Evaluation Of Impedance Control On A Powered Hip Exoskeleton

This thesis presents an impedance control strategy for a novel powered hip exoskeleton designed to provide partial assistance and leverage the dynamics of human gait. The control strategy is based on impedance control and provides the user assistance as needed which is determined by the user’s interaction with the exoskeleton. A series elastic element is used to drive the exoskeleton and measures the interaction torque between the user and the device. The device operates in two modes. Free mode is a low impedance state that attempts to provide no assistance. Assist mode increases the gains of the controller to provide assistance as needed. The device was tested on five healthy subjects, and the resulting assistive hip torque was evaluated to determine the ability of the controller to provide gait assistance. The device was evaluated at different speeds to assess the gait speed adaptation performance of the controller. Results show that hip torque assistance range was between 0.3 to 0.5 Nm/kg across the subjects, corresponding to 24% to 40% of the maximum hip torque requirements of healthy adults during walking. The peak power provided by the system is 35 W on average and a peak power of up to 45 W

Learning to Assist Different Wearers in Multitasks: Efficient and Individualized Human-In-the-Loop Adaption Framework for Exoskeleton Robots

One of the typical purposes of using lower-limb exoskeleton robots is to provide assistance to the wearer by supporting their weight and augmenting their physical capabilities according to a given task and human motion intentions. The generalizability of robots across different wearers in multiple tasks is important to ensure that the robot can provide correct and effective assistance in actual implementation. However, most lower-limb exoskeleton robots exhibit only limited generalizability. Therefore, this paper proposes a human-in-the-loop learning and adaptation framework for exoskeleton robots to improve their performance in various tasks and for different wearers. To suit different wearers, an individualized walking trajectory is generated online using dynamic movement primitives and Bayes optimization. To accommodate various tasks, a task translator is constructed using a neural network to generalize a trajectory to more complex scenarios. These generalization techniques are integrated into a unified variable impedance model, which regulates the exoskeleton to provide assistance while ensuring safety. In addition, an anomaly detection network is developed to quantitatively evaluate the wearer's comfort, which is considered in the trajectory learning procedure and contributes to the relaxation of conflicts in impedance control. The proposed framework is easy to implement, because it requires proprioceptive sensors only to perform and deploy data-efficient learning schemes. This makes the exoskeleton practical for deployment in complex scenarios, accommodating different walking patterns, habits, tasks, and conflicts. Experiments and comparative studies on a lower-limb exoskeleton robot are performed to demonstrate the effectiveness of the proposed framework.Comment: 16 pages journal articl

