Adaptive Compliance Shaping with Human Impedance Estimation

Human impedance parameters play an integral role in the dynamics of strength amplification exoskeletons. Many methods are used to estimate the stiffness of human muscles, but few are used to improve the performance of strength amplification controllers for these devices. We propose a compliance shaping amplification controller incorporating an accurate online human stiffness estimation from surface electromyography (sEMG) sensors and stretch sensors connected to the forearm and upper arm of the human. These sensor values along with exoskeleton position and velocity are used to train a random forest regression model that accurately predicts a person's stiffness despite varying movement, relaxation, and muscle co-contraction. Our model's accuracy is verified using experimental test data and the model is implemented into the compliance shaping controller. Ultimately we show that the online estimation of stiffness can improve the bandwidth and amplification of the controller while remaining robustly stable.Comment: 8 pages, 9 figures, Accepted for publication at the 2020 American Control Conference. Copyright IEEE 202

Effect of gait speed on trajectory prediction using deep learning models for exoskeleton applications

Gait speed is an important biomechanical determinant of gait patterns, with joint kinematics being influenced by it. This study aims to explore the effectiveness of fully connected neural networks (FCNNs), with a potential application for exoskeleton control, in predicting gait trajectories at varying speeds (specifically, hip, knee, and ankle angles in the sagittal plane for both limbs). This study is based on a dataset from 22 healthy adults walking at 28 different speeds ranging from 0.5 to 1.85 m/s. Four FCNNs (a generalised-speed model, a low-speed model, a high-speed model, and a low-high-speed model) are evaluated to assess their predictive performance on gait speeds included in the training speed range and on speeds that have been excluded from it. The evaluation involves short-term (one-step-ahead) predictions and long-term (200-time-step) recursive predictions. The results show that the performance of the low- and high-speed models, measured using the mean absolute error (MAE), decreased by approximately 43.7% to 90.7% when tested on the excluded speeds. Meanwhile, when tested on the excluded medium speeds, the performance of the low-high-speed model improved by 2.8% for short-term predictions and 9.8% for long-term predictions. These findings suggest that FCNNs are capable of interpolating to speeds within the maximum and minimum training speed ranges, even if not explicitly trained on those speeds. However, their predictive performance decreases for gaits at speeds beyond or below the maximum and minimum training speed ranges

Human-robot interaction for assistive robotics

This dissertation presents an in-depth study of human-robot interaction (HRI) withapplication to assistive robotics. In various studies, dexterous in-hand manipulation is included, assistive robots for Sit-To-stand (STS) assistance along with the human intention estimation. In Chapter 1, the background and issues of HRI are explicitly discussed. In Chapter 2, the literature review introduces the recent state-of-the-art research on HRI, such as physical Human-Robot Interaction (HRI), robot STS assistance, dexterous in hand manipulation and human intention estimation. In Chapter 3, various models and control algorithms are described in detail. Chapter 4 introduces the research equipment. Chapter 5 presents innovative theories and implementations of HRI in assistive robotics, including a general methodology of robotic assistance from the human perspective, novel hardware design, robotic sit-to-stand (STS) assistance, human intention estimation, and control

Adaptive compliance shaping with human impedance estimation

Robotics has been a promising and popular research area for the past few decades. Among various applications of robotic, in many cases, human are involved in different manners. Therefore, as an important sub research area of robotics, human robot interaction has drawn decent attention recently. It has been deeply and widely studied. For human robot interaction, human play an important role. Undoubtedly, the more we know about human, the easier we can do human robot interaction and the better performance we can achieve in human robot interaction. One fascinating research topic of human robot interaction would be human in exoskeleton, where human play a key role in the mechanical design of exoskeleton as well as the control strategy design of exoskeleton. Among all those applications, the augmentation exoskeleton is especially interesting due to its ability to amplify human. As mentioned previously, human properties are important for the design of exoskeleton. Unfortunately, despite many inspiring and deep studies about human properties and various proposed human models, human remains to be a complicated system that is hard to predict and model. Furthermore, human is a dynamic system whose parameters keep changing with time, bringing more challenges. As we all know, limited understanding of the control plant will limit the performance of the controller and bring difficulties in the design of a controller. In fact, the performance of many existed controller for augmentation exoskeleton is limited by using conservative values of human property parameters. A straightforward way to solve this problem is to estimate human properties online. Under this circumstance, the main challenges are to develop a control strategy, whose performance can be exploited using the estimation of human properties and a reliable method to online estimate human properties. This thesis mainly presents an adaptive compliance shaping control strategy with human impedance estimation and a brief review of a newly proposed complex stiffness model of human.Mechanical Engineerin

Learning Interaction Primitives for Biomechanical Prediction

abstract: This dissertation is focused on developing an algorithm to provide current state estimation and future state predictions for biomechanical human walking features. The goal is to develop a system which is capable of evaluating the current action a subject is taking while walking and then use this to predict the future states of biomechanical features. This work focuses on the exploration and analysis of Interaction Primitives (Amor er al, 2014) and their relevance to biomechanical prediction for human walking. Built on the framework of Probabilistic Movement Primitives, Interaction Primitives utilize an EKF SLAM algorithm to localize and map a distribution over the weights of a set of basis functions. The prediction properties of Bayesian Interaction Primitives were utilized to predict real-time foot forces from a 9 degrees of freedom IMUs mounted to a subjects tibias. This method shows that real-time human biomechanical features can be predicted and have a promising link to real-time controls applications.Dissertation/ThesisMasters Thesis Electrical Engineering 201

