Feedback Control of an Exoskeleton for Paraplegics: Toward Robustly Stable Hands-free Dynamic Walking

This manuscript presents control of a high-DOF fully actuated lower-limb exoskeleton for paraplegic individuals. The key novelty is the ability for the user to walk without the use of crutches or other external means of stabilization. We harness the power of modern optimization techniques and supervised machine learning to develop a smooth feedback control policy that provides robust velocity regulation and perturbation rejection. Preliminary evaluation of the stability and robustness of the proposed approach is demonstrated through the Gazebo simulation environment. In addition, preliminary experimental results with (complete) paraplegic individuals are included for the previous version of the controller.Comment: Submitted to IEEE Control System Magazine. This version addresses reviewers' concerns about the robustness of the algorithm and the motivation for using such exoskeleton

Feasibility and efficacy of incorporating an exoskeleton in gait training during subacute stroke rehabilitation

Introduction: Hemiparesis is the most common acute manifestation of stroke and often has a strong negative impact on walking ability leaving one third of patients dependent in walking activities outside one’s home. Improved methods for training of gait during stroke rehabilitation could tackle the challenge of achieving independent walking and promote better outcomes. Several studies have explored the value of introducing electromechanical gait machines in stroke rehabilitation to enhance gait training. One example is the exoskeleton Hybrid Assistive Limb (HAL). The HAL system has been found feasible to use during rehabilitation in the chronic stage after stroke, however knowledge of the feasibility in the subacute stage after stroke and its efficacy compared to evidence-based conventional gait training is still limited. Aim: The overall aim of this thesis was to evaluate the safety and feasibility of HAL for gait training in the subacute stage after stroke and the effect of HAL training on functioning, disability and health compared to conventional gait training, as part of an inpatient rehabilitation program in patients with severe limitations in walking in the subacute stage after stroke. Methods: This thesis contains two studies where one is a safety and feasibility study (Study I) and one is a prospective, randomized, open labeled, blinded evaluation study (Study II). In Study I, eight patients performed HAL training 5 days/week. The number of training sessions were adjusted individually and varied from 6 to 31 (median 16). Safety and feasibility aspects of the training were evaluated as well as clinical outcomes on functioning and disability (e.g. independence in walking, walking speed, balance, movement functions and activities of daily living), assessed before and after the intervention period. In Study II, 32 patients were randomized to either conventional training only or HAL training in addition to the conventional training, 4 days per week for 4 weeks. Within and between- group differences in independence in walking, walking speed/endurance, balance, movement functions and activities of daily living were investigated before and after the intervention period, as well as 6 months post stroke. In addition, gait pattern functions were evaluated after the intervention in a three-dimensional gait laboratory. At 6 months post stroke self- perceived aspects on functioning disability and health were assessed and subsequently correlated to the clinical assessments. Results: In Study I HAL was found to be safe and feasible for gait training after stroke in patients with hemiparesis, unable to walk independently, undergoing an inpatient rehabilitation program. All patients improved in walking independence and speed, movement function, and activities of daily living during the intervention period. In addition, it was found that patients walked long distances during the HAL sessions, suggesting that HAL training may be an effective method to enhance gait training during rehabilitation of patients in the subacute stage after stroke. In Study II substantial but equal improvements in the clinically evaluated outcomes in the two intervention groups were found. At six months post stroke, two thirds of patients were independent in walking, and a younger age but not intervention group served as the best predictor. Gait patterns were similarly impaired in both groups and in line with previous reports on gait patterns post stroke. Further, self-perceived ratings on functioning, disability and health were explained by the ability to perform self-care activities and not by intervention group. Conclusion: To incorporate gait training with HAL is safe and feasible during inpatient rehabilitation in the subacute stage after stroke and may be a way to increase the dose (i.e. number of steps) in gait training in the subacute stage after stroke. Among these included younger patients with hemiparesis and severe limitations in walking in the subacute stage after stroke, substantial improvements in body function and activity as well as equally impaired gait patterns were observed both after incorporated HAL training and after conventional gait training only, but without between-group differences. In future studies, potential beneficial effects on cardiovascular, respiratory, and metabolic functions should be addressed. Further, as the stroke population is heterogeneous, potential subgroups of patients who may benefit the most from electromechanically-assisted gait training should be identified

