Optimal Energy Shaping Control for a Backdrivable Hip Exoskeleton

Task-dependent controllers widely used in exoskeletons track predefined trajectories, which overly constrain the volitional motion of individuals with remnant voluntary mobility. Energy shaping, on the other hand, provides task-invariant assistance by altering the human body's dynamic characteristics in the closed loop. While human-exoskeleton systems are often modeled using Euler-Lagrange equations, in our previous work we modeled the system as a port-controlled-Hamiltonian system, and a task-invariant controller was designed for a knee-ankle exoskeleton using interconnection-damping assignment passivity-based control. In this paper, we extend this framework to design a controller for a backdrivable hip exoskeleton to assist multiple tasks. A set of basis functions that contains information of kinematics is selected and corresponding coefficients are optimized, which allows the controller to provide torque that fits normative human torque for different activities of daily life. Human-subject experiments with two able-bodied subjects demonstrated the controller's capability to reduce muscle effort across different tasks

Integral admittance shaping: A unified framework for active exoskeleton control

© 2015 Elsevier B.V. Current strategies for lower-limb exoskeleton control include motion intent estimation, which is subject to inaccuracies in muscle torque estimation as well as modeling error. Approaches that rely on the phases of a uniform gait cycle have proven effective, but lack flexibility to aid other kinds of movement. This research aims at developing a more versatile control that can assist the lower limbs independently of the movement attempted. Our control strategy is based on modifying the dynamic response of the human limbs, specifically their mechanical admittance. Increasing the admittance makes the lower limbs more responsive to any muscle torque generated by the human user. We present Integral Admittance Shaping, a unified mathematical framework for: (a) determining the desired dynamic response of the coupled system formed by the human limb and the exoskeleton, and (b) synthesizing an exoskeleton controller capable of achieving said response. The present control formulation focuses on single degree-of-freedom exoskeleton devices providing performance augmentation. The algorithm generates a desired shape for the frequency response magnitude of the integral admittance (torque-to-angle relationship) of the coupled system. Simultaneously, it generates an optimal feedback controller capable of achieving the desired response while guaranteeing coupled stability and passivity. The potential effects of the exoskeleton's assistance are motion amplification for the same joint torque, and torque reduction for the same joint motion. The robustness of the derived exoskeleton controllers to parameter uncertainties is analyzed and discussed. Results from initial trials using the controller on an experimental exoskeleton are presented as well

An admittance shaping controller for exoskeleton assistance of the lower extremities

We present a method for lower-limb exoskeleton control that defines assistance as a desired dynamic response for the human leg. Wearing the exoskeleton can be seen as replacing the leg's natural admittance with the equivalent admittance of the coupled system. The control goal is to make the leg obey an admittance model defined by target values of natural frequency, peak magnitude and zero-frequency response. No estimation of muscle torques or motion intent is necessary. Instead, the controller scales up the coupled system's sensitivity transfer function by means of a compensator employing positive feedback. This approach increases the leg's mobility and makes the exoskeleton an active device capable of performing net positive work on the limb. Although positive feedback is usually considered destabilizing, here performance and robust stability are successfully achieved through a constrained optimization that maximizes the system's gain margins while ensuring the desired location of its dominant poles

Oscillator-based assistance of cyclical movements: model-based and model-free approaches

In this article, we propose a new method for providing assistance during cyclical movements. This method is trajectory-free, in the sense that it provides user assistance irrespective of the performed movement, and requires no other sensing than the assisting robot's own encoders. The approach is based on adaptive oscillators, i.e., mathematical tools that are capable of learning the high level features (frequency, envelope, etc.) of a periodic input signal. Here we present two experiments that we recently conducted to validate our approach: a simple sinusoidal movement of the elbow, that we designed as a proof-of-concept, and a walking experiment. In both cases, we collected evidence illustrating that our approach indeed assisted healthy subjects during movement execution. Owing to the intrinsic periodicity of daily life movements involving the lower-limbs, we postulate that our approach holds promise for the design of innovative rehabilitation and assistance protocols for the lower-limb, requiring little to no user-specific calibratio

Application of wearable sensors in actuation and control of powered ankle exoskeletons: a Comprehensive Review

