Wearable Torso Exoskeletons for Human Load Carriage and Correction of Spinal Deformities

The human spine is an integral part of the human body. Its functions include mobilizing the torso, controlling postural stability, and transferring loads from upper body to lower body, all of which are essential for the activities of daily living. However, the many complex tasks of the spine leave it vulnerable to damage from a variety of sources. Prolonged walking with a heavy backpack can cause spinal injuries. Spinal diseases, such as scoliosis, can make the spine abnormally deform. Neurological disorders, such as cerebral palsy, can lead to a loss of torso control. External torso support has been used in these cases to mitigate the risk of spinal injuries, to halt the progression of spinal deformities, and to support the torso. However, current torso support designs are limited by rigid, passive, and non-sensorized structures. These limitations were the motivations for this work in developing the science for design of torso exoskeletons that can improve the effectiveness of current external torso support solutions. Central features to the design of these exoskeletons were the abilities to sense and actively control the motion of or the forces applied to the torso. Two applications of external torso support are the main focus in this study, backpack load carriage and correction of spine deformities. The goal was to develop torso exoskeletons for these two applications, evaluate their effectiveness, and exploit novel assistive and/or treatment paradigms. With regard to backpack load carriage, current torso support solutions are limited and do not provide any means to measure and/or adjust the load distribution between the shoulders and the pelvis, or to reduce dynamic loads induced by walking. Because of these limitations, determining the effects of modulating these loads between the shoulders and the pelvis has not been possible. Hence, the first scientific question that this work aims to address is What are the biomechanical and physiological effects of distributing the load and reducing the dynamic load of a backpack on human body during backpack load carriage? Concerning the correction of spinal deformities, the most common treatment is the use of a spine brace. This method has been shown to effectively slow down the progression of spinal deformity. However , a limitation in the effectiveness of this treatment is the lack of knowledge of the stiffness characteristics of the human torso. Previously, there has been no means to measure the stiffness of human torso. An improved understanding of this subject would directly affect treatment outcomes by better informing the appropriate external forces (or displacements) to apply in order to achieve the desired correction of the spine. Hence, the second scientific question that this work aims to address is How can we characterize three dimensional stiffness of the human torso for quantifiable assessment and targeted treatment of spinal deformities? In this work, a torso exoskeleton called the Wearable upper Body Suit (WEBS) was developed to address the first question. The WEBS distributes the backpack load between the shoulders and the pelvis, senses the vertical motion of the pelvis, and provides gait synchronized compensatory forces to reduce dynamic loads of a backpack during walking. It was hypothesized that during typical backpack load carriage, load distribution and dynamic load compensation reduce gait and postural adaptations, the user’s overall effort and metabolic cost. This hypothesis was supported by biomechanical and physiological measurements taken from twelve healthy male subjects while they walked on a treadmill with a 25 percent body weight backpack. In terms of load distribution and dynamic load compensation, the results showed reductions in gait and postural adaptations, muscle activity, vertical and braking ground reaction forces, and metabolic cost. Based on these results, it was concluded that the wearable upper body suit can potentially reduce the risk of musculoskeletal injuries and muscle fatigue associated with carrying heavy backpack loads, as well as reducing the metabolic cost of loaded walking. To address the second question, the Robotic Spine Exoskeleton (ROSE) was developed. The ROSE consists of two parallel robot platforms connected in series that can adjust to fit snugly at different levels of the human torso and dynamically modulate either the posture of the torso or the forces exerted on the torso. An experimental evaluation of the ROSE was performed with ten healthy male subjects that validated its efficacy in controlling three dimensional corrective forces exerted on the torso while providing flexibility for a wide range of torso motions. The feasibility of characterizing the three dimensional stiffness of the human torso was also validated using the ROSE. Based on these results, it was concluded that the ROSE may alleviate some of the limitations in current brace technology and treatment methods for spine deformities, and offer a means to explore new treatment approaches to potentially improve the therapeutic outcomes of the brace treatment

Homotopy-based training of NeuralODEs for accurate dynamics discovery

Conceptually, Neural Ordinary Differential Equations (NeuralODEs) pose an attractive way to extract dynamical laws from time series data, as they are natural extensions of the traditional differential equation-based modeling paradigm of the physical sciences. In practice, NeuralODEs display long training times and suboptimal results, especially for longer duration data where they may fail to fit the data altogether. While methods have been proposed to stabilize NeuralODE training, many of these involve placing a strong constraint on the functional form the trained NeuralODE can take that the actual underlying governing equation does not guarantee satisfaction. In this work, we present a novel NeuralODE training algorithm that leverages tools from the chaos and mathematical optimization communities - synchronization and homotopy optimization - for a breakthrough in tackling the NeuralODE training obstacle. We demonstrate architectural changes are unnecessary for effective NeuralODE training. Compared to the conventional training methods, our algorithm achieves drastically lower loss values without any changes to the model architectures. Experiments on both simulated and real systems with complex temporal behaviors demonstrate NeuralODEs trained with our algorithm are able to accurately capture true long term behaviors and correctly extrapolate into the future.Comment: 12 pages, 6 figures, submitted to ICLR202

Development of a Chaff Dispense Program for Target Tracking Radar Deception

This study aims to develop an appropriate chaff dispensing program to deceive the target tracking radar (TTR) effectively. Chaff is a countermeasure commonly used by fighter aircraft to deceive TTR. However, there has been a lack of methodology for calculating chaff dispense programs that take into account the specific characteristics of the fighter, chaff, and TTR. This study proposes a methodology that considers these variables to calculate chaff dispense programs and addresses this gap. The proposed method is demonstrated through TESS engagement, which shows its effectiveness in various engagement situations

Development of a Chaff Dispense Program for Target Tracking Radar Deception

This study aims to develop an appropriate chaff dispensing program to deceive the target tracking radar (TTR) effectively. Chaff is a countermeasure commonly used by fighter aircraft to deceive TTR. However, there has been a lack of methodology for calculating chaff dispense programs that take into account the specific characteristics of the fighter, chaff, and TTR. This study proposes a methodology that considers these variables to calculate chaff dispense programs and addresses this gap. The proposed method is demonstrated through TESS engagement, which shows its effectiveness in various engagement situations

Challenges and Trends of Machine Learning in the Myoelectric Control System for Upper Limb Exoskeletons and Exosuits

Myoelectric control systems as the emerging control strategies for upper limb wearable robots have shown their efficacy and applicability to effectively provide motion assistance and/or restore motor functions in people with impairment or disabilities, as well as augment physical performance in able-bodied individuals. In myoelectric control, electromyographic (EMG) signals from muscles are utilized, improving adaptability and human-robot interactions during various motion tasks. Machine learning has been widely applied in myoelectric control systems due to its advantages in detecting and classifying various human motions and motion intentions. This chapter illustrates the challenges and trends in recent machine learning algorithms implemented on myoelectric control systems designed for upper limb wearable robots, and highlights the key focus areas for future research directions. Different modalities of recent machine learning-based myoelectric control systems are described in detail, and their advantages and disadvantages are summarized. Furthermore, key design aspects and the type of experiments conducted to validate the efficacy of the proposed myoelectric controllers are explained. Finally, the challenges and limitations of current myoelectric control systems using machine learning algorithms are analyzed, from which future research directions are suggested