Partial hand test fixture


Improving Control of Robotic Hand Prostheses


Partial-hand amputation is the most common form of upper limb amputation, affecting an estimated 500,000 individuals in the United States as of 2005. Between 1988 and 1999, more than 150,000 Americans had amputations through the palm of the hand (1.5%), of the thumb (18%), or of one or more digits.

The most frequent cause of partial-hand amputation is trauma, often a result of work- or leisure activity–related accidents or combat-related injury. Thus, the majority of partial-hand amputations occur in relatively young, active individuals.

Loss of part of the hand impairs the ability to make functional hand-grasps, which are essential for eating and dressing, for leisure activities, and for performing job-related tasks. Notably, over half of partial-hand amputees are unable to return to previous employment, and most must modify their work duties because their prosthesis lacks sufficient function. In fact, partial-hand amputees often perceive themselves to be at a higher level of disability than do individuals with arm amputations at the elbow or shoulder level.

New, powered partial-hand prostheses that can perform many of the functions of an intact hand are becoming available; however, controlling these devices is problematic. Typically, users control their prosthesis using electrical (electromyographic or EMG) signals generated by muscles in the hand (the intrinsic hand muscles). However, the intrinsic muscles are small and are often damaged by the amputation, so obtaining usable EMG signals can be difficult. This method also only allows the user to control one hand movement; a switching mechanism must be used to control additional movements.

Muscles in the forearm (called extrinsic hand muscles) also control movement of the hand and digits; however, these muscles lie close together, making it difficult to get independent EMG signals; again, the user can only control one hand movement. In addition, the extrinsic muscles lie close to the muscles that move the wrist, so using EMG from extrinsic hand muscles to control a prosthesis means the user has to keep their wrist still—preventing the user from positioning the hand appropriately during tasks.

A solution to this problem is to use pattern recognition technology to decode EMG signals from the extrinsic muscles. Any attempted movement of the hand or digits creates a pattern of EMG signals that is specific for that hand movement. A computer algorithm can learn these patterns and determine what movement the user intends to make. The user can thus control the prosthesis—and perform a variety of hand movements—just by attempting to make the desired movement.

Our research goal is to develop a pattern recognition algorithm that enables accurate control of a multifunction partial-hand prosthesis using EMG from extrinsic muscles while still allowing the user to move their wrist. We will also develop training protocols that enable the user to perform different hand movements simultaneously, which is not possible with current control systems.

Our control system will enable users to intuitively and easily control new generation multifunctional partial-hand prostheses. In addition, an advanced control system will provide impetus for design of even more functional devices—bringing technology closer to replacing the dexterity of the intact human hand.


Related Publications


Adewuyi AA, Hargrove LJ, and Kuiken TA. Evaluating EMG Feature and Classifier Selection for Application to Partial-Hand Prosthesis ControlFrontiers in Neurorobotics 10:15, 2016.

Earley EJ, Hargrove LJ, and Kuiken TA. Dual window pattern recognition classifier for improved partial-hand prosthesis controlFrontiers in Neuroscience, 10:58, 2016.

Adewuyi A, Hargrove L, and Kuiken TA. An Analysis of Intrinsic and Extrinsic Hand Muscle EMG for Improved Pattern Recognition ControlIEEE Trans Neural Syst Rehabil Eng, May 6, 2015

Earley, Eric J., Adenike A. Adewuyi, and Levi J. Hargrove. Optimizing pattern recognition-based control for partial-hand prosthesis applicationEngineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE. IEEE, 2014.

Adewuyi, Adenike A., Levi J. Hargrove, and Todd A. Kuiken. Towards improved partial-hand prostheses: The effect of wrist kinematics on pattern-recognition-based controlNeural Engineering (NER), 2013 6th International IEEE/EMBS Conference on. IEEE, 2013.

Birdwell J, Hargrove L, Kuiken T, and Weir R. Isolated Activation of the Extrinsic Thumb Muscles and Compartments of the Extrinsic Finger MusclesJ Neurophysiol, 110(6): 1385-1392. 2013.


The contents of this webpage were developed under a grant from the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR grant number 90RE5014-02-00). NIDILRR is a Center within the Administration for Community Living (ACL), Department of Health and Human Services (HHS). The contents of this webpage do not necessarily represent the policy of NIDILRR, ACL, HHS, and you should not assume endorsement by the Federal Government.

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