Human Performance Augmentation Research Articles

An integrated evaluation approach of wearable lower limb exoskeletons for human performance augmentation

Wearable robots have been growing exponentially during the past years and it is crucial to quantify the performance effectiveness and to convert them into practical benchmarks. Although there exist some common metrics such as metabolic cost, many other characteristics still needs to be presented and demonstrated. In this study, we developed an integrated evaluation (IE) approach of wearable exoskeletons of lower limb focusing on human performance augmentation. We proposed a novel classification of trial tasks closely related to exoskeleton functions, which were divided into three categories, namely, basic trial at the preliminary phase, semi-reality trial at the intermediate phase, and reality trial at the advanced phase. In the present study, the IE approach has been exercised with a subject who wore an active power-assisted knee (APAK) exoskeleton with three types of trial tasks, including walking on a treadmill at a certain angle, walking up and down on three-step stairs, and ascending in 11-storey stairs. Three wearable conditions were carried out in each trial task, i.e. with unpowered exoskeleton, with powered exoskeleton, and without the exoskeleton. Nine performance indicators (PIs) for evaluating performance effectiveness were adopted basing on three aspects of goal-level, task-based kinematics, and human–robot interactions. Results indicated that compared with other conditions, the powered APAK exoskeleton make generally lesser heart rate (HR), Metabolic equivalent (METs), biceps femoris (BF) and rectus femoris (RF) muscles activation of the subject at the preliminary phase and intermediate phase, however, with minimal performance augmentation at advanced phase, suggesting that the APAK exoskeleton is not suitable for marketing and should be further improved. In the future, continuous iterative optimization for the IE approach may help the robot community to attain a comprehensive benchmarking methodology for robot-assisted locomotion more efficiently.

Sensitivity Adaptation of Lower-Limb Exoskeleton for Human Performance Augmentation Based on Deep Reinforcement Learning

The lower-limb exoskeleton for human performance augmentation (LEHPA) in sensitivity amplification control (SAC) is vulnerable to model parameter uncertainties and unmodeled dynamics due to its large sensitivity to external disturbances. This paper proposes sensitivity adaptation based on deep reinforcement learning (SADRL) to reduce the dependence on the model accuracy and tackle the ever-changing human-exoskeleton interaction (HEI) dynamics by interpreting the sensitivity adjustment as a Markov Decision Process (MDP). The agent learns an appropriate sensitivity for each exoskeleton state. To train the control policy safely and efficiently, a multibody simulation environment is created to implement the training process, accompanied by a novel hybrid inverse-forward dynamics simulation method to carry out the simulation. For comparison purposes, the SAC controller is introduced as a benchmark. A novel performance evaluation method based on the HEI forces at the back, thighs, and shanks is proposed to evaluate the control effect of the trained SADRL controller quantitatively. The SADRL controller is compared with the SAC controller at five specified walking speeds, resulting in a lumped HEI force ratio of 0.54. The total decrease of HEI forces demonstrates the superior control effect of the SADRL strategy.

End-to-End High-Level Control of Lower-Limb Exoskeleton for Human Performance Augmentation Based on Deep Reinforcement Learning

End-to-End High-Level Control of Lower-Limb Exoskeleton for Human Performance Augmentation Based on Deep Reinforcement Learning

Human performance augmentation: the importance of integrative physiological quantification.

In recent years, there has been an explosion of new approaches (technological, methodological, pharmacological, etc.) designed to improve physical performance for athletes, the military and in other applications. The goal of the present discussion is to review and quantify several ways in which physiology can provide important insights about which tools may lead to improved performance (and may therefore be worth resource investment) and which tools are less likely to provide meaningful enhancement. To address these objectives, we review examples of technological solutions/approaches in terms of the magnitude of their potential (or actual) influences: transformational, moderate, ineffective or undetermined. As one example, if there were a technology which significantly increased arterial oxygen partial pressure by 10%, this would be relatively meaningless in healthy people resting at sea level, where it would have a minimal effect on arterial oxygen content. However, there might be specific situations where such an effect would be very helpful, including at high altitude or in some patient populations. We discuss the importance of quantitative evaluation of putative approaches to performance enhancement and highlight the important role of integrative physiologists in the development and critical appraisal of these approaches.

