An Interface between an Exoskeletal Elbow Motion Assistance Robot and the Human Upper Arm

2002 ◽  
Vol 14 (5) ◽  
pp. 439-452 ◽  
Author(s):  
Kazuo Kiguchi ◽  
◽  
Shingo Kariya ◽  
Takakazu Bnaka ◽  
Keigo Watanabe ◽  
...  
Keyword(s):  
Surface Electromyography ◽  
Elbow Joint ◽  
Human Subject ◽  
Angular Position ◽  
Skin Surface ◽  
Human Motion ◽  
Daily Activities ◽  
Joint Motion ◽  
Upper Arm ◽  
Elbow Motion

We have been developing exoskeletal motion assistance robots for human motion support to help physically weak persons. Since elbow motion is one of the simplest and most important motion in daily activities, we have developed a exoskeletal robot for human elbow motion assistance. In this system, the angular position and impedance of the exoskeletal robot are controlled by multiple fuzzy-neuro controllers. Skin surface electromyography (EMG) signals and wrist force by the human subject during elbow joint motion have been used as input information for the controller. Since the activation of working muscles tends to vary with the human subject's upper arm posture, we propose an interface that cancels out the effect of posture changes of the human subject's upper arm. Experimental results show the effectiveness of the proposed interface.

Electrogoniometer for the Measurement of Human Elbow Joint Rotation

1980 ◽  
Vol 102 (4) ◽  
pp. 301-310 ◽  
Author(s):  
E. Y. Chao ◽  
K. N. An ◽  
L. J. Askew ◽  
B. F. Morrey
Keyword(s):  
Elbow Joint ◽  
Daily Living ◽  
Model Simulation ◽  
Normal Subjects ◽  
Functional Evaluation ◽  
Joint Motion ◽  
Forearm Rotation ◽  
Joint Rotation ◽  
Present Apparatus ◽  
Elbow Motion

Since the electrogoniometric method has been justified for the measurement of lower extremity joint motion, a similar device is developed for the measurement of elbow joint and forearm rotations. In this design, the axis of forearm rotation coincides with the anatomical axis which eliminates the cross talk existing in the regular triaxial goniometer. Although the axis of abduction-adduction is still offset from the elbow joint, special linkage arrangement was used to obtain equivalent motion. Experimental method was used to validate the accuracy of the device and model simulation was performed to emphasize the importance of accurate placement of the instrument on test subjects. Application of the present apparatus to normal subjects was studied to illustrate the range of elbow motion required in performing normal activities of daily living. This device is currently used in the functional evaluation of patients with elbow and forearm problems.

Modeling the Human Elbow Joint Dynamics from Surface Electromyography

2014 ◽  
pp. 114-128 ◽  
Author(s):  
Andrés Felipe Ruiz-Olaya
Keyword(s):  
Postural Control ◽  
Surface Electromyography ◽  
Elbow Joint ◽  
Mechanical Impedance ◽  
External Perturbation ◽  
Human Movement ◽  
Biomechanical Modelling ◽  
Joint Dynamics ◽  
Human Motor ◽  
Human Joint

Biomechanical modelling and analysis of human motion are main topics of interest for a number of disciplines, ranging from biomechanics to human movement science. There exist various experimental and theoretical techniques developed to model the biomechanics and human motor system. A classic way to characterize a system is done by perturbation analysis, through applying an external perturbation and the observation of changes in the dynamic of system. In literature, human joint dynamics has been studied mainly in relation to external perturbations. However, those perturbations interact with the natural human motor behaviour. This chapter describes an approximation for non-invasive biomechanical modelling of the elbow joint dynamics from electromyographic information. A case study presents results obtained aimed at deriving a relationship between the dynamic behaviour of the human elbow joint and Surface Electromyography (SEMG) information in postural control. A set of experiments were carried out to measure bioelectrical (SEMG) and biomechanics information from human elbow joint, during postural control (i.e. isometric contractions) and correlate them with mechanical impedance at elbow joint. Estimates of elbow impedance were obtained by applying torque perturbations to the forearm. The results demonstrate that it is possible to estimate human joint dynamics from SEMG. The obtained results can contribute to the field of human motor control and also to its application in robotics and other engineering applications through the definition, specification and characterization of properties associated with the human upper limb and strategies used by people to command it.

