Jacob L. Segil

Mechanical design and performance specifications of anthropomorphic prosthetic hands: A review

In this article, we set forth a detailed analysis of the mechanical characteristics of anthropomorphic prosthetic hands. We report on an empirical study concerning the performance of several commercially available myoelectric prosthetic hands, including the Vincent, iLimb, iLimb Pulse, Bebionic, Bebionic v2, and Michelangelo hands. We investigated the finger design and kinematics, mechanical joint coupling, and actuation methods of these commercial prosthetic hands. The empirical findings are supplemented with a compilation of published data on both commercial and prototype research prosthetic hands. We discuss numerous mechanical design parameters by referencing examples in the literature. Crucial design trade-offs are highlighted, including number of actuators and hand complexity, hand weight, and grasp force. Finally, we offer a set of rules of thumb regarding the mechanical design of anthropomorphic prosthetic hands.

Dexterous Control of a Prosthetic Hand Using Fine-Wire Intramuscular Electrodes in Targeted Extrinsic Muscles

Restoring dexterous motor function equivalent to that of the human hand after amputation is one of the major goals in rehabilitation engineering. To achieve this requires the implementation of a effortless human-machine interface that bridges the artificial hand to the sources of volition. Attempts to tap into the neural signals and to use them as control inputs for neuroprostheses range in invasiveness and hierarchical location in the neuromuscular system. Nevertheless today, the primary clinically viable control technique is the electromyogram measured peripherally by surface electrodes. This approach is neither physiologically appropriate nor dexterous because arbitrary finger movements or hand postures cannot be obtained. Here we demonstrate the feasibility of achieving real-time, continuous and simultaneous control of a multi-digit prosthesis directly from forearm muscles signals using intramuscular electrodes on healthy subjects. Subjects contracted physiologically appropriate muscles to control four degrees of freedom of the fingers of a physical robotic hand independently. Subjects described the control as intuitive and showed the ability to drive the hand into 12 postures without explicit training. This is the first study in which peripheral neural correlates were processed in real-time and used to control multiple digits of a physical hand simultaneously in an intuitive and direct way.

Humans can integrate feedback of discrete events in their sensorimotor control of a robotic hand

Abstract Providing functionally effective sensory feedback to users of prosthetics is a largely unsolved challenge. Traditional solutions require high band-widths for providing feedback for the control of manipulation and yet have been largely unsuccessful. In this study, we have explored a strategy that relies on temporally discrete sensory feedback that is technically simple to provide. According to the Discrete Event-driven Sensory feedback Control (DESC) policy, motor tasks in humans are organized in phases delimited by means of sensory encoded discrete mechanical events. To explore the applicability of DESC for control, we designed a paradigm in which healthy humans operated an artificial robot hand to lift and replace an instrumented object, a task that can readily be learned and mastered under visual control. Assuming that the central nervous system of humans naturally organizes motor tasks based on a strategy akin to DESC, we delivered short-lasting vibrotactile feedback related to events that are known to forcefully affect progression of the grasp-lift-and-hold task. After training, we determined whether the artificial feedback had been integrated with the sensorimotor control by introducing short delays and we indeed observed that the participants significantly delayed subsequent phases of the task. This study thus gives support to the DESC policy hypothesis. Moreover, it demonstrates that humans can integrate temporally discrete sensory feedback while controlling an artificial hand and invites further studies in which inexpensive, noninvasive technology could be used in clever ways to provide physiologically appropriate sensory feedback in upper limb prosthetics with much lower band-width requirements than with traditional solutions.

Design and Validation of a Morphing Myoelectric Hand Posture Controller Based on Principal Component Analysis of Human Grasping

An ideal myoelectric prosthetic hand should have the ability to continuously morph between any posture like an anatomical hand. This paper describes the design and validation of a morphing myoelectric hand controller based on principal component analysis of human grasping. The controller commands continuously morphing hand postures including functional grasps using between two and four surface electromyography (EMG) electrodes pairs. Four unique maps were developed to transform the EMG control signals in the principal component domain. A preliminary validation experiment was performed by 10 nonamputee subjects to determine the map with highest performance. The subjects used the myoelectric controller to morph a virtual hand between functional grasps in a series of randomized trials. The number of joints controlled accurately was evaluated to characterize the performance of each map. Additional metrics were studied including completion rate, time to completion, and path efficiency. The highest performing map controlled over 13 out of 15 joints accurately.

