Assessment of Comfort During NMES-induced Quadriceps Contractions at Two Knee Joint Angles

2015 ◽  
Vol 7 (5) ◽  
pp. 181-189 ◽  
Author(s):  
Cody B. Bremner ◽  
William R. Holcomb ◽  
Christopher D. Brown ◽  
Michael G. Miller
Keyword(s):  
Knee Joint ◽  
Joint Angles

Information signaled by sensory fibers in medial articular nerve

1975 ◽  
Vol 38 (6) ◽  
pp. 1464-1472 ◽  
Author(s):  
F. J. Clark
Keyword(s):  
Knee Joint ◽  
Entire Range ◽  
Joint Angle ◽  
Nerve Fibers ◽  
Pacinian Corpuscle ◽  
Joint Angles ◽  
Discharge Characteristics ◽  
Gentle Pressure ◽  
Sensory Fibers ◽  
Deep Pressure

Responses of 331 individual medial articular nerve fibers innervating the cat knee joint were tested to bending the joint over its entire range and to pressing on the tissues of the joint. The 331 fibers were classified into five groups on the basis of their discharge characteristics: slowly adapting (64), phasic (103), Pacinian corpuscle-like (12), weakly activated (39), and nonactivated (113). Five of the slowly adapting and all twelve of the Pacinian corpuscle-like receptors responded at intermediate joint angles. The remainder responded, if at all, only near the extremes of joint bending or twisting. Many of these same receptors could be activated by pressing about the knee. Sometimes gentle pressure on the focus sufficed to produce a vigorous discharge. The properties of these receptors are considered to be consistent with the hypothesis that articular mechanoreceptors do not signal joint angle but are involved in deep-pressure sensations.

Phase Plane Analysis of Limb Trajectories in Nonhandicapped and Cerebral Palsied Subjects

1985 ◽  
Vol 2 (3) ◽  
pp. 214-227 ◽  
Author(s):  
Anne Beuter ◽  
Alan Garfinkel
Keyword(s):  
Dynamical System ◽  
Knee Joint ◽  
Phase Plane ◽  
Second Order ◽  
Study Phase ◽  
Phase Plane Analysis ◽  
Joint Angles ◽  
Kinematic Data ◽  
Movement Data ◽  
Plane Analysis

In this study, phase plane analysis was used to describe the mechanisms involved in human intralimb dynamics during a multijoint coordinated task. Nonhandicapped, spastic, and athetoid cerebral palsied individuals were videotaped as they performed a stepping task. Kinematic data for the hip and knee joint angles were digitized, smoothed, differentiated, and plotted. Phase plane analysis of movement data reveals striking differences between nonhandicapped and cerebral palsied individuals. Whereas nonhandicapped individuals have trajectories in the phase plane that suggest a self-contained second-order dynamical system, cerebral palsied individuals have self-interesting loops in their phase planes. Based upon these patterns some dynamical distinctions are offered, and suggestions are made toward a possible model.

Continuous Estimation Prediction of Knee Joint Angles Using Fusion of Electromyographic and Inertial Sensors for Active Transfemoral Leg Prostheses

2018 ◽  
Vol 10 (02) ◽  
pp. 1840008
Author(s):  
Alberto López-Delis ◽  
Cristiano J. Miosso ◽  
João L. A. Carvalho ◽  
Adson F. da Rocha ◽  
Geovany A. Borges
Keyword(s):  
Knee Joint ◽  
Joint Angle ◽  
Inertial Sensors ◽  
Sagittal Plane ◽  
Joint Angles ◽  
Knee Joint Angle ◽  
Improved Performance ◽  
Continuous Estimation ◽  
Movement Intention ◽  
Semg Signals

Information extracted from the surface electromyographic (sEMG) signals can allow for the detection of movement intention in transfemoral prostheses. The sEMG can help estimate the angle between the femur and the tibia in the sagittal plane. However, algorithms based exclusively on sEMG information can lead to inaccurate results. Data captured by inertial-sensors can improve this estimate. We propose three myoelectric algorithms that extract data from sEMG and inertial sensors using Kalman-filters. The proposed fusion-based algorithms showed improved performance compared to methods based exclusively on sEMG data, generating improvements in the accuracy of knee joint angle estimation and reducing estimation artifacts.