Powered ankle exoskeletons (PAEs) are robotic devices developed for gait assistance, rehabilitation, and augmentation. To fulfil their purposes, PAEs vastly rely heavily on their sensor systems. Human–machine interface sensors collect the biomechanical signals from the human user to inform the higher level of the control hierarchy about the user’s locomotion intention and requirement, whereas machine–machine interface sensors monitor the output of the actuation unit to ensure precise tracking of the high-level control commands via the low-level control scheme. The current article aims to provide a comprehensive review of how wearable sensor technology has contributed to the actuation and control of the PAEs developed over the past two decades. The control schemes and actuation principles employed in the reviewed PAEs, as well as their interaction with the integrated sensor systems, are investigated in this review. Further, the role of wearable sensors in overcoming the main challenges in developing fully autonomous portable PAEs is discussed. Finally, a brief discussion on how the recent technology advancements in wearable sensors, including environment—machine interface sensors, could promote the future generation of fully autonomous portable PAEs is provided

Brain activity on encoding different textures EEG signal acquisition with ExoAtlet®

Powered exoskeletons play a crucial role in the rehabilitation field improving the quality of life for those who need them. Thus, being a major contribution for patients integration into society, providing them with more autonomy and freedom. In spite of these positive outcomes, a thorough description of the brain correlates connected to exoskeleton control is still needed. For instance, the perception of different pavement textures when wearing an exoskeleton is probably going to cause changes in cerebral activity, which could impact both sensory encoding and Brain-Computer Interface (BCI) control. Therefore, the main goal of this work is to describe the brain activity response to different textured pavements using ExoAtlet ® powered exoskeleton. In order to measure, process, analyze and classify the impact of different textures on neurophysiological rhythms, 4-minute signals were recorded by Electroencephalogram (EEG) with a 16-channel cap (actiCAP by Brain Products). Each of the three experimental subjects was instructed to walk in place on four different types of pavement (flat, carpet, foam, and rubber circles) with and without the exoskeleton, for a total of eight different experimental conditions. A counterbalanced design was applied, and informed consent was obtained from participants (Committee for Health Sciences of the Universidade Católica Portuguesa - 99/2022). Additionally, four machine learning methods, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), and Artificial Neural Network (ANN), were selected in order to analyze three distinct classification problems. This study found that there were changes associated with the delta frequency band for electrodes C3 and C4, and when comparing the classifiers performance, LDA presented the best accuracy across the three classification problems involving all subjects. Thereby, this work concludes that the results are consistent with the hypothesis that sensory processing of pavement textures during exoskeleton control induces neural changes and delta variations of the C3 and C4 electrodes. Additionally, LDA demonstrated the best performance across the three classifications of subject-independent problems.Os exoesqueletos motorizados desempenham um papel crucial no campo da reabilitação, melhorando a qualidade de vida das pessoas que deles necessitam. Deste modo, são um contributo importante para que os pacientes com condições físicas limitadas sejam mais facilmente integrados na sociedade, proporcionando-lhes mais autonomia e liberdade. Embora esta tecnologia tenha os seus aspetos positivos, ainda existe a necessidade de descrever os correlatos cerebrais direcionados para o controlo do exoesqueleto. Por exemplo, a percepção de diferentes pavimentos quando se usa um exoesqueleto vai provavelmente causar alterações na actividade cerebral, o que pode ter impacto tanto na codificação sensorial como no controlo da interface cérebro-máquina (BCI). Deste modo, o principal objetivo deste trabalho é descrever a atividade cerebral às diferentes texturas dos pavimentos, utilizando o exoesqueleto ExoAtlet ®. A fim de medir, processar, analisar e classificar o impacto de diferentes texturas em ritmos neurofisiológicos, foram registados sinais de 4 minutos atravês the Eletroencefalograma (EEG) com uma touca de 16 canais (actiCAP by Brain Products). Cada um dos três voluntários foi instruído a dar passos no lugar em quatro tipos diferentes de pavimento (plano, alcatifa, espuma, e círculos de borracha) com e sem o exosqueleto, num total de oito condições experimentais diferentes. Foi aplicado um desenho contrabalançado e foi obtido o consentimento informado dos participantes (Comissão para as Ciências da Saúde da Universidade Católica Portuguesa - 99/2022). Adicionalmente, foram selecionados quatro classificadores: máquinas de vetores de suporte (SVM), k-vizinhos mais próximos (KNN), análise discriminante linear (LDA) e redes neuronais artificiais (ANN) para analisar três problemas de classificação distintos. Os resultados obtidos por este estudo demonstraram que existiam alterações associadas à banda de frequência delta para os eléctrodos C3 e C4 e, ao comparar o desempenho dos classificadores, o LDA apresentou a melhor exatidão nos três problemas de classificação envolvendo todos os sujeitos. Assim, estes resultados são consistentes com a hipótese de que o processamento sensorial dos pavimentos durante o controlo do exoesqueleto induz alterações neuronais