A review on locomotion mode recognition and prediction when using active orthoses and exoskeletons

Understanding how to seamlessly adapt the assistance of lower-limb wearable assistive devices (active orthosis (AOs) and exoskeletons) to human locomotion modes (LMs) is challenging. Several algorithms and sensors have been explored to recognize and predict the users’ LMs. Nevertheless, it is not yet clear which are the most used and effective sensor and classifier configurations in AOs/exoskeletons and how these devices’ control is adapted according to the decoded LMs. To explore these aspects, we performed a systematic review by electronic search in Scopus and Web of Science databases, including published studies from 1 January 2010 to 31 August 2022. Sixteen studies were included and scored with 84.7 ± 8.7% quality. Decoding focused on level-ground walking along with ascent/descent stairs tasks performed by healthy subjects. Time-domain raw data from inertial measurement unit sensors were the most used data. Different classifiers were employed considering the LMs to decode (accuracy above 90% for all tasks). Five studies have adapted the assistance of AOs/exoskeletons attending to the decoded LM, in which only one study predicted the new LM before its occurrence. Future research is encouraged to develop decoding tools considering data from people with lower-limb impairments walking at self-selected speeds while performing daily LMs with AOs/exoskeletons.This work was funded in part by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under grant 2020.05711.BD, under the Stimulus of Scientific Employment with the grant 2020.03393.CEECIND, and in part by the FEDER Funds through the COMPETE 2020— Programa Operacional Competitividade e Internacionalização (POCI) and P2020 with the Reference Project SmartOs Grant POCI-01-0247-FEDER-039868, and by FCT national funds, under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020

A novel approach to user controlled ambulation of lower extremity exoskeletons using admittance control paradigm

The robotic lower extremity exoskeletons address the ambulatory problems confronting individuals with paraplegia. Paraplegia due to spinal cord injury (SCI) can cause motor deficit to the lower extremities leading to inability to walk. Though wheelchairs provide mobility to the user, they do not provide support to all activities of everyday living to individuals with paraplegia. Current research is addressing the issue of ambulation through the use of wearable exoskeletons that are pre-programmed. There are currently four exoskeletons in the U.S. market: Ekso, Rewalk, REX and Indego. All of the currently available exoskeletons have 2 active Degrees of Freedom (DOF) except for REX which has 5 active DOF. All of them have pre-programmed gait giving the user the ability to initiate a gait but not the ability to control the stride amplitude (height), stride frequency or stride length, and hence restricting users’ ability to navigate across different surfaces and obstacles that are commonly encountered in the community. Most current exoskeletons do not have motors for abduction or adduction to provide users with the option for movement in coronal plane, hence restricting user’s ability to effectively use the exoskeletons. These limitations of currently available pre-programmed exoskeleton models are sought to be overcome by an intuitive, real time user-controlled control mechanism employing admittance control by using hand-trajectory as a surrogate for foot trajectory. Preliminary study included subjects controlling the trajectory of the foot in a virtual environment using their contralateral hand. The study proved that hands could produce trajectories similar to human foot trajectories when provided with haptic and visual feedback. A 10 DOF 1/2 scale biped robot was built to test the control paradigm. The robot has 5 DOF on each leg with 2 DOF at the hip to provide flexion/extension and abduction/adduction, 1 DOF at the knee to provide flexion and 2 DOF at the ankle to provide flexion/extension and inversion/eversion. The control mechanism translates the trajectory of each hand into the trajectory of the ipsilateral foot in real time, thus providing the user with the ability to control each leg in both sagittal and coronal planes using the admittance control paradigm. The efficiency of the control mechanism was evaluated in a study using healthy subjects controlling the robot on a treadmill. A trekking pole was attached to each foot of the biped. The subjects controlled the trajectory of the foot of the biped by applying small forces in the direction of the required movement to the trekking pole through a force sensor. The algorithm converted the forces to Cartesian position of the foot in real time using admittance control; the Cartesian position was converted to joint angles of the hip and knee using inverse kinematics. The kinematics, synchrony and smoothness of the trajectory produced by the biped robot was evaluated at different speeds, with and without obstacles, and compared with typical walking by human subjects on the treadmill. Further, the cognitive load required to control the biped on the treadmill was evaluated and the effect of speed and obstacles with cognitive load on the kinematics, synchrony and smoothness was analyzed