Towards a human-in-the-loop control for a smart orthotic system

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)Stroke is the main cause of paralysis. This pathology has provoked a considerable increase of persons with motor impairments. With a therapy focused on each clinical case, the total or partial recovery can be achieved. Powered orthoses have been developed to promote an effective recover, based on repetitive gait training and user’s active participation. Many control approaches have been developed to control these devices, but none of them promotes an user-oriented strategy focused to the user’s needs. In an attempt of solving this issue, a new approach named Human-in-the-loop is emerging. This strategy allows the adaptation of some assistive parameters based on the user’s energetic cost, promoting a therapy tailored to each end-user needs. However, to estimate the energy expenditure, the use of non-ergonomic sensors, not suitable for clinical context, is required. Thus, it is necessary to find new ways of estimating energy expenditure using wearable and comfortable sensors. In this dissertation, the first steps to introduce the Human-in-the-loop strategy into a powered orthosis are presented. For this purpose, two strategies were developed: a strategy that allows the angular trajectory adaptation in real-time and other that promotes a stiffness adaptation all over the gait cycle. Both strategies were validated with healthy subjects. In the first strategy, the orthosis was able to modify its assistance in a fraction of microseconds, and the end-users were able to follow her with a median error below 10%. Regarding the second strategy, the results show that the orthosis allowed an effective change in the systems’ interaction stiffness, promoting an active participation of each user during its assistance. The energetic impact of using the robotic assistive device is also presented. As it promotes an energy expenditure augmentation in more than 30% in comparison to walk without the device, the necessity of implementing the Human-in-the-loop strategy was highlighted. In an attempt of finding an ergonomic technique to estimate the energetic cost, the use of machine learning algorithms was tested. The results, obtained with a MLP and a LSTM, prove that it is possible to estimate the energy expenditure with a mean error close to 11%. Future work consists in the implementation of the model in real-time and the collection of more data with the aforementioned control approaches, in a way of constructing a more robust model.O AVC é uma das maiores causas de paralisia. Esta patologia, cada vez mais com maior incidência nos jovens, tem provocado um aumento considerável de pessoas com problemas de mobilidade. Com uma terapia focada a cada caso clínico, a recuperação total ou parcial pode ser conseguida. As ortóteses ativas têm vindo a ser desenvolvidas com o propósito de promover uma recuperação eficaz, baseada em treinos repetitivos e numa participação ativa dos utilizadores. Várias abordagens de controlo têm vindo a ser desenvolvidas para controlar estes dispositivos, mas nenhuma delas promove uma estratégia orientada às necessidades do utilizador. Na tentativa de solucionar este problema, uma nova abordagem, designada por Human-in-the-loop está a emergir. Baseada no custo energético, esta estratégia permite adaptar parâmetros da assistência, promovendo uma terapia focada e direcionada a cada utilizador. No entanto, para estimar o custo energético, recorre-se ao uso de sensores que não são adequados para contexto clínico. Assim, torna-se necessário estudar novas formas de estimar o custo energético. Nesta dissertação são apresentados os primeiros passos para introduzir o controlo Human-in-the-loop numa ortótese ativa. Para isso, duas estratégias foram apresentadas: uma estratégia que permite adaptar a trajetória angular da ortótese, em tempo real, e outra que promove a adaptação da complacência do sistema ao longo do ciclo da marcha. Ambas foram validadas com sujeitos saudáveis. Relativamente à primeira abordagem, a ortótese foi capaz de modificar a sua assistência em microssegundos, e os utilizadores foram capazes de a seguir com um erro mediano inferior a 10%. No que diz respeito à segunda abordagem, os resultados mostram que a ortótese promoveu uma alteração eficaz da complacência de interação, promovendo uma participação ativa do utilizador durante a sua assistência. O impacto energético do uso do sistema robótico é, também, apresentado. Promovendo um aumento do custo energético em mais de 30%, a necessidade da estratégia Human-in-the-loop foi realçada. Na tentativa de encontrar uma técnica para estimar o custo energético, recorreu-se ao uso de machine learning. Os resultados, obtidos com uma MLP e uma LSTM, provam que é possível estimar o custo energético com um erro médio próximo dos 11%. Trabalho futuro passa pela implementação do modelo em tempo real e a recolha de mais dados com as abordagens de controlo apresentadas, de forma a construir um modelo mais robusto

Design and Motion Control of a Lower Limb Robotic Exoskeleton

This chapter presents the results of research work on design, actuator selection and motion control of a lower extremity exoskeleton developed to provide legged mobility to spinal cord injured (SCI) individuals. The exoskeleton has two degrees of freedom per leg. Hip and knee joints are actuated in the sagittal plane by using DC servomotors. Additional effort supplied by user’s arms through crutches is defined as user support rate (USR). Experimentally determined USR values are considered in actuator torque computations for achieving a realistic actuator selection. A custom-embedded system is used to control exoskeleton. Reference joint trajectories are determined by using clinical gait analysis (CGA). Three-loop cascade controllers with current, velocity and position feedback are designed for controlling the joint motions of the exoskeleton. A non-linear ARX model is used to determine controller parameters. Overall performance and an assistive effect of WSE-2 are experimentally investigated by conducting tests with a paraplegic patient with T10 complete injury

Tele-impedance based assistive control for a compliant knee exoskeleton

This paper presents a tele-impedance based assistive control scheme for a knee exoskeleton device. The proposed controller captures the user’s intent to generate task-related assistive torques by means of the exoskeleton in different phases of the subject’s normal activity. To do so, a detailed musculoskeletal model of the human knee is developed and experimentally calibrated to best match the user’s kinematic and dynamic behavior. Three dominant antagonistic muscle pairs are used in our model, in which electromyography (EMG) signals are acquired, processed and used for the estimation of the knee joint torque, trajectory and the stiffness trend, in real time. The estimated stiffness trend is then scaled and mapped to a task-related stiffness interval to agree with the desired degree of assistance. The desired stiffness and equilibrium trajectories are then tracked by the exoskeleton’s impedance controller. As a consequence, while minimum muscular activity corresponds to low stiffness, i.e. highly transparent motion, higher co-contractions result in a stiffer joint and a greater level of assistance. To evaluate the robustness of the proposed technique, a study of the dynamics of the human–exoskeleton system is conducted, while the stability in the steady state and transient condition is investigated. In addition, experimental results of standing-up and sitting-down tasks are demonstrated to further investigate the capabilities of the controller. The results indicate that the compliant knee exoskeleton, incorporating the proposed tele-impedance controller, can effectively generate assistive actions that are volitionally and intuitively controlled by the user’s muscle activity

