Editorial: Biomechatronics: Harmonizing Mechatronic Systems With Human Beings.

There has been a growing body of research in the recent years on human-robot interactions, human-machine interfaces and intelligent devices that are centered around human application, however, these works by and large lacked in focus on how to harmonize the interactions between mechatronic systems and users in the loop. This is one of the key areas for evaluating the success of any mechatronic system implementation on human. The collection of papers in this volume is touching upon the frontiers of this research area as to how the efficacy of such biomechatronic systems could be evaluated and improved. There are a total of 19 papers looking into various aspects of human-machine interfaces (HMIs) using electromyography (EMG) and electroencephalography (EEG), tactile feedback, external devices such as exoskeletons and prosthetic devices for assistance and rehabilitation, novel techniques like machine learning and intelligent computation, and experimental evaluation or validation. The following paragraphs aim to give a glimpse of the contents presented in this eBook. Specifically, these are categorized under three distinct headings: (A) Novel exoskeletons for assistance and training, (B) Advanced human-machine interfaces in biomechatronics, and (C) Experimental outcomes and validation

A Sonomyography-based Muscle Computer Interface for Individuals with Spinal Cord Injury

Impairment of hand functions in individuals with spinal cord injury (SCI) severely disrupts activities of daily living. Recent advances have enabled rehabilitation assisted by robotic devices to augment the residual function of the muscles. Traditionally, non-invasive electromyography-based peripheral neural interfaces have been utilized to sense volitional motor intent to drive robotic assistive devices. However, the dexterity and fidelity of control that can be achieved with electromyography-based control have been limited due to inherent limitations in signal quality. We have developed and tested a muscle-computer interface (MCI) utilizing sonomyography to provide control of a virtual cursor for individuals with motor-incomplete spinal cord injury. We demonstrate that individuals with SCI successfully gained control of a virtual cursor by utilizing contractions of muscles of the wrist joint. The sonomyography-based interface enabled control of the cursor at multiple graded levels demonstrating the ability to achieve accurate and stable endpoint control. Our sonomyography-based muscle-computer interface can enable dexterous control of upper-extremity assistive devices for individuals with motor-incomplete SCI

An analysis of electromyography as an input method for resilient and affordable systems: human-computer interfacing using the body’s electrical activity

This article was published in the Spring 2014 issue of the Journal of Undergraduate Researc

Gait Cycle-Inspired Learning Strategy for Continuous Prediction of Knee Joint Trajectory from sEMG

Predicting lower limb motion intent is vital for controlling exoskeleton robots and prosthetic limbs. Surface electromyography (sEMG) attracts increasing attention in recent years as it enables ahead-of-time prediction of motion intentions before actual movement. However, the estimation performance of human joint trajectory remains a challenging problem due to the inter- and intra-subject variations. The former is related to physiological differences (such as height and weight) and preferred walking patterns of individuals, while the latter is mainly caused by irregular and gait-irrelevant muscle activity. This paper proposes a model integrating two gait cycle-inspired learning strategies to mitigate the challenge for predicting human knee joint trajectory. The first strategy is to decouple knee joint angles into motion patterns and amplitudes former exhibit low variability while latter show high variability among individuals. By learning through separate network entities, the model manages to capture both the common and personalized gait features. In the second, muscle principal activation masks are extracted from gait cycles in a prolonged walk. These masks are used to filter out components unrelated to walking from raw sEMG and provide auxiliary guidance to capture more gait-related features. Experimental results indicate that our model could predict knee angles with the average root mean square error (RMSE) of 3.03(0.49) degrees and 50ms ahead of time. To our knowledge this is the best performance in relevant literatures that has been reported, with reduced RMSE by at least 9.5%

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

Powered knee orthosis for human gait rehabilitation: first advances

This paper presents a new system for a powered knee orthosis, that was designed to assist and improve the gait function of patients with gait pathologies. The system contains the orthotic device (embedded with sensors for angle and user-orthosis interaction torque measurements, and an electric actuator) and wearable sensors (inertial measurement unit, force sensitive resistors, and electromyography sensors), which allows the generation of smart rehabilitation tools and several motion assistive techniques. The main goal is to present a conceptual overview and functional description of the system and use scenarios of each component. The attachment mechanism of the orthosis to the limb is also highlighted, being composed of a straps system fixed in the mechanical links of the joint. It was noticed that users with distinct lower-limb morphologies can presents difficulties wearing the orthosis, since the device needs constant adjust to align the mechanical and human joints. The system was validated in ground-level walking on healthy subjects, with emphasis on the impact of the device in the user. The subjects reported that the orthosis is comfortable to use, easy to wear, and no issues were raised regarding the aesthetics of the device. Only the weight was assimilated as a possible hindrance (compensated in the future). Future challenges involve the inclusion of an ankle joint in the system and the use of the proposed tool in rehabilitation.This work is supported by the FCT - Fundacao para a Ciencia e Tecnologia - with the reference scholarship SFRH/BD/108309/2015, with the reference project UID/EEA/04436/2013, and by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalizacao (POCI) - with the reference project POCI-01-0145-FEDER-006941, and partially supported with grant RYC-2014-16613 by Spanish Ministry of Economy and Competitiveness

Biomechanical Knee Joint for Exoskeleton

Our senior design project consisted of designing and manufacturing a biomechanically accurate, actuated knee joint to be integrated into an exoskeleton being developed by the Lower Limb Exoskeleton Assist Project (LLEAP), a part of the EMPOWER student association at Cal Poly, San Luis Obispo. As the human knee flexes and extends throughout gait motion, the center of rotation changes. Currently marketed exoskeletons have one point of rotation, which over constrains the knee and causes misalignment between the user and the suit [1]. Our goal was to mimic natural knee joint motion by changing the center or rotation, thus reducing misalignment and limiting power loss. We designed this knee joint for our prospective exoskeleton user: Carlo Ruggiero, a 21-year-old Cal Poly student with a complete C8 injury to his spine which resulted in loss of function and sensation from his chest and below. Our design consists of a linear actuator mounted along the outside of the user’s thigh, which drives a four-bar linkage in line with the user’s knee. After manufacturing and testing our design, it was found that the joint met the necessary power requirements and reached the required angles for human gait. However, the linear actuator that was purchased was too long to fit properly on the user’s leg and is unable to vary its speed. This verification prototype proves that mimicking knee joint motion for exoskeleton applications is feasible but requires integration of a linear actuator with greater power density and the ability for speed control. We also recommend using biomedical imaging to accurately determine the center of rotation throughout actuation. This would allow for tuning of the lengths of the links in the four-bar linkage to match the user’s knee biomechanics more precisely