This article describes the performance of a flexible resistive sensor network to track shoulder motion. This system monitors every gesture of the human shoulder in its range of motion except rotations around the longitudinal axis of the arm. In this regard, the design considers the movement of the glenohumeral, acromioclavicular, sternoclavicular, and scapulothoracic joints. The solution presented in this work considers several sensor configurations and compares its performance with a set of inertial measurement units (IMUs). These devices have been put together in a shoulder suit with Optitrack visual markers in order to be used as pose ground truth. Optimal configurations of flexible resistive sensors, in terms of accuracy requirements and number of sensors, have been obtained by applying principal component analysis techniques. The data provided by each configuration are then mapped onto the shoulder pose by using neural network algorithms. According to the results shown in this article, a set of flexible resistive sensors can be an adequate alternative to IMUs for multiaxial shoulder pose tracking in open spaces. Furthermore, the system presented can be easily embedded in fabric or wearable devices without obstructing the user's motion.

This article presents a practical application of the Kalman filter by implementing a sensor array in a Bebop Parrot drone in order to detect objects within a determined rank to reduce the background noise in the obtained measurements. The effect of the relationship between the measurement taken by the sensors and the effectiveness of the background noise reduction using the Kalman filter was studied, thus reducing the variation of the sensor measurement. The objective of this implementation can help improve the teaching of the application of Kalman filter. Information readings are performed by the HC-SR04 ultrasonic sensors connected to the data acquisition board Arduino Yun, which interprets the electronic pulses to get the distance in meters and, at the same time, send them via serial port to a Raspberry Pi board; this board implements the Kalman filter sending the data via Wi-Fi to a computer connected to the network that allows visualizing the results of measurements and filter performance. The results obtained show the improvement of the data measurement by the distance sensors with the purpose of providing the drone with greater accuracy of data acquisition.

Recovery of therapeutic or functional ambulatory capacity in patients with rotator cuff injury is a primary goal of rehabilitation. Wearable powered exoskeletons allow patients to perform repetitive practice with large movements to maximize recovery, even immediately after the acute event. The aim of this paper is to describe the usability, acceptability and acceptance of a hybrid exoskeleton for upper-limb passive rehabilitation using the System Usability Scale (SUS) questionnaire. This equipment, called ExoFlex, is defined as a hybrid exoskeleton since it is made up of rigid and soft components. The exoskeleton mechanical description is presented along with its control system and the way motion is structured in rehabilitation sessions. Seven patients (six women and one man) have participated in the evaluation of this equipment, which are in the range of 50 to 79 years old. Preliminary evidence of the acceptance and usability by both patients and clinicians are very promising, obtaining an average score of 80.71 in the SUS test, as well as good results in a questionnaire that evaluates the clinicians’ perceived usability of ExoFlex.

Motion tracking techniques have been extensively studied in recent years. However, capturing movements of the upper limbs is a challenging task. This document presents the estimation of arm orientation and elbow and wrist position using wearable flexible sensors (WFSs). A study was developed to obtain the highest range of motion (ROM) of the shoulder with as few sensors as possible, and a method for estimating arm length and a calibration procedure was proposed. Performance was verified by comparing measurement of the shoulder joint angles obtained from commercial two-axis soft angular displacement sensors (sADS) from Bend Labs and from the ground truth system (GTS) OptiTrack. The global root-mean-square error (RMSE) for the shoulder angle is 2.93 degrees and 37.5 mm for the position estimation of the wrist in cyclical movements; this measure of RMSE was improved to 13.6 mm by implementing a gesture classifier.

Current limitation is crucial to protect DC motors from overheating in overload situations. This consideration is more critical in applications that involve the movement of variable loads, such as exoskeleton applications, where there exists dynamic human-robot interaction. This work presents a software current limitation method based on a continuous DC motor model predictor. By predicting, according to the motor model, the voltages that would make the current match a threshold, the proposed method dynamically saturates the maximum voltages applicable to the motor to maintain the current under the predefined threshold. Using this method, a full-time current limitation can be obtained, but if desired, it also allows current peaks of configurable duration. Only motor parameters need to be known for the application of this algorithm, and it is easily adjustable to different DC motor models. Stand-alone motor tests show an effective limitation of current, achieving up to a 98.04% of maximum current when limited. The method has also been successfully validated in a rehabilitation exoskeleton application with four participants, obtaining an average current of 92.34% of current threshold when limited.