A Computational Approach for Human-like Motion Generation in Upper Limb Exoskeletons Supporting Scapulohumeral Rhythms

This paper proposes a computational approach for generation of reference path for upper-limb exoskeletons considering the scapulohumeral rhythms of the shoulder. The proposed method can be used in upper-limb exoskeletons with 3 Degrees of Freedom (DoF) in shoulder and 1 DoF in elbow, which are capable of supporting shoulder girdle. The developed computational method is based on Central Nervous System (CNS) governing rules. Existing computational reference generation methods are based on the assumption of fixed shoulder center during motions. This assumption can be considered valid for reaching movements with limited range of motion (RoM). However, most upper limb motions such as Activities of Daily Living (ADL) include large scale inward and outward reaching motions, during which the center of shoulder joint moves significantly. The proposed method generates the reference motion based on a simple model of human arm and a transformation can be used to map the developed motion for other exoskeleton with different kinematics. Comparison of the model outputs with experimental results of healthy subjects performing ADL, show that the proposed model is able to reproduce human-like motions.Comment: In 2017 IEEE International Symposium on Wearable & Rehabilitation Robotics (WeRob2017

A kinematic model of the shoulder complex for estimating shoulder girdle angles without acromial sensing

The movements of each joint in the shoulder complex is important to measure for studying shoulder function, injuries, and rehabilitation. The current standards for measuring these motions are non-invasive motion capture of surface markers or regression equations from other research studies. Certain environments, such as robotic exoskeletons and spacesuits, are not compatible with motion capture systems because their hardware obscures or precludes these measurement. The existing regression models are generalized across a wide range of subjects and are designed with experimental data that has minimal environmental interaction. So, these methods are insufficient for estimating shoulder girdle motion in an occluded setting and with substantial human-device interaction. The objective of this thesis is to develop and evaluate a novel kinematic shoulder model that estimates shoulder girdle angles without acromial sensors. This model leverages the geometric similarities of the human shoulder and a RRSS spatial linkage to constrain the internal degrees of freedom of the shoulder mechanism. A nonlinear optimization method is used to predict the configuration of the shoulder by matching desired distances between the scapula and the ribcage. This model is validated using experimental measurements of 77 arm movements from five subjects. With ideal inputs, the kinematic model is able to accurately estimate shoulder girdle angles within 2°. The model was also able to outperform two existing shoulder regression models. However, small changes to model geometry or input kinematics result in significant errors in all shoulder angles. This is likely due to the rigidity of the kinematic constraint, which uses an idealized mechanical model to represent a complex biological system with flexible joints. Ultimately, this work shows that the kinematic constraints from this linkage model can be used to predict shoulder angles during a variety of different movements without sensors on the acromion. The model's robustness can be improved by pairing it with a compliant joint model to permit errors in anthropometry and input kinematics.Mechanical Engineerin

Actas de SABI2020

Los temas salientes incluyen un marcapasos pulmonar que promete complementar y eventualmente sustituir la conocida ventilación mecánica por presión positiva (intubación), el análisis de la marchaespontánea sin costosos equipamientos, las imágenes infrarrojas y la predicción de la salud cardiovascular en temprana edad por medio de la biomecánica arterial