contributes a multifaceted new suite of control evaluation metrics, along with a guide to their application, for use in research and testing of myoelectric controllers today, and potentially for use in broader rehabilitation technologies of the future.

A Case Series in Position-Aware Myoelectric Prosthesis Control Using Recurrent Convolutional Neural Network Classification with Transfer Learning.

Position-aware myoelectric prosthesis controllers require long, data-intensive training routines. Transfer Learning (TL) might reduce training burden. A TL model can be pre-trained using forearm muscle signal data from many individuals to become the starting point for a new user. A recurrent convolutional neural network (RCNN)-based classifier has already been shown to benefit from TL in offline analysis (95% accuracy). The present real-time study tested whether an RCNN-based classification controller with TL (RCNN-TL) could reduce training burden, offer improved device control (per functional task performance metrics), and mitigate what is known as the "limb position effect". 27 participants without amputation were recruited. 19 participants performed wrist/hand movements across multiple limb positions, with resulting forearm muscle signal data used to pre-train RCNN-TL. 8 other participants donned a simulated prosthesis, retrained (calibrated) and tested RCNN-TL, plus trained and tested a conventional linear discriminant analysis classification controller (LDA-Baseline). Results confirmed that TL reduces user training burden. RCNN-TL yielded improved task performance durations over LDA-Baseline (in specific Grasp and Release phases), yet other metrics worsened. Overall, this work contributes training condition factors necessary for TL success, identifies metrics needed for comprehensive control analysis, and contributes insights towards improved position-aware control.

Composite Recurrent Convolutional Neural Networks Offer a Position-Aware Prosthesis Control Alternative While Balancing Predictive Accuracy with Training Burden.

To mitigate the "limb position effect" that hinders myoelectric upper limb prosthesis control, pattern recognition-based models must accurately predict user-intended movements across a multitude of limb positions. Such models can use electromyography (EMG) and inertial measurement units to capture necessary multi-position data. However, this data capture solution requires lengthy user-performed model training routines, with movements in many limb positions, plus retraining thereafter due to inherent signal variations over time. While a general-purpose control model (trained with a dataset that represents numerous device users) eliminates the user-training requirement altogether, it yields low movement predictive accuracy. Conversely, a user-specific control model (trained with a smaller dataset from an individual) yields high predictive accuracy, but requires retraining over time. This study capitalizes on the benefits offered by both such control options, and contributes an alternative control solution-a novel recurrent convolutional neural network (RCNN)-based Composite Model that combines the representation portion of a general-purpose model, with the decision portion of a user-specific model. The resulting Composite Model offers moderate movement predictive accuracy across various limb positions and a reduction in user training routine requirements, suggesting a new research direction to help mitigate the limb position effect along with model training burden.

Development and Validation of a Closed-Loop Functional Electrical Stimulation-Based Controller for Gait Rehabilitation Using a Finite State Machine Model.

Functional electrical stimulation (FES) can be used to initiate lower limb muscle contractions and has been widely applied in gait rehabilitation. Establishing the correct timing of FES activation during each phase of the gait (walking) cycle remains challenging as most FES systems rely on open-loop control, whereby the controller receives no feedback about joint kinematics and instead relies on predetermined/timed muscle stimulation. The objective of this study was to develop and validate a closed-loop FES-based control solution for gait rehabilitation using a finite state machine (FSM) model. A two-phased study approach was taken: (1) Experimentally-Informed Study: A neuromuscular-derived FSM model was developed to drive closed-loop FES-based control for gait rehabilitation. The finite states were determined using electromyography and joint kinematics data of 12 non-disabled adults, collected during treadmill walking. The gait cycles were divided into four states, namely: swing-to-stance, push off, pre-swing, and toe up. (2) Simulation Study: A closed-loop FES-based control solution that employed the resulting FSM model, was validated through comparisons of neuro-musculo-skeletal computer simulations of impaired versus healthy gait. This closed-loop controller yielded steadier simulated impaired gait, in comparison to an open-loop alternative. The simulation results confirmed that accurate timing of FES activation during the gait cycle, as informed by kinematics data, is important to natural gait retraining. The closed-loop FES-based solution, introduced in this study, contributes to the repository of gait rehabilitation control options and offers the advantage of being simplistic to implement. Furthermore, this control solution is expected to integrate well with powered exoskeleton technologies.

Neurorobotic fusion of prosthetic touch, kinesthesia, and movement in bionic upper limbs promotes intrinsic brain behaviors.

