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

Segil, Jacob L.; Weir, Richard F.

doi:10.1109/tnsre.2013.2260172

Cited by 34 publications

(26 citation statements)

References 23 publications

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“…Previously, we found that a specific mapping of the EMG signals in the PC domain augmented the ability of subjects to drive a VH into functional postures when using the principal components derived by Santello et al [31]. This work builds upon that finding by developing a novel algorithm for a postural controller that is not dependent on PCA to derive the postural vectors.…”

Section: Introductionmentioning

confidence: 65%

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Novel postural control algorithm for control of multifunctional myoelectric prosthetic hands

Segil

Weir

2015

J Rehabil Res Dev

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Abstract-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 twodimensional 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 h 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.

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Section: Introductionmentioning

confidence: 65%

“…The postural controller presented here integrates several novel aspects with respect to previous work [24][25][26]31]. Here, the postural map is fully customizable due to the novel derivation of the JAT.…”

Section: Novel Aspectsmentioning

confidence: 99%

Novel postural control algorithm for control of multifunctional myoelectric prosthetic hands

Segil

Weir

2015

J Rehabil Res Dev

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“…Controller 3 (C3) was a postural controller based on previous work by the authors (Figure 3) [14]. * The architecture used EMG signals like a joystick to morph the hand posture.…”

Section: Controller 3: Postural Controllermentioning

confidence: 99%

“…For example, an intuitive interface would measure the contraction from the flexor or extensor digitorum muscles in order to close or open the digits of a prosthetic hand [6]. MECs for multigrasp prostheses implement different control architectures such as pattern recognition [7][8][9], finite state machines [10][11][12], and postural control (PC) schemes [13][14][15] that transform user intent in the form of EMG signals to motor commands sent to a prosthesis. Pattern recognition systems exploit artificial intelligence algorithms (such as artificial neural networks, fuzzy logic algorithms, support vector machines, etc.)…”

Section: Introductionmentioning

confidence: 99%

“…Thus, in this work we compared two types of finite state machines and a PC scheme using both virtual and physical assessment procedures. We used the Southampton Hand Assessment Procedure (SHAP) [18] in order to study the effectiveness of each MEC to perform ADLs with a physical multigrasp artificial hand as well as a virtual hand posture-matching task in order to measure the ability of subjects to reproduce six target postures [14].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2014

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Abstract-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 when using 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.

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Neuromorphic Model of Reflex for Realtime Human-Like Compliant Control of Prosthetic Hand

et al. 2020

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Current control of prosthetic hands is ineffective when grasping deformable, irregular, or heavy objects. In humans, grasping is achieved under spinal reflexive control of the musculotendon skeletal structure, which produces a hand stiffness commensurate with the task. We hypothesize that mimicking reflex on a prosthetic hand may improve grasping performance and safety when interacting with human. Here, we present a design of compliant controller for prosthetic hand with a neuromorphic model of human reflex. The model includes 6 motoneuron pools containing 768 spiking neurons, 1 muscle spindle with 128 spiking afferents, and 1 modified Hill-type muscle. Models are implemented using neuromorphic hardware with 1 kHz real-time computing. Experimental tests showed that the prosthetic hand could sustain a 40 N load compared to 95 N for an adult. Stiffness range was adjustable from 60 to 640 N/m, about 46.6% of that of human hand. The grasping velocity could be ramped up to 14.4 cm/s, or 24% of the human peak velocity. The complaint control could switch between free movement and contact force when pressing a deformable beam. The amputee can achieve a 47% information throughput of healthy humans. Overall, the reflex-enabled prosthetic hand demonstrated the attributes of human compliant grasping with the neuromorphic model of spinal neuromuscular reflex.

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

Cited by 34 publications

References 23 publications

Novel postural control algorithm for control of multifunctional myoelectric prosthetic hands

Novel postural control algorithm for control of multifunctional myoelectric prosthetic hands

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

Neuromorphic Model of Reflex for Realtime Human-Like Compliant Control of Prosthetic Hand

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