Synergistic Elbow Control for a Myoelectric Transhumeral Prosthesis

Alshammary, Nasser A.; Bennett, Daniel A.; Goldfarb, Michael

doi:10.1109/tnsre.2017.2781719

Cited by 21 publications

(22 citation statements)

References 17 publications

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“…While this may not be surprising, this reaffirms the potential interchangeability of the two reference frames; an important feature in modeling the world space in mobile robots and prosthetics. One application is the implementation of an IMU in a prosthetic device [46] to orient it either with respect to gravity or the torso, which may include other hardware considerations.…”

Section: Discussionmentioning

confidence: 99%

Dimensionality Reduction and Motion Clustering During Activities of Daily Living: Decoupling Hand Location and Orientation

Gloumakov

Spiers

Dollar

2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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This paper is the second in a two-part series analyzing human arm and hand motion during a wide range of unstructured tasks. In this work, we track the hand of healthy individuals as they perform a variety of activities of daily living (ADLs) in three ways decoupled from hand orientation: end-point locations of the hand trajectory, whole path trajectories of the hand, and straight-line paths generated using start and end points of the hand. These data are examined by a clustering procedure to reduce the wide range of hand use to a smaller representative set. Hand orientations are subsequently analyzed for the end-point location clustering results and subsets of orientations are identified in three reference frames: global, torso, and forearm. Data driven methods that are used include dynamic time warping (DTW), DTW barycenter averaging (DBA), and agglomerative hierarchical clustering with Ward's linkage. Analysis of the endpoint locations, path trajectory, and straight-line path trajectory identified 5, 5, and 7 ADL task categories, respectively, while hand orientation analysis identified up to 4 subsets of orientations for each task location, discretized and classified to the facets of a rhombicuboctahedron. Together these provide insight into our hand usage in daily life and inform an implementation in prosthetic or robotic devices using sequential control.

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Section: Discussionmentioning

confidence: 99%

Dimensionality Reduction and Motion Clustering During Activities of Daily Living: Decoupling Hand Location and Orientation

Gloumakov

Spiers

Dollar

2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Signal acquisition and processing are a great challenge in the control of above elbow amputation due to few or no amount of residual muscle and weak muscle activity [ 4 , 10 , 11 ]. Furthermore, remaining muscle sites for the prosthetic control are not physiologically identified to the distal arm functions [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees

Sattar

Kausar

Usama

et al. 2022

Sensors

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Prosthetic arms are designed to assist amputated individuals in the performance of the activities of daily life. Brain machine interfaces are currently employed to enhance the accuracy as well as number of control commands for upper limb prostheses. However, the motion prediction for prosthetic arms and the rehabilitation of amputees suffering from transhumeral amputations is limited. In this paper, functional near-infrared spectroscopy (fNIRS)-based approach for the recognition of human intention for six upper limb motions is proposed. The data were extracted from the study of fifteen healthy subjects and three transhumeral amputees for elbow extension, elbow flexion, wrist pronation, wrist supination, hand open, and hand close. The fNIRS signals were acquired from the motor cortex region of the brain by the commercial NIRSport device. The acquired data samples were filtered using finite impulse response (FIR) filter. Furthermore, signal mean, signal peak and minimum values were computed as feature set. An artificial neural network (ANN) was applied to these data samples. The results show the likelihood of classifying the six arm actions with an accuracy of 78%. The attained results have not yet been reported in any identical study. These achieved fNIRS results for intention detection are promising and suggest that they can be applied for the real-time control of the transhumeral prosthesis.

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“…The measure of performance used for personalisation defines the prosthetic's objective, while the human has its own internal measure of performance and thus objective. In collaborative human-robot applications, such as motionbased synergistic prosthesis [5], [6], [7], it is required that these objectives align, i.e. that both the human and the robot have the same objective functions [1], [8].…”

Section: Introductionmentioning

confidence: 99%

The Use of Implicit Human Motor Behaviour in the Online Personalisation of Prosthetic Interfaces

Garcia-Rosas,

Yu,

Oetomo

et al. 2020

Preprint

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In [1], the authors proposed a data-driven optimisation algorithm for the personalisation of human-prosthetic interfaces, demonstrating the possibility of adapting prosthesis behaviour to its user while the user performs tasks with it. This method requires that the human and the prosthesis personalisation algorithm have same pre-defined objective function. This was previously ensured by providing the human with explicit feedback on what the objective function is. However, constantly displaying this information to the prosthesis user is impractical. Moreover, the method utilised task information in the objective function which may not be available from the wearable sensors typically used in prosthetic applications. In this work, the previous approach is extended to use a prosthesis objective function based on implicit human motor behaviour, which represents able-bodied human motor control and is measureable using wearable sensors. The approach is tested in a hardware implementation of the personalisation algorithm on a prosthetic elbow, where the prosthetic objective function is a function of upper-body compensation, and is measured using wearable IMUs. Experimental results on able-bodied subjects using a supernumerary prosthetic elbow mounted on an elbow orthosis suggest that it is possible to use a prosthesis objective function which is implicit in human behaviour to achieve collaboration without providing explicit feedback to the human, motivating further studies.

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Synergistic Elbow Control for a Myoelectric Transhumeral Prosthesis

Cited by 21 publications

References 17 publications

Dimensionality Reduction and Motion Clustering During Activities of Daily Living: Decoupling Hand Location and Orientation

Dimensionality Reduction and Motion Clustering During Activities of Daily Living: Decoupling Hand Location and Orientation

fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees

The Use of Implicit Human Motor Behaviour in the Online Personalisation of Prosthetic Interfaces

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