Bionic prostheses have restorative potential. However, the complex interplay between intuitive motor control, proprioception, and touch that represents the hallmark of human upper limb function has not been revealed. Here, we show that the neurorobotic fusion of touch, grip kinesthesia, and intuitive motor control promotes levels of behavioral performance that are stratified toward able-bodied function and away from standard-of-care prosthetic users. This was achieved through targeted motor and sensory reinnervation, a closed-loop neural-machine interface, coupled to a noninvasive robotic architecture. Adding touch to motor control improves the ability to reach intended target grasp forces, find target durometers among distractors, and promote prosthetic ownership. Touch, kinesthesia, and motor control restore balanced decision strategies when identifying target durometers and intrinsic visuomotor behaviors that reduce the need to watch the prosthetic hand during object interactions, which frees the eyes to look ahead to the next planned action. The combination of these three modalities also enhances error correction performance. We applied our unified theoretical, functional, and clinical analyses, enabling us to define the relative contributions of the sensory and motor modalities operating simultaneously in this neural-machine interface. This multiperspective framework provides the necessary evidence to show that bionic prostheses attain more human-like function with effective sensory-motor restoration.

Myoelectric prosthesis users and non-disabled individuals wearing a simulated prosthesis exhibit similar compensatory movement strategies.

BACKGROUND: Research studies on upper limb prosthesis function often rely on the use of simulated myoelectric prostheses (attached to and operated by individuals with intact limbs), primarily to increase participant sample size. However, it is not known if these devices elicit the same movement strategies as myoelectric prostheses (operated by individuals with amputation). The objective of this study was to address the question of whether non-disabled individuals using simulated prostheses employ the same compensatory movements (measured by hand and upper body kinematics) as individuals who use actual myoelectric prostheses. METHODS: The upper limb movements of two participant groups were investigated: (1) twelve non-disabled individuals wearing a simulated prosthesis, and (2) three individuals with transradial amputation using their custom-fitted myoelectric devices. Motion capture was used for data collection while participants performed a standardized functional task. Performance metrics, hand movements, and upper body angular kinematics were calculated. For each participant group, these measures were compared to those from a normative baseline dataset. Each deviation from normative movement behaviour, by either participant group, indicated that compensatory movements were used during task performance. RESULTS: Results show that participants using either a simulated or actual myoelectric prosthesis exhibited similar deviations from normative behaviour in phase durations, hand velocities, hand trajectories, number of movement units, grip aperture plateaus, and trunk and shoulder ranges of motion. CONCLUSIONS: This study suggests that the use of a simulated prosthetic device in upper limb research offers a reasonable approximation of compensatory movements employed by a low- to moderately-skilled transradial myoelectric prosthesis user.

Small supernumerary marker chromosomes derived from chromosome 14 and/or 22.

Small supernumerary marker chromosomes (sSMCs) are additional derivative chromosomes present in an otherwise numerically and structurally normal karyotype. They may derive from each of the 24 human chromosomes, and most contain a normal centromeric region with an alphoid sequence from a single chromosome. The majority of human chromosomes have a unique centromeric DNA-sequence enabling their indubitable characterization. However, chromosomes 14 and 22 share a common centromeric sequence D14/22Z1, and sSMCs with this DNA-stretch can derive from either chromosome. Euchromatin-carrying sSMCs(14 or 22) may be further characterized by molecular cytogenetics. However, in most diagnostic laboratories, heterochromatic sSMCs cannot be differentiated between chromosomes 14 or 22 derivation and are often reported as der(14 or 22). Still, heterochromatic sSMC(14 or 22) can be distinguished from each other using the D22Z4 probe (non-commercial) localized to 22p11.2. Herein, 355 sSMC(14 or 22) analyzed in the authors' laboratory during the last ~ 20 years are summarized to address the questions: (1) What are the true frequencies of chromosome 14- and chromosome 22- derived sSMCs within D14/22Z1-positive cases? (2) Does sub-characterization of sSMC(14) and sSMC(22) make a difference in routine diagnostics? These questions could be answered as follows: (ad 1) within the studied group of sSMCs ~ 40% are derived from chromosome 14 and ~ 60% from chromosome 22; (ad 2) the knowledge on exact sSMC origin can help to save costs in routine diagnostics; i.e. in a clinically abnormal person with sSMC(14) a test for uniparental disomy is indicated, which is not necessary if a chromosome 22 origin for the sSMC was determined.

