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

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 Future of Humanoid Robots

This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book

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

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