scholarly journals The effect of changing plantarflexion resistive moment of an articulated ankle–foot orthosis on ankle and knee joint angles and moments while walking in patients post stroke

2015 ◽  
Vol 30 (8) ◽  
pp. 775-780 ◽  
Author(s):  
Toshiki Kobayashi ◽  
Madeline L. Singer ◽  
Michael S. Orendurff ◽  
Fan Gao ◽  
Wayne K. Daly ◽  
...  
Keyword(s):  
Knee Joint ◽  
Joint Angles ◽  
Ankle Foot Orthosis ◽  
Post Stroke ◽  
Foot Orthosis

scholarly journals Estimation of 3D Knee Joint Angles during Cycling Using Inertial Sensors: Accuracy of a Novel Sensor-to-Segment Calibration Procedure Based on Pedaling Motion

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2474 ◽  
Author(s):  
Sébastien Cordillet ◽  
Nicolas Bideau ◽  
Benoit Bideau ◽  
Guillaume Nicolas
Keyword(s):  
Knee Joint ◽  
Inertial Sensor ◽  
External Rotation ◽  
Calibration Method ◽  
Calibration Procedure ◽  
Angle Measurement ◽  
Measurement Unit ◽  
Joint Angles ◽  
Calibration Methods ◽  
Flexion Extension

This paper presents a novel sensor-to-segment calibration procedure for inertial sensor-based knee joint kinematics analysis during cycling. This procedure was designed to be feasible in-field, autonomously, and without any external operator or device. It combines a static standing up posture and a pedaling task. The main goal of this study was to assess the accuracy of the new sensor-to-segment calibration method (denoted as the ‘cycling’ method) by calculating errors in terms of body-segment orientations and 3D knee joint angles using inertial measurement unit (IMU)-based and optoelectronic-based motion capture. To do so, 14 participants were evaluated during pedaling motion at a workload of 100 W, which enabled comparisons of the cycling method with conventional calibration methods commonly employed in gait analysis. The accuracy of the cycling method was comparable to that of other methods concerning the knee flexion/extension angle, and did not exceed 3.8°. However, the cycling method presented the smallest errors for knee internal/external rotation (6.65 ± 1.94°) and abduction/adduction (5.92 ± 2.85°). This study demonstrated that a calibration method based on the completion of a pedaling task combined with a standing posture significantly improved the accuracy of 3D knee joint angle measurement when applied to cycling analysis.

scholarly journals Validation of a Device to Measure Knee Joint Angles for a Dynamic Movement

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1747 ◽  
Author(s):  
Mirel Ajdaroski ◽  
Ruchika Tadakala ◽  
Lorraine Nichols ◽  
Amanda Esquivel
Keyword(s):  
Knee Joint ◽  
Motion Capture ◽  
Cruciate Ligament ◽  
Linear Regression Analysis ◽  
Knee Kinematics ◽  
The United States ◽  
Wearable Device ◽  
Joint Angles ◽  
Dynamic Movement ◽  
Flexion Extension

Participation in sports has risen in the United States over the last few years, increasing the risk of injuries such as tears to the anterior cruciate ligament (ACL) in the knee. Previous studies have shown a correlation between knee kinematics when landing from a jump and this injury. The purpose of this study was to validate the ability of a commercially available inertial measurement units (IMUs) to accurately measure knee joint angles during a dynamic movement. Eight healthy subjects participated in the study. Validation was performed by comparing the angles measured by the wearable device to those obtained through the gold standard motion capture system when landing from a jump. Root mean square, linear regression analysis, and Bland–Altman plots were performed/constructed. The mean difference between the wearable device and the motion capture data was 8.4° (flexion/extension), 4.9° (ab/adduction), and 3.9° (rotation). In addition, the device was more accurate at smaller knee angles. In our study, a commercially available wearable IMU was able to perform fairly well under certain conditions and was less accurate in other conditions.

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