A performance evaluation of commercially available and 3D-printable prosthetic hands: a comparison using the anthropomorphic hand assessment protocol.

Despite significant technological progress in prosthetic hands, a device with functionality akin to a biological extremity is far from realization. To better support the development of next-generation technologies, we investigated the grasping capabilities of clinically prescribable and commercially available (CPCA) prosthetic hands against those that are 3D-printed, which offer cost-effective and customizable solutions. Our investigation utilized the Anthropomorphic Hand Assessment Protocol (AHAP) as a benchtop evaluation of the multi-grasp performance of 3D-printed devices against CPCA prosthetic hands.

Preliminary Evaluation of the Effect of Mechanotactile Feedback Location on Myoelectric Prosthesis Performance Using a Sensorized Prosthetic Hand.

A commonly cited reason for the high abandonment rate of myoelectric prostheses is a lack of grip force sensory feedback. Researchers have attempted to restore grip force sensory feedback by stimulating the residual limb's skin surface in response to the prosthetic hand's measured grip force. Recent work has focused on restoring natural feedback to the missing digits directly through invasive surgical procedures.

Examining the Use of Temporal-Difference Incremental Delta-Bar-Delta for Real-World Predictive Knowledge Architectures.

Predictions and predictive knowledge have seen recent success in improving not only robot control but also other applications ranging from industrial process control to rehabilitation. A property that makes these predictive approaches well-suited for robotics is that they can be learned online and incrementally through interaction with the environment. However, a remaining challenge for many prediction-learning approaches is an appropriate choice of prediction-learning parameters, especially parameters that control the magnitude of a learning machine's updates to its predictions (the or ).

Defining the design requirements for an assistive powered hand exoskeleton: A pilot explorative interview study and case series.

Background: Powered hand exoskeletons are an emerging technology that have shown promise in assisting individuals with impaired hand function. A number of hand exoskeleton designs have been described in the literature; however, the majority have not been supported by patient-oriented criteria.

Objective: The aim of this study was to define preliminary end-user needs and expectations for an assistive hand exoskeleton.

The Effect of an Automatically Levelling Wrist Control System.

Upper limb loss is a devastating injury for which current prosthetic replacement inadequately compensates. A lack of wrist movement in prostheses due to mechanical design and control system considerations compels prosthetic users to employ compensatory movements using their upper back and shoulder that can eventually result in strain and overuse injuries. One possible means of easing this control burden is to allow a prosthetic wrist to self-regulate, keeping the terminal device of the prosthesis level relative to the ground when appropriate, such as when raising a cup of liquid.

Real time monitoring of transtibial elevated vacuum prostheses: A case series on socket air pressure.

Prosthetic elevated vacuum is a suspension method used to reduce daily volume changes of the residual limb. Evaluation of the effectiveness of these systems is limited due to a lack of correlation to actual socket air pressure, particularly during unconstrained movements. This may explain some of the variability in functional outcomes reported in the literature.

Design and Integration of an Inexpensive Wearable Mechanotactile Feedback System for Myoelectric Prostheses.

The aim of this paper was to demonstrate the functionality of an inexpensive mechanotactile sensory feedback system for transhumeral myoelectric prostheses. We summarize the development of a tactile-integrated prosthesis, including 1) evaluation of sensors that were retrofit onto existing commercial terminal devices; 2) design of two custom mechanotactile tactors that were integrated into a socket without compromising suction suspension; 3) design of a modular controller which translated sensor input to tactor output, was wirelessly adjusted, and fit within a prosthetic forearm; and 4) evaluation of the system with a single transhumeral participant. Prosthesis functionality was demonstrated over three test sessions; the participant was able to identify tactor stimulation location and demonstrated a reduction in grasp force with the mechanotactile stimulation.

Illusory movement perception improves motor control for prosthetic hands.

To effortlessly complete an intentional movement, the brain needs feedback from the body regarding the movement's progress. This largely nonconscious kinesthetic sense helps the brain to learn relationships between motor commands and outcomes to correct movement errors. Prosthetic systems for restoring function have predominantly focused on controlling motorized joint movement.

Probability matching in perceptrons: Effects of conditional dependence and linear nonseparability.

Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent's environment. For instance, when artificial neural networks match probability, the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which consist of a set of input units directly connected to a single output unit) learn to match probability when presented different cues in isolation.

Cutaneous sensory outcomes from three transhumeral targeted reinnervation cases.

Background: Although targeted muscle reinnervation has been shown to be effective in enhancing prosthetic control for upper limb amputees, restored hand sensations have been variable. An understanding of possible sensory feedback channels is crucial in working toward more effective closed-loop prosthetic control.

Objectives: To compare sensory outcomes of different targeted sensory reinnervation approaches.

Application of real-time machine learning to myoelectric prosthesis control: A case series in adaptive switching.

Background: Myoelectric prostheses currently used by amputees can be difficult to control. Machine learning, and in particular learned predictions about user intent, could help to reduce the time and cognitive load required by amputees while operating their prosthetic device.

Objectives: The goal of this study was to compare two switching-based methods of controlling a myoelectric arm: non-adaptive (or conventional) control and adaptive control (involving real-time prediction learning).

Characterizing the effects of amplitude, frequency and limb position on vibration induced movement illusions: Implications in sensory-motor rehabilitation.

Background: Strategic vibration of musculotendinous regions of a limb elicits illusionary sensations of movement. As a rehabilitation technique, this 'kinesthetic illusion' has demonstrated beneficial results for numerous sensory-motor disorders. However, literature shows little consistency in the vibration parameters or body positioning used, and their effects have yet to be comprehensively investigated.