A Modular Adjustable Transhumeral Prosthetic Socket for Evaluating Myoelectric Control.

Novel myoelectric control strategies may yield more robust, capable prostheses which improve quality of life for those affected by upper-limb loss; however, the development and translation of such strategies from an experimental setting towards daily use by persons with limb loss is a slow and costly process. Since prosthesis functionality is highly dependent on the physical interface between the user's prosthetic socket and residual limb, assessment of such controllers under realistic (noisy) environmental conditions, integrated into prosthetic sockets, and with participants with amputation is essential for obtaining representative results. Unfortunately, this step is particularly difficult as participant- and control strategy-specific prosthetic sockets must be custom-designed and manufactured. There is thus a need for a system to reduce these burdens and facilitate this crucial phase of the development pipeline. This study aims to address this gap through the design and assessment of an inexpensive and easy-to-use 3D-printed Modular-Adjustable transhumeral Prosthetic Socket (MAPS). This 3D-printed, open-source socket was developed in consultation with prosthetists and compared with a participant-specific suction socket in a single-participant case-study. We conducted mechanical and functional assessments to ensure that the developed socket enabled similar performance compared to participant-specific sockets. Both socket systems yielded similar results in mechanical and functional assessments, as well as in self-reported user feedback. The MAPS system shows promise as a research tool which catalyzes the development and deployment of novel myoelectric control strategies by better-enabling comprehensive assessment involving participants with amputations.

Gaze and Movement Assessment (GaMA): Inter-site validation of a visuomotor upper limb functional protocol.

BACKGROUND: Successful hand-object interactions require precise hand-eye coordination with continual movement adjustments. Quantitative measurement of this visuomotor behaviour could provide valuable insight into upper limb impairments. The Gaze and Movement Assessment (GaMA) was developed to provide protocols for simultaneous motion capture and eye tracking during the administration of two functional tasks, along with data analysis methods to generate standard measures of visuomotor behaviour. The objective of this study was to investigate the reproducibility of the GaMA protocol across two independent groups of non-disabled participants, with different raters using different motion capture and eye tracking technology. METHODS: Twenty non-disabled adults performed the Pasta Box Task and the Cup Transfer Task. Upper body and eye movements were recorded using motion capture and eye tracking, respectively. Measures of hand movement, angular joint kinematics, and eye gaze were compared to those from a different sample of twenty non-disabled adults who had previously performed the same protocol with different technology, rater and site. RESULTS: Participants took longer to perform the tasks versus those from the earlier study, although the relative time of each movement phase was similar. Measures that were dissimilar between the groups included hand distances travelled, hand trajectories, number of movement units, eye latencies, and peak angular velocities. Similarities included all hand velocity and grip aperture measures, eye fixations, and most peak joint angle and range of motion measures. DISCUSSION: The reproducibility of GaMA was confirmed by this study, despite a few differences introduced by learning effects, task demonstration variation, and limitations of the kinematic model. GaMA accurately quantifies the typical behaviours of a non-disabled population, producing precise quantitative measures of hand function, trunk and angular joint kinematics, and associated visuomotor behaviour. This work advances the consideration for use of GaMA in populations with upper limb sensorimotor impairment.

Hand Function Kinematics when using a Simulated Myoelectric Prosthesis.

Studies that investigate myoelectric prosthesis control commonly use non-disabled participants fitted with a simulated prosthetic device. This approach improves participant recruitment numbers but assumes that simulated movements represent those of actual prosthesis users. If this assumption is valid, then movement performance differences between simulated prosthesis users and normative populations should be similar to differences between actual prosthesis users and normative populations. As a first step in testing this assumption, the objective of this study was to quantify movement performance differences between simulated transradial myoelectric prosthesis hand function and normative hand function. Motion capture technology was used to obtain hand kinematics for 12 non-disabled simulated prosthesis participants who performed a functional object-manipulation task. Performance metrics, end effector movement, and grip aperture results were compared to 20 nondisabled participants who used their own hand during task execution. Simulated prosthesis users were expected to perform the functional task more slowly, with multiple peaks in end effector velocity profiles, and a plateau in grip aperture when reaching to pick up objects, when compared to non-disabled participants. This study confirmed these expectations and recommends that subsequent research be undertaken to quantify differences in actual myoelectric prosthesis hand function versus normative hand function.

Regarding the rights and duties of Clinical Laboratory Geneticists in genetic healthcare systems; results of a survey in over 50 countries.

