Design and Validation of a Compatible 3-Degrees of Freedom Shoulder Exoskeleton With an Adaptive Center of Rotation

2014 ◽  
Vol 136 (7) ◽  
Author(s):  
Hua Yan ◽  
Canjun Yang ◽  
Yansong Zhang ◽  
Yiqi Wang
Keyword(s):  
Soft Tissues ◽  
Dimensional Space ◽  
Interaction Force ◽  
Shoulder Abduction ◽  
Upper Arm ◽  
Human Machine Interaction ◽  
Center Of Rotation ◽  
Custom Made ◽  
Flexion Extension ◽  
Machine Interaction

This paper outlines an experimentally based design method for a compatible 3-DOF shoulder exoskeleton with an adaptive center of rotation (CoR) by matching the mechanical CoR with the anatomical CoR to reduce human–machine interaction forces and improve comfort during dynamic humeral motion. The spatial–temporal description for anatomical CoR motion is obtained via a specific experimental task conducted on six healthy subjects. The task is comprised of a static section and a dynamic section, both of which are recorded with an infrared motion capture system using body-attached markers. To reduce the influence of human soft tissues, a custom-made four-marker group block was placed on the upper arm instead of using discrete markers. In the static section, the position of anatomical CoR is kept stationary and calculated using a well-known functional method. Based on the static results, the dynamic section determines the statistical relationship between the dynamic CoR position and the humeral orientation using an optimization method when subjects move their upper arm freely in the sagittal and coronal planes. Based on the resolved anatomical CoR motion, a new mechanical CoR model derived from a traditional ball-and-socket joint is applied to match the experimental results as closely as possible. In this mechanical model, the CoR motion in three-dimensional space is adjusted by translating two of the three intersecting joint axes, including the shoulder abduction/adduction and flexion/extension. A set of optimal translation parameters is obtained through proper matching criterion for the two CoRs. Based on the translation parameters, a compatible shoulder exoskeleton was manufactured and compared with a traditional shoulder exoskeleton with a fixed CoR. An experimental test was conducted to validate the CoR motion adaptation ability by measuring the human–machine interaction force during passive shoulder joint motion. The results provide a promising direction for future anthropomorphic shoulder exoskeleton design.

scholarly journals Prediction of state anxiety by machine learning applied to photoplethysmography data

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10448
Author(s):  
David Perpetuini ◽  
Antonio Maria Chiarelli ◽  
Daniela Cardone ◽  
Chiara Filippini ◽  
Sergio Rinella ◽  
...  
Keyword(s):  
Machine Learning ◽  
State Anxiety ◽  
Affective Computing ◽  
Pulse Wave ◽  
Learning Algorithm ◽  
Nervous Activity ◽  
Human Machine Interaction ◽  
Promising Tool ◽  
Custom Made ◽  
Machine Interaction

Background As the human behavior is influenced by both cognition and emotion, affective computing plays a central role in human-machine interaction. Algorithms for emotions recognition are usually based on behavioral analysis or on physiological measurements (e.g., heart rate, blood pressure). Among these physiological signals, pulse wave propagation in the circulatory tree can be assessed through photoplethysmography (PPG), a non-invasive optical technique. Since pulse wave characteristics are influenced by the cardiovascular status, which is affected by the autonomic nervous activity and hence by the psychophysiological state, PPG might encode information about emotional conditions. The capability of a multivariate data-driven approach to estimate state anxiety (SA) of healthy participants from PPG features acquired on the brachial and radial artery was investigated. Methods The machine learning method was based on General Linear Model and supervised learning. PPG was measured employing a custom-made system and SA of the participants was assessed through the State-Trait Anxiety Inventory (STAI-Y) test. Results A leave-one-out cross-validation framework showed a good correlation between STAI-Y score and the SA predicted by the machine learning algorithm (r = 0.81; p = 1.87∙10−9). The preliminary results suggested that PPG can be a promising tool for emotions recognition, convenient for human-machine interaction applications.