A Deep Learning-Based Computer Aided Detection (CAD) System for Difficult-to-Detect Brain Metastases

We sought to develop a computer-aided detection (CAD) system that optimally augments human performance, excelling especially at identifying small inconspicuous brain metastases (BMs), by training a convolutional neural network on a unique magnetic resonance imaging (MRI) data set containing subtle BMs that were not detected prospectively during routine clinical care. Patients receiving stereotactic radiosurgery (SRS) for BMs at our institution from 2016 to 2018 without prior brain-directed therapy or small cell histology were eligible. For patients who underwent 2 consecutive courses of SRS, treatment planning MRIs from their initial course were reviewed for radiographic evidence of an emerging metastasis at the same location as metastases treated in their second SRS course. If present, these previously unidentified lesions were contoured and categorized as retrospectively identified metastases (RIMs). RIMs were further subcategorized according to whether they did (+DC) or did not (-DC) meet diagnostic imaging-based criteria to definitively classify them as metastases based upon their appearance in the initial MRI alone. Prospectively identified metastases (PIMs) from these patients, and from patients who only underwent a single course of SRS, were also included. An open-source convolutional neural network architecture was adapted and trained to detect both RIMs and PIMs on thin-slice, contrast-enhanced, spoiled gradient echo MRIs. Patients were randomized into 5 groups: 4 for training/cross-validation and 1 for testing. One hundred thirty-five patients with 563 metastases, including 72 RIMS, met criteria. For the test group, CAD sensitivity was 94% for PIMs, 80% for +DC RIMs, and 79% for PIMs and +DC RIMs with diameter <3 mm, with a median of 2 false positives per patient and a Dice coefficient of 0.79. Our CAD model, trained on a novel data set and using a single common MR sequence, demonstrated high sensitivity and specificity overall, outperforming published CAD results for small metastases and RIMs - the lesion types most in need of human performance augmentation.

Corrugated Diaphragm Actuator for Soft Robotic Applications

AbstractSoft robotics is projected to have a significant impact on healthcare, industry, and the military to deliver assistance in rehabilitation, daily living activities, repetitive motion tasks, and human performance augmentation. Many attempts have been made for application-specific robotic joints, robots, and exoskeletons using various actuator types, materials, and designs. The progress of creating soft robotic systems can be accelerated if a set of actuators with defined characteristics were developed, similar to conventional robotic actuators, which can be assembled to create desired systems including exoskeletons and end effectors. This work presents the design methodology of such a modular actuator, created with a novel corrugated diaphragm that can apply linear displacement, angular displacement, and force. This modular actuator approach allows for creating various robotic joints by arranging them into different configurations. The modular corrugated diaphragm actuator concept was validated through numerical simulation, fabrication, and testing. Linear displacement, angular displacement, and force characteristics were shown for a single module and in multi-module assemblies. Actuator assemblies that are configured in a serial and parallel manner were investigated to demonstrate the applicability and versatility of the concept of the modular corrugated diaphragm actuator for creating single and multi-degree-of-freedom joints.

Precision force control of an underactuated stance leg exoskeleton for human performance augmentation

Lower limb exoskeleton which augments the human performance is a wearable human–machine integrated system used to assist people carrying heavy loads. Recently, underactuated lower limb exoskeleton systems with some passive joints become more and more attractive due to the advantages of smaller weight, lower system energy consumption and lower cost. However, because of the less of control inputs, the existed control methods of fully actuated exoskeletons cannot be extended to underactuated systems, which makes the robust controller design of underactuated lower limb exoskeletons becomes more challenged. This article focuses on the high-performance human–machine interaction force control design of underactuated lower limb exoskeletons with passive ankle joint. In order to solve the reduction of control inputs, the holonomic constraint from the wearer is considered, which help transform the dynamics of 3-degree-of-freedom underactuated exoskeleton in joint space into a 2-degree-of-freedom fully actuated system in Cartesian space. A two-level interaction force controller using adaptive robust control algorithm is proposed to effectively address the negative effect of various model uncertainties and external disturbances. In order to facilitate the control parameter selection, a gain tuning method is also presented. Comparative simulations are carried out, which indicate that the proposed two-level interaction force controller achieves smaller interaction force and better robust performance to various modeling errors and disturbances.

How to run 50% faster without external energy.

Technological innovations may enable next-generation running shoes to provide unprecedented mobility. But how could a running shoe increase the speed of motion without providing external energy? We found that the top speed of running may be increased more than 50% using a catapult-like exoskeleton device, which does not provide external energy. Our finding uncovers the hidden potential of human performance augmentation via unpowered robotic exoskeletons. Our result may lead to a new-generation of augmentation devices developed for sports, rescue operations, and law enforcement, where humans could benefit from increased speed of motion.

Ranking Importance of Exoskeleton Design Aspects

The objective of this research project was to determine what a conglomerate of professionals consider as the most important metrics to consider when designing an exoskeleton for training. Over 400 researchers, engineers, and scientists were polled in a ranked order survey covering more than 50 different aspects in engineering design. These aspects were identified from a cogent literature review for consideration. While there are a slew of papers covering the results of exoskeleton designs as posture support mechanisms, rehabilitation mechanisms, tools to assist or replace body functions, and human performance augmentation, few cover what aspects were considered in the engineering design phase.

