Upper limb orientation assessment as an articulated body chain.

Upper limb orientation estimation based on Magnetic, Angular Rate, and Gravity sensors considering multiple body segments is presented in this work. The proposal allowed assessing the activity of two or more body segments individually and jointly, regardless of their spatial relationship. A custom-made system was developed incorporating a complementary filter and a proportional-integral control for data sensor merging and, noise and instrumentation error reduction. Two controlled tests were carried out to assess the performance of the system. The former evaluated the response of the method in motionless conditions, while the latter assessed the feasibility to follow trajectories in 3D space. Ten volunteers were recruited to evaluate the system performance in three semi-controlled and daily life task tests. The system was evaluated using the common parameters in motion tracking methods and relied on a digital motion processor. The system's outcome presented a root mean square error in the range of 2.65°-3.98° for the semi-controlled tests and 0.48°-1.389° for the daily life task test. The system tests analysis proved that the proposal permitted obtaining the articulated body chain information of multiple segments when three or more MARG sensors are used.

PD Control Compensation Based on a Cascade Neural Network Applied to a Robot Manipulator.

A Proportional Integral Derivative (PID) controller is commonly used to carry out tasks like position tracking in the industrial robot manipulator controller; however, over time, the PID integral gain generates degradation within the controller, which then produces reduced stability and bandwidth. A proportional derivative (PD) controller has been proposed to deal with the increase in integral gain but is limited if gravity is not compensated for. In practice, the dynamic system non-linearities frequently are unknown or hard to obtain. Adaptive controllers are online schemes that are used to deal with systems that present non-linear and uncertainties dynamics. Adaptive controller use measured data of system trajectory in order to learn and compensate the uncertainties and external disturbances. However, these techniques can adopt more efficient learning methods in order to improve their performance. In this work, a nominal control law is used to achieve a sub-optimal performance, and a scheme based on a cascade neural network is implemented to act as a non-linear compensation whose task is to improve upon the performance of the nominal controller. The main contributions of this work are neural compensation based on a cascade neural networks and the function to update the weights of neural network used. The algorithm is implemented using radial basis function neural networks and a recompense function that leads longer traces for an identification problem. A two-degree-of-freedom robot manipulator is proposed to validate the proposed scheme and compare it with conventional PD control compensation.

Evaluation of suitability of a micro-processing unit of motion analysis for upper limb tracking.

The aim of this study is to assess the suitability of a micro-processing unit of motion analysis (MPUMA), for monitoring, reproducing, and tracking upper limb movements. The MPUMA is based on an inertial measurement unit, a 16-bit digital signal controller and a customized algorithm. To validate the performance of the system, simultaneous recordings of the angular trajectory were performed with a video-based motion analysis system. A test of the flexo-extension of the shoulder joint during the active elevation in a complete range of 120º of the upper limb was carried out in 10 healthy volunteers. Additional tests were carried out to assess MPUMA performance during upper limb tracking. The first, a 3D motion reconstruction of three movements of the shoulder joint (flexo-extension, abduction-adduction, horizontal internal-external rotation), and the second, an upper limb tracking online during the execution of three movements of the shoulder joint followed by a continuous random movement without any restrictions by using a virtual model and a mechatronic device of the shoulder joint. Experimental results demonstrated that the MPUMA measured joint angles that are close to those from a motion-capture system with orientation RMS errors less than 3º.