A CONTINOUS ROTARY ACTUATION MECHANISM FOR A POWERED HIP EXOSKELETON

This thesis presents a new mechanical design for an exoskeleton actuator to power the sagittal plane motion in the human hip. The device uses a DC motor to drive a Scotch yoke mechanism and series elasticity to take advantage of the cyclic nature of human gait and to reduce the maximum power and control requirements of the exoskeleton. The Scotch yoke actuator creates a position-dependent transmission that varies between 4:1 and infinity, with the peak transmission ratio aligned to the peak torque periods of the human gait cycle. Simulation results show that both the peak and average motor torque can be reduced using this mechanism, potentially allowing a less powerful motor to be used. Furthermore, the motor never needs to reverse direction even when the hip joint does. Preliminary testing shows the exoskeleton can provide an assistive torque and is capable of accurate position tracking at speeds covering the range of human walking. This thesis provides a detailed analysis of how the dynamic nature of human walking can be leveraged, how the hip actuator was designed, and shows how the exoskeleton performed during preliminary human trials

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

A unilateral robotic knee exoskeleton to assess the role of natural gait assistance in hemiparetic patients.

Background: Hemiparetic gait is characterized by strong asymmetries that can severely affect the quality of life of stroke survivors. This type of asymmetry is due to motor deficits in the paretic leg and the resulting compensations in the nonparetic limb. In this study, we aimed to evaluate the effect of actively promoting gait symmetry in hemiparetic patients by assessing the behavior of both paretic and nonparetic lower limbs. This paper introduces the design and validation of the REFLEX prototype, a unilateral active knee–ankle–foot orthosis designed and developed to naturally assist the paretic limbs of hemiparetic patients during gait. Methods: REFLEX uses an adaptive frequency oscillator to estimate the continuous gait phase of the nonparetic limb. Based on this estimation, the device synchronically assists the paretic leg following two different control strategies: (1) replicating the movement of the nonparetic leg or (2) inducing a healthy gait pattern for the paretic leg. Technical validation of the system was implemented on three healthy subjects, while the effect of the generated assistance was assessed in three stroke patients. The effects of this assistance were evaluated in terms of interlimb symmetry with respect to spatiotemporal gait parameters such as step length or time, as well as the similarity between the joint’s motion in both legs. Results: Preliminary results proved the feasibility of the REFLEX prototype to assist gait by reinforcing symmetry. They also pointed out that the assistance of the paretic leg resulted in a decrease in the compensatory strategies developed by the nonparetic limb to achieve a functional gait. Notably, better results were attained when the assistance was provided according to a standard healthy pattern, which initially might suppose a lower symmetry but enabled a healthier evolution of the motion of the nonparetic limb. Conclusions: This work presents the preliminary validation of the REFLEX prototype, a unilateral knee exoskeleton for gait assistance in hemiparetic patients. The experimental results indicate that assisting the paretic leg of a hemiparetic patient based on the movement of their nonparetic leg is a valuable strategy for reducing the compensatory mechanisms developed by the nonparetic limb.post-print6406 K