A real-time 3D reconstruction of staircases for rehabilitative exoskeletons

In medical contexts, the use of assistive exoskeletons for the rehabilitation of people with impaired mobility represents a common practice. Recent advances suggest that, soon, such mechatronic systems will also be used to assist people in their everyday life. In order to reach such target, exoskeletons must become able to perceive the environment. To this purpose, a system for the parametric identification of a staircase is proposed in this paper. More precisely, given a staircase of unknown geometry, the system identifies its 3D shape. Furthermore, it also estimates the reciprocal orientation and distance between the exoskeleton and the staircase. Differently from other approaches, this result is achieved by means of low cost devices: an inertial measurement unit, two ranging sensors, and an Arm-Cortex processor. Starting from the ranging sensors acquisitions, the staircase model is identified in real time, during the execution of a step. The proposed procedure is based on an extended recursive total least squares strategy, in order to fully exploit the computational capabilities of the Arm processor, and it is characterized by execution times smaller than 10 −3 s. The estimation algorithm has been tested on an actual exoskeleton and the resulting experimental outcomes are compared with the results obtained through alternative methods

Mechanical Redesign and Implementation of Intuitive User Input Methods for a Hand Exoskeleton Informed by User Studies on Individuals with Chronic Upper Limb Impairments

Individuals with upper limb motor deficits due to neurological conditions, such as stroke and traumatic brain injury, may exhibit hypertonia and spasticity, which makes it difficult for these individuals to open their hand. The Hand Orthosis with Powered Extension (HOPE) Hand was created in 2018. The performance of the HOPE Hand was evaluated by conducting a Box and Blocks test with an impaired subject. Improvements were identified and the HOPE Hand was mechanically redesigned to increase the functionality in performing grasps. The original motor configuration was reorganized to include active thumb flexion and extension, as well as thumb abduction/adduction. An Electromyography (EMG) study was conducted on 19 individuals (10 healthy, 9 impaired) to evaluate the viability of EMG device control for the specified user group. EMG control, voice control, and manual control were implemented with the HOPE Hand 2.0 and the exoskeleton system was tested for usability during a second Box and Blocks test

The Future of Humanoid Robots

This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book

Biomechatronics: Harmonizing Mechatronic Systems with Human Beings

This eBook provides a comprehensive treatise on modern biomechatronic systems centred around human applications. A particular emphasis is given to exoskeleton designs for assistance and training with advanced interfaces in human-machine interaction. Some of these designs are validated with experimental results which the reader will find very informative as building-blocks for designing such systems. This eBook will be ideally suited to those researching in biomechatronic area with bio-feedback applications or those who are involved in high-end research on manmachine interfaces. This may also serve as a textbook for biomechatronic design at post-graduate level

Using High Density EMG to Proportionally Control 3D Model of Human Hand

Control of human hand using surface electromyography (EMG) is already established in various mechanisms, but proportionally controlling magnitudes degrees of freedom (DOF) of humanoid hand model is still highly developed in recent years. This paper proposes another method to achieve a proportional estimation and control of human’s hand multiple DOFs. Gestures in the form of American Sign Language (ABCDFIKLOW) were chosen as the targets, of which ten alphabetical gestures were specifically used following their clarity on its 3D model. Then the dataset of the movements gestures was simultaneously recorded using High-density electromyography (HD-EMG) and motion capture system. Sensor placements were on intrinsic - extrinsic muscles for HD-EMG and finger joints for the motion capture system. To derive the proportional control in time series between both datasets (HD-EMG and kinematics data), neural network (NN) and k-Nearest Neighbour were used. The models produced around 70-95 % (R index) accuracy for the eleven DOFs in four healthy subjects’ hand. kNN’s performance was better than NN, even if the input features were reduced either using manual selections or principal component analysis (PCA). The time series controls could also identify most sign language gestures (9 of 10), with difficulty was given on O gesture. The false interpretation was because of nearly identical muscle’s EMG and kinematics data between O and C. This paper intends to extend its conference version [1] by adding more in-depth Results and Discussion along making other sections more comprehensive