Studies on gait control using a portable pneumatically powered ankle-foot orthosis (PPAFO) during human walking

A powered ankle-foot orthosis (AFO) can be very useful for people with neuromuscular injury. Control of powered AFOs will be more efficient to provide assistance to individuals with lower limb muscle impairments if we can identify different gait events during walking. A walking or gait cycle can be divided into multiple phases and sub-phases by proper gait event detection, and these phases/sub-phases are associated with one of the three main functional tasks during the gait cycle: loading response, forward propulsion, and limb advancement. The gait cycle of one limb can also be characterized by examining the limb’s behavior over one stride, which can be quantified as 0% to 100% of a gait cycle (GC). One easy approach to identify gait events is by checking whether sensor signals go above/below a predetermined threshold. By estimation of a walker’s instantaneous state, as represented by a specific percentage of the gait cycle (from states 0 to 100, which correlate with 0% to 100% GC), we can efficiently detect the various gait events more accurately. Our Human Dynamics and Controls Laboratory previously developed the portable pneumatically powered ankle-foot orthosis (PPAFO), which was capable of providing torque in both plantarflexion and dorsiflexion directions at the ankle. There were three types of sensor attached with the PPAFO (two force sensitive resistors and an angle sensor). In this dissertation, three aspects of effective control strategies for the PPAFO have been proposed. In the first study, two improved and reliable state estimators (Modified Fractional Time (MFT) and Artificial Neural Network (ANN)) were proposed for identifying when the limb with the PPAFO was at a certain percentage of the gait cycle. A correct estimation of percentage of gait cycle will assist with detecting specific gait events more accurately. The performance of new estimators was compared to a previously developed Fractional Time state estimation technique. To control a powered AFO using these estimators, however, detection of proper actuation timing is necessary. In the second study, a supervised learning algorithm to classify the appropriate start timing for plantarflexor actuation was proposed. Proper actuation timing has only been addressed in the literature in terms of functional efficiency or metabolic cost during walking. In this study, we will explore identifying the plantarflexor actuation timing in terms of biomechanics outcomes of human walking using a machine learning based algorithm. The third study investigated the recognition of different gait modes encountered during walking. The actuation scheme plays a significant role in walking on level ground, stair descent or stair ascent modes. The wrong actuation scheme for a given mode can cause falls or trips. A gait mode recognition technique was developed for detecting these different modes by attaching an inertial measurement unit and using a classifier based on artificial neural networks. This new algorithm improves upon the current one step delay limitation found as a drawback of a previously developed technique. Overall, this dissertation focused on addressing some important issues related to control of powered AFO that ultimately will help to assist people wearing the device in daily life situations during walking. The proposed approaches and algorithms introduced in this dissertation showed very promising results that proved that these methods can successfully improve the control system of powered AFOs

Joint Trajectory Generation and High-level Control for Patient-tailored Robotic Gait Rehabilitation

This dissertation presents a group of novel methods for robot-based gait rehabilitation which were developed aiming to offer more individualized therapies based on the specific condition of each patient, as well as to improve the overall rehabilitation experience for both patient and therapist. A novel methodology for gait pattern generation is proposed, which offers estimated hip and knee joint trajectories corresponding to healthy walking, and allows the therapist to graphically adapt the reference trajectories in order to fit better the patient's needs and disabilities. Additionally, the motion controllers for the hip and knee joints, mobile platform, and pelvic mechanism of an over-ground gait rehabilitation robotic system are also presented, as well as some proposed methods for assist as needed therapy. Two robot-patient synchronization approaches are also included in this work, together with a novel algorithm for online hip trajectory adaptation developed to reduce obstructive forces applied to the patient during therapy with compliant robotic systems. Finally, a prototype graphical user interface for the therapist is also presented

The Development of an assistive chair for elderly with sit to stand problems

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyStanding up from a seated position, known as sit-to-stand (STS) movement, is one of the most frequently performed activities of daily living (ADLs). However, the aging generation are often encountered with STS issues owning to their declined motor functions and sensory capacity for postural control. The motivated is rooted from the contemporary market available STS assistive devices that are lack of genuine interaction with elderly users. Prior to the software implementation, the robot chair platform with integrated sensing footmat is developed with STS biomechanical concerns for the elderly. The work has its main emphasis on recognising the personalised behavioural patterns from the elderly users’ STS movements, namely the STS intentions and personalised STS feature prediction. The former is known as intention recognition while the latter is defined as assistance prediction, both achieved by innovative machine learning techniques. The proposed intention recognition performs well in multiple subjects scenarios with different postures involved thanks to its competence of handling these uncertainties. To the provision of providing the assistance needed by the elderly user, a time series prediction model is presented, aiming to configure the personalised ground reaction force (GRF) curve over time which suggests successful movement. This enables the computation of deficits between the predicted oncoming GRF curve and the personalised one. A multiple steps ahead prediction into the future is also implemented so that the completion time of actuation in reality is taken into account