Specialists of human genetic diagnostics can be divided into four groups: Medical Geneticists (MDG), Genetic Nurses and/or Counsellors (GN/GC), Clinical Laboratory Geneticists (CLG) and Laboratory Genetics Technicians (LGT). While the first two groups are in direct patient contact, the work of the latter two, of equal importance for patient care, are often hidden as they work behind the scenes. Herein the first study on the rights and duties of CLGs is presented. We present the results of a survey performed in 35 European and 18 non-European countries with 100 participating specialists. A national CLG title is available in 60% of European countries, and in 77% of the surveyed European countries a CLG can be the main responsible head of the laboratory performing human genetic tests. However, in only 20% of European countries is a lab-report valid with only a CLGs' signature - even though the report is almost always formulated by the CLG, and an interpretation of the obtained results in a clinical context by the CLG is expected in nearly 90% of European countries. Interestingly, CLGs see patients in 30% of European countries, and are also regularly involved in student education. Overall, the CLG profession includes numerous duties, which are quite similar in all regions of the world. Strikingly, the CLG's rights and responsibilities of leading a lab, or signing a report are regulated differently according to country specific regulations. Overall, the CLG is a well-recognized profession worldwide and often working within a multidisciplinary team of human genetic diagnostics professionals.

Characterization of interfacial socket pressure in transhumeral prostheses: A case series.

One of the most important factors in successful upper limb prostheses is the socket design. Sockets must be individually fabricated to arrive at a geometry that suits the user's morphology and appropriately distributes the pressures associated with prosthetic use across the residual limb. In higher levels of amputation, such as transhumeral, this challenge is amplified as prosthetic weight and the physical demands placed on the residual limb are heightened. Yet, in the upper limb, socket fabrication is largely driven by heuristic practices. An analytical understanding of the interactions between the socket and residual limb is absent in literature. This work describes techniques, adapted from lower limb prosthetic research, to empirically characterize the pressure distribution occurring between the residual limb and well-fit transhumeral prosthetic sockets. A case series analyzing the result of four participants with transhumeral amputation is presented. A Tekscan VersaTek pressure measurement system and FaroArm Edge coordinate measurement machine were employed to capture socket-residual limb interface pressures and geometrically register these values to the anatomy of participants. Participants performed two static poses with their prosthesis under two separate loading conditions. Surface pressure maps were constructed from the data, highlighting pressure distribution patterns, anatomical locations bearing maximum pressure, and the relative pressure magnitudes. Pressure distribution patterns demonstrated unique characteristics across the four participants that could be traced to individual socket design considerations. This work presents a technique that implements commercially available tools to quantitatively characterize upper limb socket-residual limb interactions. This is a fundamental first step toward improved socket designs developed through informed, analytically-based design tools.

Identification of anhydroecgonine ethyl ester in the urine of a drug overdose victim.

Toxicological evaluation of postmortem urine collected from a 41-year-old deceased white male detected anhydroecgonine ethyl ester (ethylecgonidine, AEEE), a transesterification product of smoked cocaine co-abused with ethanol. A solid phase extraction (SPE) method was used to extract cocaine, AEEE, and related metabolites from urine. SPE on a 1 mL urine sample from the decedent followed by GC-MS detected AEEE. Other metabolites identified by GC-MS included cocaine, cocaethylene, and anhydroecgonine methyl ester (AEME). To determine whether some or all of the AEEE was artifactually produced in the heated GC injector port, an alternative LC-MS method was developed. LC/MS following SPE found at least 50 ng/mL of AEEE in the extract. The mass fragmentation (MS/MS and MS3) of AEEE detected in the urine was compared to spectra of authentic, synthesized compound. AEEE is a potential additional forensic marker for the co-abuse of smoked cocaine and ethanol.

White-faced monkey (Cebus capucinus) ecology and management in neotropical agricultural landscapes during the dry season

Habitat use by a C. capucinus troop was studied in an agricultural landscape during late dry season (March-April 1994) in northwest Costa Rica. Riparian forests, palm canals and living fence rows accounted for 82 of observations, significantly more than the other six habitats present. The study troop consumed 24 species of plants and five animals. Feeding concentrated on the introduced African oil palm (Elaeis guineensis) (33.6) and mango (Mangifera indica) (27.2), found mostly in palm canals and mango orchards respectively. The troop rested between 0930-1330 hr and fed and moved between 0530-0930 hr and 1330-1730 hr. Living fence rows were used as travel routes or corridors and less intensively for other activities.