Novel targeted sensory reinnervation technique to restore functional hand sensation after transhumeral amputation.

We present a case study of a novel variation of the targeted sensory reinnervation technique that provides additional control over sensory restoration after transhumeral amputation. The use of intraoperative somatosensory evoked potentials on individual fascicles of the median and ulnar nerves allowed us to specifically target sensory fascicles to reroute to target cutaneous nerves at a distance away from anticipated motor sites in a transhumeral amputee. This resulted in restored hand maps of the median and ulnar nerve in discrete spatially separated areas.

Get out of the corner: Inhibition and the effect of location type and number on perceptron and human reorientation.

Spatial learning and navigation have frequently been investigated using a reorientation task paradigm (Cheng, Cognition, 23(2), 149-78, 1986). However, implementing this task typically involves making tacit assumptions about the nature of spatial information. This has important theoretical consequences: Theories of reorientation typically focus on angles at corners as geometric cues and ignore information present at noncorner locations.

Differentiating models of associative learning: reorientation, superconditioning, and the role of inhibition.

A recent associative model (Miller, N.Y., & Shettleworth, S.

A case study in Gantt charts as historiophoty: A century of psychology at the University of Alberta.

History is typically presented as historiography, where historians communicate via the written word. However, some historians have suggested alternative formats for communicating and thinking about historical information. One such format is known as historiophoty, which involves using a variety of visual images to represent history.

Equilibria of perceptrons for simple contingency problems.

The contingency between cues and outcomes is fundamentally important to theories of causal reasoning and to theories of associative learning. Researchers have computed the equilibria of Rescorla-Wagner models for a variety of contingency problems, and have used these equilibria to identify situations in which the Rescorla-Wagner model is consistent, or inconsistent, with normative models of contingency. Mathematical analyses that directly compare artificial neural networks to contingency theory have not been performed, because of the assumed equivalence between the Rescorla-Wagner learning rule and the delta rule training of artificial neural networks.

The development of a myoelectric training tool for above-elbow amputees.

The objective of above-elbow myoelectric prostheses is to reestablish the functionality of missing limbs and increase the quality of life of amputees. By using electromyography (EMG) electrodes attached to the surface of the skin, amputees are able to control motors in myoelectric prostheses by voluntarily contracting the muscles of their residual limb. This work describes the development of an inexpensive myoelectric training tool (MTT) designed to help upper limb amputees learn how to use myoelectric technology in advance of receiving their actual myoelectric prosthesis.

Online human training of a myoelectric prosthesis controller via actor-critic reinforcement learning.

As a contribution toward the goal of adaptable, intelligent artificial limbs, this work introduces a continuous actor-critic reinforcement learning method for optimizing the control of multi-function myoelectric devices. Using a simulated upper-arm robotic prosthesis, we demonstrate how it is possible to derive successful limb controllers from myoelectric data using only a sparse human-delivered training signal, without requiring detailed knowledge about the task domain. This reinforcement-based machine learning framework is well suited for use by both patients and clinical staff, and may be easily adapted to different application domains and the needs of individual amputees.

Myoelectric training systems.

Myoelectric prostheses aim to restore the functionality of amputated limbs and improve the quality of life of amputees. Myoelectric training systems are used to train and assess the ability of amputees in how to use myoelectric technology in advance of receiving their actual myoelectric prostheses. This article describes the myoelectric training systems that have been developed over the last 20 years in both the literature and commercial industries.

Development of a contact call in black-capped chickadees (Poecile atricapillus) hand-reared in different acoustic environments.

The tseet contact call, common to black-capped (Poecile atricapillus) and mountain chickadees (P. gambeli), is the most frequently produced vocalization of each species. Previous work has characterized the tseet call of black-capped and mountain chickadees from different geographic locations in terms of nine acoustic features.

Mechanisms of call note-type perception in black-capped chickadees (Poecile atricapillus): peak shift in a note-type continuum.

We report on operant conditioning and artificial neural network (ANN) simulations aimed at further elucidating mechanisms of black-capped chickadee chick-a-dee call note category perception. Specifically, we tested for differences in the speed of acquisition among different discrimination tasks and, in two selected discrimination groups, searched for evidence of peak shift. Earlier, unreported ANN data were instrumental in providing the motivation for the current set of studies with chickadees and are provided here.

Black-capped (Poecile atricapillus) and mountain chickadee (Poecile gambeli) contact call contains species, sex, and individual identity features.

The tseet contact call, common to both black-capped and mountain chickadees, is among the most frequently produced call of each species, but has remained little studied until now. In the current study, the authors characterized the tseet call of adult allopatric and sympatric black-capped and mountain chickadees in terms of nine acoustic features in a fashion similar to descriptive accounts of both species' chick-a-dee calls. Summary statistics, the potential for individual coding, and classification by linear discriminant analysis were used to describe the tseet call.

Using perceptrons to explore the reorientation task.

The reorientation task is a paradigm that has been used extensively to study the types of information used by humans and animals to navigate in their environment. In this task, subjects are reinforced for going to a particular location in an arena that is typically rectangular in shape. The subject then has to find that location again after being disoriented, and possibly after changes have been made to the arena.

Simple artificial neural networks that match probability and exploit and explore when confronting a multiarmed bandit.

The matching law (Herrnstein 1961) states that response rates become proportional to reinforcement rates; this is related to the empirical phenomenon called probability matching (Vulkan 2000). Here, we show that a simple artificial neural network generates responses consistent with probability matching. This behavior was then used to create an operant procedure for network learning.