Biomechanics and energetics of walking in powered ankle exoskeletons using myoelectric control versus mechanically intrinsic control

BackgroundControllers for assistive robotic devices can be divided into two main categories: controllers using neural signals and controllers using mechanically intrinsic signals. Both approaches are prevalent in research devices, but a direct comparison between the two could provide insight into their relative advantages and disadvantages. We studied subjects walking with robotic ankle exoskeletons using two different control modes: dynamic gain proportional myoelectric control based on soleus muscle activity (neural signal), and timing-based mechanically intrinsic control based on gait events (mechanically intrinsic signal). We hypothesized that subjects would have different measures of metabolic work rate between the two controllers as we predicted subjects would use each controller in a unique manner due to one being dependent on muscle recruitment and the other not.MethodsThe two controllers had the same average actuation signal as we used the control signals from walking with the myoelectric controller to shape the mechanically intrinsic control signal. The difference being the myoelectric controller allowed step-to-step variation in the actuation signals controlled by the user’s soleus muscle recruitment while the timing-based controller had the same actuation signal with each step regardless of muscle recruitment.ResultsWe observed no statistically significant difference in metabolic work rate between the two controllers. Subjects walked with 11% less soleus activity during mid and late stance and significantly less peak soleus recruitment when using the timing-based controller than when using the myoelectric controller. While walking with the myoelectric controller, subjects walked with significantly higher average positive and negative total ankle power compared to walking with the timing-based controller.ConclusionsWe interpret the reduced ankle power and muscle activity with the timing-based controller relative to the myoelectric controller to result from greater slacking effects. Subjects were able to be less engaged on a muscle level when using a controller driven by mechanically intrinsic signals than when using a controller driven by neural signals, but this had no affect on their metabolic work rate. These results suggest that the type of controller (neural vs. mechanical) is likely to affect how individuals use robotic exoskeletons for therapeutic rehabilitation or human performance augmentation.

On the Role of Physical Interaction on Performance of Object Manipulation by Dyads.

Human physical interactions can be intrapersonal, e.g., manipulating an object bimanually, or interpersonal, e.g., transporting an object with another person. In both cases, one or two agents are required to coordinate their limbs to attain the task goal. We investigated the physical coordination of two hands during an object-balancing task performed either bimanually by one agent or jointly by two agents. The task consisted of a series of static (holding) and dynamic (moving) phases, initiated by auditory cues. We found that task performance of dyads was not affected by different pairings of dominant and non-dominant hands. However, the spatial configuration of the two agents (side-by-side vs. face-to-face) appears to play an important role, such that dyads performed better side-by-side than face-to-face. Furthermore, we demonstrated that only individuals with worse solo performance can benefit from interpersonal coordination through physical couplings, whereas the better individuals do not. The present work extends ongoing investigations on human-human physical interactions by providing new insights about factors that influence dyadic performance. Our findings could potentially impact several areas, including robotic-assisted therapies, sensorimotor learning and human performance augmentation.

Adaptive Robust Backstepping Force Control of 1-DOF Joint Exoskeleton for Human Performance Augmentation

The exoskeleton for human performance augmentation is a human-machine interaction system whose control goal is to minimize the human machine interaction force while carrying external loads and following human motion. This paper addresses the dynamics and the interaction force control of a 1-DOF joint exoskeleton. A backstepping control method based on the whole dynamics is proposed to overcome the bandwidth restriction of the commonly used cascade control. Lager bandwidth can be achieved by considering the coupling effect between different layers of dynamics. Moreover, a backstepping adaptive robust control(ARC) algorithm is developed to deal with the effect of parametric uncertainties and uncertain nonlinearities of the system. It is theoretically shown that the proposed adaptive robust backstepping force controller not only achieves good robust performance to model uncertainty, but also faster closed-loop responses and smaller interaction forces. All these theoretical results have been validated by simulation.

A Neurostimulation-based Advanced Training System for Human Performance Augmentation

A growing body of evidence suggests that trans-cranial neurostimulation can enhance key cognitive faculties associated with human performance across a wide range of domains including enhanced executive functions, perception and attention, motor performance, and memory enhancement [1-6]. However, multiple studies have found that changing the neurostimulation parameters can cause drastically different neural responses. Trans-cranial direct current stimulation (tDCS) enhances or suppresses the activation of neurons depending on the polarity of the electric field within the neural tissue.

Nanotechnology and Medicine

The application of nanotechnology to the prevention and treatment of human diseases holds great promise, but has great hurdles. Nanomaterials must be biocompatible, non-toxic and functional in biologic (wet) conditions and well enough defined to pass the scrutiny of regulatory agencies. Early applications of nanomaterials will likely involve the development of medications that take advantage of unique aspects of nanostructures to achieve or enhance therapeutic activity. Examples will be provided for the design, synthesis and analysis of therapeutic nanomaterials where distinct kinds of attached molecules allow for unique therapeutic functions. These applications include antimicrobial compounds, drug and gene delivery and functional imaging. Concepts of future nanotechnology applications such as cellular engineering, human performance augmentation and genetic manipulation for the treatment of human disease will be addressed.