Controller design of a robotic orthosis using sinusoidal-input describing function model

Stroke is one of top leading causes of death in the world and it happens to more than 15 million people yearly. According to the National Stroke Association of Malaysia (NASAM), stroke is the third leading cause of death in Malaysia with around 40,000 cases reported annually. Forty percent of stroke survivors suffer from movement impairments after stroke. My grandfather was one of the victims and he was unable to attend any rehabilitation sessions due to several reasons. Hence, he lost the golden time to regain his movement and freedom. There are a lot of similar cases that happen daily in Malaysia. Besides, as the number of stroke patients increases yearly, the need for physiotherapists or rehabilitation machines equally increases. Hence, a low-cost clinical rehabilitation device is essential to provide assistance for an effective rehabilitation program and substitute the conventional method, as well as to reduce the burden of physiotherapists. In future, the proposed rehabilitation device would benefit not only stroke patients, but any patients who lost their normal walking ability including post-accident patients or those who suffer from spinal cord injury. The rehabilitation device aims to provide training assistance to patients not only in rehabilitation centres but also at home for daily training. The robotic orthosis is planned to be configured based on moving joint angles of human lower extremities. In the first stage of this research, angle-time characteristics for knee and hip swinging motion are utilised as a sagittal motion reference for the rehabilitation devices. The aim of following a proper gait cycle during rehabilitation training is to train patients to perform standing and swinging phases at proper timing and simultaneously provide the correct position reference to the patient during rehabilitation training. This can prevent patients from walking abnormally with an asymmetric gait cycle along or after the rehabilitation program. Besides, various limitations and the bulky structure of other rehabilitation devices lead to the design of the two-link lower limb rehabilitation device. This project aims to develop an assistive robotic rehabilitation device that generates a human gait trajectory for hemiplegic stroke patient gait rehabilitation in future. The shortcomings of other control applications due to environmental conditions and disturbances lead to the implementation of the describing function approach in the development of the devices. A sinusoidal-input describing function (SIDF) approach was implemented to linearize the nonlinear robotic orthosis with linear transfer function. The reason for utilising the SIDF approach is due to the nonlinear actual plant model with the present of load torque disturbances, discontinuous nonlinearities such as saturation and backlash, and also multivariable in the system. The nonlinear properties of the plant were proven in the preliminary stage of the research. A conventional controller, PID control combined with position and trajectory inputs were also applied to the system in the early stage of research. However, the experimental results were not satisfying. Finally, the SIDF approach was chosen to linearize the nonlinear system. Hence, generating a controller is much easier with a linear model of the nonlinear system. A SIDF approach was implemented to generate a controller for the multivariable, nonlinear closed loop system. Firstly, the SIDF approach enables the determination of the linear function of the nonlinear model known as the SIDF model. By utilising the linear model to mimic the behaviour of the nonlinear rehabilitation system, the controller for the nonlinear plant was able to be generated. In this research a controller based on linear control theory technique was used. The MATLAB library was used to design the lead-lag controller for the rehabilitation device. Various simulations such as step responses, tracking and decoupling of both links were performed on the generated controller with the nonlinear model to study the capability of the controller. Besides that, real life experiment testing was carried out to validate the feasibility of the controller designed via the SIDF approach. Simulation and experimental results were obtained, compared, and discussed. The highly accurate responses gained from experimental setup showed the robustness of the controller generated via SIDF approach. The implementation of the SIDF approach in a rehabilitation device (vertical two-link manipulator) is a first and hence, fulfils a novelty requirement for this research

ASSISTIVE DEVICE FOR LOWER EXTREMITY GAIT TRAINING AND ASSISTANCE

Ph.DDOCTOR OF PHILOSOPH

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

Stiffness and position control of a prosthetic wrist by means of an EMG interface

In this paper, we present a novel approach for decoding electromyographic signals from an amputee and for interfacing them with a prosthetic wrist. The model for the interface makes use of electromyographic signals from electrodes placed in agonistic and antagonistic sides of the forearm. The model decodes these signals in order to control both the position and the stiffness of the wrist