scholarly journals Movement Estimation Using Soft Sensors Based on Bi-LSTM and Two-Layer LSTM for Human Motion Capture

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1801 ◽  
Author(s):  
Haitao Guo ◽  
Yunsick Sung
Keyword(s):  
Motion Capture ◽  
Short Term Memory ◽  
Arm Movements ◽  
Ground Truth ◽  
Human Movement ◽  
Human Motion ◽  
Soft Sensors ◽  
Upper Arm ◽  
Human Motion Capture ◽  
Gesture Control

The importance of estimating human movement has increased in the field of human motion capture. HTC VIVE is a popular device that provides a convenient way of capturing human motions using several sensors. Recently, the motion of only users’ hands has been captured, thereby greatly reducing the range of motion captured. This paper proposes a framework to estimate single-arm orientations using soft sensors mainly by combining a Bi-long short-term memory (Bi-LSTM) and two-layer LSTM. Positions of the two hands are measured using an HTC VIVE set, and the orientations of a single arm, including its corresponding upper arm and forearm, are estimated using the proposed framework based on the estimated positions of the two hands. Given that the proposed framework is meant for a single arm, if orientations of two arms are required to be estimated, the estimations are performed twice. To obtain the ground truth of the orientations of single-arm movements, two Myo gesture-control sensory armbands are employed on the single arm: one for the upper arm and the other for the forearm. The proposed framework analyzed the contextual features of consecutive sensory arm movements, which provides an efficient way to improve the accuracy of arm movement estimation. In comparison with the ground truth, the proposed method estimated the arm movements using a dynamic time warping distance, which was the average of 73.90% less than that of a conventional Bayesian framework. The distinct feature of our proposed framework is that the number of sensors attached to end-users is reduced. Additionally, with the use of our framework, the arm orientations can be estimated with any soft sensor, and good accuracy of the estimations can be ensured. Another contribution is the suggestion of the combination of the Bi-LSTM and two-layer LSTM.

Daily Activities Classification on Human Motion Primitives Detection Dataset

2018 ◽  
pp. 117-125 ◽  
Author(s):  
Zi Hau Chin ◽  
Hu Ng ◽  
Timothy Tzen Vun Yap ◽  
Hau Lee Tong ◽  
Chiung Ching Ho ◽  
...  
Keyword(s):  
Human Motion ◽  
Daily Activities ◽  
Motion Primitives

Control System for an Elbow Joint Motion Simulator

2007 ◽  
Author(s):  
Patrick J. Schimoler ◽  
Jeffrey S. Vipperman ◽  
Laurel Kuxhaus ◽  
Angela M. Flamm ◽  
Daniel D. Budny ◽  
...  
Keyword(s):  
Control System ◽  
Elbow Joint ◽  
Degrees Of Freedom ◽  
Biceps Brachii ◽  
Control Algorithms ◽  
Joint Motion ◽  
Motion Simulator ◽  
Co Activation ◽  
Flexion Extension ◽  
The Many

The many muscles crossing the elbow joint allow for its motions to be created from different combinations of muscular activations. Muscles are strictly contractile elements and the joints they surround rely on varying loads from opposing antagonists for stability and movement. In designing a control system to actuate an elbow in a realistic manner, unidirectional, tendon-like actuation and muscle co-activation must be considered in order to successfully control the elbow’s two degrees of freedom. Also important is the multifunctionality of certain muscles, such as the biceps brachii, which create moments impacting both degrees of freedom: flexion / extension and pronation / supination. This paper seeks to develop and implement control algorithms on an elbow joint motion simulator that actuates cadaveric elbow specimens via four major muscles that cross the elbow joint. The algorithms were validated using an anatomically-realistic mechanical elbow. Clinically-meaningful results, such as the evaluation of radial head implants, can only be obtained under repeatable, realistic conditions; therefore, physiologic motions must be created by the application of appropriate loads. This is achieved by including load control on the muscles’ actuators as well as displacement control on both flexion / extension and supination / pronation.

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