scholarly journals Robust Human Machine Interface Based on Head Movements Applied to Assistive Robotics

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Elisa Perez ◽  
Natalia López ◽  
Eugenio Orosco ◽  
Carlos Soria ◽  
Vicente Mut ◽  
...  
Keyword(s):  
Control Algorithm ◽  
Inertial Sensor ◽  
Minimum Variance ◽  
Head Movements ◽  
Assistive Robotics ◽  
Artificial Vision ◽  
Technology System ◽  
Minimum Variance Estimator ◽  
Machine Interface ◽  
Robotic Wheelchair

This paper presents an interface that uses two different sensing techniques and combines both results through a fusion process to obtain the minimum-variance estimator of the orientation of the user’s head. Sensing techniques of the interface are based on an inertial sensor and artificial vision. The orientation of the user’s head is used to steer the navigation of a robotic wheelchair. Also, a control algorithm for assistive technology system is presented. The system is evaluated by four individuals with severe motors disability and a quantitative index was developed, in order to objectively evaluate the performance. The results obtained are promising since most users could perform the proposed tasks with the robotic wheelchair.

A new method to reduce the noise of the miniaturised inertial sensors disposed in redundant linear configurations

2013 ◽  
Vol 117 (1188) ◽  
pp. 111-132 ◽  
Author(s):  
T. L. Grigorie ◽  
R. M. Botez
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Frequency ◽  
Minimum Variance ◽  
Measurement Unit ◽  
Time Step ◽  
Accelerometer Sensor ◽  
Sensor Model ◽  
Input Acceleration

Abstract This paper presents a new adaptive algorithm for the statistical filtering of miniaturised inertial sensor noise. The algorithm uses the minimum variance method to perform a best estimate calculation of the accelerations or angular speeds on each of the three axes of an Inertial Measurement Unit (IMU) by using the information from some accelerometers and gyros arrays placed along the IMU axes. Also, the proposed algorithm allows the reduction of both components of the sensors’ noise (long term and short term) by using redundant linear configurations for the sensors dispositions. A numerical simulation is performed to illustrate how the algorithm works, using an accelerometer sensor model and a four-sensor array (unbiased and with different noise densities). Three cases of ideal input acceleration are considered: 1) a null signal; 2) a step signal with a no-null time step; and 3) a low frequency sinusoidal signal. To experimentally validate the proposed algorithm, some bench tests are performed. In this way, two sensors configurations are used: 1) one accelerometers array with four miniaturised sensors (n = 4); and 2) one accelerometers array with nine miniaturised sensors (n = 9). Each of the two configurations are tested for three cases of input accelerations: 0ms−1, 9·80655m/s2 and 9·80655m/s2.

scholarly journals The use of a cascaded Kinect and electromyography gesture decoding algorithm in an initial robot-aided hand neurorehabilitation

2018 ◽  
Vol 10 (1) ◽  
pp. 168781401775196 ◽  
Author(s):  
Ping Wang ◽  
Yabo Wang ◽  
He Huang ◽  
Feng Ru ◽  
Quan Pan
Keyword(s):  
Control Algorithm ◽  
Motion Tracking ◽  
Simulation Experiment ◽  
Tracking Error ◽  
Torque Control ◽  
Tracking Accuracy ◽  
Rehabilitation Robots ◽  
Machine Interface ◽  
Repetitive Activities ◽  
Active Assistance

In order to improve the neurological recovery of hand neurorehabilitation, target-oriented, intensive, repetitive activities of daily living are used, such as training with recognition of hand gestures during robot-aided exercise. In this article, a cascade control algorithm integrating electromyography bio-feedback into hand gesture recognition is proposed. The outer loop is the trajectory motion tracking with Kinect-based gesture decoding classifier, and the inner loop is torque control with electromyography bio-feedback in the real time. This proposed method improves the tracking accuracy. The tracking error is effectively reduced from 70.56 to 28.07 in the simulation experiment. The initial test proves that the proposed method with additional torque control allows active assistance on the human–machine interface of other rehabilitation robots in future.

scholarly journals Validation of an Inertial Sensor Algorithm to Quantify Head and Trunk Movement in Healthy Young Adults and Individuals with Mild Traumatic Brain Injury

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4501 ◽  
Author(s):  
Lucy Parrington ◽  
Deborah Jehu ◽  
Peter Fino ◽  
Sean Pearson ◽  
Mahmoud El-Gohary ◽  
...  
Keyword(s):  
Inertial Sensor ◽  
Rotational Velocity ◽  
Correlation Coefficients ◽  
Head Movements ◽  
Home Exercise ◽  
Trunk Movement ◽  
Percent Error ◽  
Criterion Data ◽  
Measurement Units ◽  
Promising Solution

Wearable inertial measurement units (IMUs) may provide useful, objective information to clinicians interested in quantifying head movements as patients’ progress through vestibular rehabilitation. The purpose of this study was to validate an IMU-based algorithm against criterion data (motion capture) to estimate average head and trunk range of motion (ROM) and average peak velocity. Ten participants completed two trials of standing and walking tasks while moving the head with and without moving the trunk. Validity was assessed using a combination of Intra-class Correlation Coefficients (ICC), root mean square error (RMSE), and percent error. Bland-Altman plots were used to assess bias. Excellent agreement was found between the IMU and criterion data for head ROM and peak rotational velocity (average ICC > 0.9). The trunk showed good agreement for most conditions (average ICC > 0.8). Average RMSE for both ROM (head = 2.64°; trunk = 2.48°) and peak rotational velocity (head = 11.76 °/s; trunk = 7.37 °/s) was low. The average percent error was below 5% for head and trunk ROM and peak rotational velocity. No clear pattern of bias was found for any measure across conditions. Findings suggest IMUs may provide a promising solution for estimating head and trunk movement, and a practical solution for tracking progression throughout rehabilitation or home exercise monitoring.

scholarly journals PID Controller Implemented in Festo CDPX

2019 ◽  
Vol 260 ◽  
pp. 02008
Author(s):  
Primož Podržaj
Keyword(s):  
Control Systems ◽  
Pid Controller ◽  
Control Algorithm ◽  
Closed Loop ◽  
Pi Control ◽  
Programmable Logic ◽  
Educational Tool ◽  
Level Control ◽  
Loop Control ◽  
Machine Interface

In this paper, we describe the procedure for the implementation of the PID controller in the Festo CDPX operator unit. These units enable the execution of the control algorithm and human machine interface in a single unit. In our laboratory the unit is used to teach the students about the basics of control systems. For this purpose, one of the most common closed loop control systems for the education purposes was selected. It is a water level control system. In this paper the design of the whole system is presented. The need for a PI control algorithm is also explained. The programming of the operator unit CDPX, both in Festo CoDeSys and Designer Studio is explained. Such a simple system has turned out to be a great educational tool for Control Theory and Programmable Logic Controller related subjects.

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