Human Activity Recognition and Control of Wearable Robots

abstract: Wearable robotics has gained huge popularity in recent years due to its wide applications in rehabilitation, military, and industrial fields. The weakness of the skeletal muscles in the aging population and neurological injuries such as stroke and spinal cord injuries seriously limit the abilities of these individuals to perform daily activities. Therefore, there is an increasing attention in the development of wearable robots to assist the elderly and patients with disabilities for motion assistance and rehabilitation. In military and industrial sectors, wearable robots can increase the productivity of workers and soldiers. It is important for the wearable robots to maintain smooth interaction with the user while evolving in complex environments with minimum effort from the user. Therefore, the recognition of the user's activities such as walking or jogging in real time becomes essential to provide appropriate assistance based on the activity. This dissertation proposes two real-time human activity recognition algorithms intelligent fuzzy inference (IFI) algorithm and Amplitude omega ($A \omega$) algorithm to identify the human activities, i.e., stationary and locomotion activities. The IFI algorithm uses knee angle and ground contact forces (GCFs) measurements from four inertial measurement units (IMUs) and a pair of smart shoes. Whereas, the $A \omega$ algorithm is based on thigh angle measurements from a single IMU. This dissertation also attempts to address the problem of online tuning of virtual impedance for an assistive robot based on real-time gait and activity measurement data to personalize the assistance for different users. An automatic impedance tuning (AIT) approach is presented for a knee assistive device (KAD) in which the IFI algorithm is used for real-time activity measurements. This dissertation also proposes an adaptive oscillator method known as amplitude omega adaptive oscillator ($A\omega AO$) method for HeSA (hip exoskeleton for superior augmentation) to provide bilateral hip assistance during human locomotion activities. The $A \omega$ algorithm is integrated into the adaptive oscillator method to make the approach robust for different locomotion activities. Experiments are performed on healthy subjects to validate the efficacy of the human activities recognition algorithms and control strategies proposed in this dissertation. Both the activity recognition algorithms exhibited higher classification accuracy with less update time. The results of AIT demonstrated that the KAD assistive torque was smoother and EMG signal of Vastus Medialis is reduced, compared to constant impedance and finite state machine approaches. The $A\omega AO$ method showed real-time learning of the locomotion activities signals for three healthy subjects while wearing HeSA. To understand the influence of the assistive devices on the inherent dynamic gait stability of the human, stability analysis is performed. For this, the stability metrics derived from dynamical systems theory are used to evaluate unilateral knee assistance applied to the healthy participants.Dissertation/ThesisDoctoral Dissertation Aerospace Engineering 201

Towards a safe and responsive control framework for human-centered robots

Human-centered robots are a specific kind of service robot, which interact with humans physically or cognitively and help humans with tasks in uncertain environments. They can be humanoid robots, exoskeletons, or manipulators and mobile platforms that provide us good services. However, human-centered robots are still not perfect enough for us to use nowadays. On the one hand, human-centered robots are still slow and inefficient for their tasks because the human inputs and dynamics that they react to are uncertain, immeasurable, or even completely unknown. On the other hand, human-centered robots face much more complicated safety requirements than other kinds of robots because humans are dynamic and vulnerable during physical human-robot interaction. To resolve these issues of human-centered robots, the work in this dissertation explores new models for reducing human uncertainty and new control algorithms for improving safety warranty. The first half of this dissertation introduces a complex stiffness model for describing the uncertain human impedance. The discovery of this new model is motivated to explain the observation of a consistent damping ratio of a human versus different environmental dynamics. It replaces the linear damping term in a conventional mass-spring-damping model with a hysteretic damping term, an imaginary value in the frequency domain. Because of the correlation between the stiffness term and the newly discovered hysteretic damping term in the complex stiffness model, we can significantly reduce the human impedance uncertainty. Based on the complex stiffness model, we can adopt nonlinear control strategies for improving the responsiveness and the human-friendliness of human-centered robots. The second half of this dissertation introduces the concept of a barrier pair, which consists of a barrier function and a controller for the safety verification and warranty of a human-centered robot. We obtain a barrier pair by solving an optimization problem subject to a series of linear matrix inequalities representing the state-space, input, and stability constraints of a human-centered robot. By incorporating sampling-based methods into the synthesis of barrier pairs, human-centered robots can guarantee safe operation with non-convex state-space constraints. The sampling-based barrier pair method helps us construct a control framework of human-robot shared autonomy. A human-centered robot in this control framework uses an inference of a human's objective to figure out how to assist the human and prevent the human from potential accidents.Mechanical Engineerin