Comparative study of state-of-the-art myoelectric controllers for multigrasp prosthetic hands

A myoelectric controller should provide an intuitive and effective human-machine interface that deciphers user intent in real-time and is robust enough to operate in daily life. Many myoelectric control architectures have been developed, including pattern recognition systems, finite state machines, and more recently, postural control schemes. Here, we present a comparative study of two types of finite state machines and a postural control scheme using both virtual and physical assessment procedures with seven nondisabled subjects. The Southampton Hand Assessment Procedure (SHAP) was used in order to compare the effectiveness of the controllers during activities of daily living using a multigrasp artificial hand. Also, a virtual hand posture matching task was used to compare the controllers when reproducing six target postures. The performance when using the postural control scheme was significantly better (p < 0.05) than the finite state machines during the physical assessment when comparing within-subject averages using the SHAP percent difference metric. The virtual assessment results described significantly greater completion rates (97% and 99%) for the finite state machines, but the movement time tended to be faster (2.7 s) for the postural control scheme. Our results substantiate that postural control schemes rival other state-of-the-art myoelectric controllers.

Novel postural control algorithm for control of multifunctional myoelectric prosthetic hands.

The myoelectric controller (MEC) remains a technological bottleneck in the development of multifunctional prosthetic hands. Current MECs require physiologically inappropriate commands to indicate intent and lack effectiveness in a clinical setting. Postural control schemes use surface electromyography signals to drive a cursor in a continuous two-dimensional domain that is then transformed into a hand posture. Here, we present a novel algorithm for a postural controller and test the efficacy of the system during two experiments with 11 total subjects. In the first experiment, we found that performance increased when a velocity cursor-control technique versus a position cursor-control technique was used. Also, performance did not change when using 3, 4, or 12 surface electrodes. In the second experiment, subjects commanded a six degree-of-freedom virtual hand into seven functional postures without training, with completion rates of 82 +/- 4%, movement times of 3.5 +/- 0.2 s, and path efficiencies of 45 +/- 3%. Subjects retained the ability to use the postural controller at a high level across days after a single 1 hr training session. Our results substantiate the novel algorithm for a postural controller as a robust and advantageous design for a MEC of multifunction prosthetic hands.

Functional Assessment of a Myoelectric Postural Controller and Multi-Functional Prosthetic Hand by Persons With Trans-Radial Limb Loss

The functional assessment of myoelectric control algorithms by persons with amputation promotes the overarching goal of the field of prosthetic limb design: to replace what was lost. However, many studies use experimental paradigms with virtual interfaces and able-bodied subjects that do not capture the challenges of a clinical implementation with an amputee population. A myoelectric control system must be robust to variable physiology, loading effects of the prosthesis on the limb, and limb position effects during dynamic tasks. Here persons with transradial limb loss performed activities of daily living using a postural controller and multi-functional prosthetic hand in order to verify that the postural controller was robust to these clinical challenges. The Southampton Hand Assessment Procedure was performed by persons with limb loss and able-bodied subjects. The results indicate that persons with limb loss and able-limbed subjects achieved the same performance and therefore that the clinical challenges were overcome. Persons with limb loss achieved 55% of physiological hand function on average. Also, the postural controller is compared to other state of the art myoelectric controllers and prosthetic hands previously tested. This work confirms that the postural controller is potentially a clinically-viable method to control myoelectric multi-functional prosthetic hands.

Manipulation Using the “Utah” Prosthetic Hand: The Role of Stiffness in Manipulation

We describe our approach to the IROS “Hand-in-Hand” manipulation challenge using a simple one degree-of-freedom prehensor, which is known to be highly effective in prosthetic applications. The claw consists of two prongs of which only one is mobile, requiring the user to first make contact with the immobile prong to create a constraint and then use the second prong to exert force on the object. Despite its simplicity, this design is able to grasp a wide variety of objects and reliably manipulate them. In particular, stiffness is advantageous both when manipulating very small objects, where force needs to be applied precisely, as well as heavy ones, where forces needs to be exerted without deforming the claw itself. This approach reaches its limitations during tasks that require more degrees of freedom, for example grasping and subsequently actuating scissors. These tasks instead highlight the benefits of compliance and underactuation, stimulating a discussion about trade-offs in hand designs.