Adaptive Robust Cascade Force Control of 1-DOF Joint Exoskeleton for Human Performance Augmentation

2015 ◽  
Author(s):  
Shan Chen ◽  
Bin Yao ◽  
Zheng Chen ◽  
Xiaocong Zhu ◽  
Shiqiang Zhu
Keyword(s):  
Force Control ◽  
Motion Tracking ◽  
Human Performance ◽  
Control Method ◽  
Interaction Force ◽  
Human Motion ◽  
Adaptive Robust Control ◽  
Human Machine Interaction ◽  
High Level ◽  
Machine Interaction

The control objective of exoskeleton for human performance augmentation is to minimize the human machine interaction force while carrying external loads and following human motion. This paper addresses the dynamics and the interaction force control of a 1-DOF hydraulically actuated joint exoskeleton. A spring with unknown stiffness is used to model the human-machine interface. A cascade force control method is adopted with high-level controller generating the reference position command while low level controller doing motion tracking. Adaptive robust control (ARC) algorithm is developed for both two controllers to deal with the effect of parametric uncertainties and uncertain nonlinearities of the system. The proposed adaptive robust cascade force controller can achieve small human-machine interaction force and good robust performance to model uncertainty which have been validated by experiment.

Virtual Hazard Analysis: A Methodology

1997 ◽  
Vol 41 (2) ◽  
pp. 859-863
Author(s):  
Scott Allen Ziolek ◽  
Robert Carder Hale
Keyword(s):  
Virtual Environments ◽  
Dimensional Space ◽  
Hazard Analysis ◽  
Human Machine Interaction ◽  
3 Dimensional ◽  
Modeling Simulation ◽  
Simulation Techniques ◽  
Human Modeling ◽  
Virtual Simulations ◽  
Machine Interaction

This paper presents a method for representing and analyzing hazards in virtual simulations as regions in 3-dimensional space. The objectives of this study are to provide designers and analysts a technique for evaluating hazards concerning human-machine interaction in virtual environments, as well as, initiating a dialog concerning future hazard simulation techniques and standards. Information obtained for this paper is based upon a study to incorporate hazard information into a human modeling simulation package called DEPTH (Design Evaluation for Personnel, Training, and Human Factors), which is being developed by U.S.A.F. Armstrong Laboratory.

scholarly journals Exoskeleton Follow-Up Control Based on Parameter Optimization of Predictive Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Shijia Zha ◽  
Tianyi Li ◽  
Lidan Cheng ◽  
Jihua Gu ◽  
Wei Wei ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Target Position ◽  
Interaction Force ◽  
Human Motion ◽  
Sensor Data ◽  
Prediction Algorithm ◽  
Human Machine Interaction ◽  
Predictive Algorithm ◽  
Machine Interaction

The prediction of sensor data can help the exoskeleton control system to get the human motion intention and target position in advance, so as to reduce the human-machine interaction force. In this paper, an improved method for the prediction algorithm of exoskeleton sensor data is proposed. Through an algorithm simulation test and two-link simulation experiment, the algorithm improves the prediction accuracy by 14.23 ± 0.5%, and the sensor data is smooth. Input the predicted signal into the two-link model, and use the calculated torque method to verify the prediction accuracy data and smoothness. The simulation results showed that the algorithm can predict the joint angle of the human body and can be used for the follow-up control of the swinging legs of the exoskeleton.

Concise CNN model for face expression recognition

2021 ◽  
pp. 1-9
Author(s):  
Harshadkumar B. Prajapati ◽  
Ankit S. Vyas ◽  
Vipul K. Dabhi
Keyword(s):  
Network Architecture ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Face Expression ◽  
Computational Overhead ◽  
Average Accuracy ◽  
Proposed Model ◽  
Face Expression Recognition ◽  
Expression Classification ◽  
Machine Interaction

Face expression recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN.

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