Error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networks

BACKGROUND: The design of an optimal neuroprostheses controller and its clinical use presents several challenges. First, the physiological system is characterized by highly inter-subjects varying properties and also by non stationary behaviour with time, due to conditioning level and fatigue. Secondly, the easiness to use in routine clinical practice requires experienced operators. Therefore, feedback controllers, avoiding long setting procedures, are required. METHODS: The error mapping controller (EMC) here proposed uses artificial neural networks (ANNs) both for the design of an inverse model and of a feedback controller. A neuromuscular model is used to validate the performance of the controllers in simulations. The EMC performance is compared to a Proportional Integral Derivative (PID) included in an anti wind-up scheme (called PIDAW) and to a controller with an ANN as inverse model and a PID in the feedback loop (NEUROPID). In addition tests on the EMC robustness in response to variations of the Plant parameters and to mechanical disturbances are carried out. RESULTS: The EMC shows improvements with respect to the other controllers in tracking accuracy, capability to prolong exercise managing fatigue, robustness to parameter variations and resistance to mechanical disturbances. CONCLUSION: Different from the other controllers, the EMC is capable of balancing between tracking accuracy and mapping of fatigue during the exercise. In this way, it avoids overstressing muscles and allows a considerable prolongation of the movement. The collection of the training sets does not require any particular experimental setting and can be introduced in routine clinical practice

Development of a Novel Powered Orthosis System for Hemiplegia Patients

芝浦工業大学201

A Human Motor Control-Inspired Control System for a Walking Hybrid Neuroprosthesis

The purpose of this research is to develop a human motor control-inspired control system for a hybrid neuroprosthesis that combines functional electrical stimulation (FES) with electric motors. This device is intended to reproduce gait for persons with spinal cord injuries (SCI). Each year approximately 17,000 people suffer from an SCI in the U.S. alone, of which about 20% of them are diagnosed with complete paraplegia. Currently, there is a lot of interest in gait restoration for subjects with paraplegia but the existing technologies use either solely FES or electric motors. These two sources of actuation both have their own limitation when used alone. Recently, there have been efforts to provide a combination of the two means of actuation, FES and motors, into gait restoration devices called hybrid neuroprostheses. In this dissertation the derivation and experimental demonstration of control systems for the hybrid neuroprosthesis are presented. Particularly, the dissertation addresses technical challenges associated with the real-time control of a FES such as nonlinear muscle dynamics, actuator dynamics, muscle fatigue, and electromechanical delays (EMD). In addition, when FES is combined with electric motors in hybrid neuroprostheses, an actuator redundancy problem is introduced. To address the actuator redundancy issue, a synergy-based control framework is derived. This synergy-based framework is inspired from the concept of muscle synergies in human motor control theory. Dynamic postural synergies are developed and used in the feedforward path of the control system for the walking hybrid neuroprosthesis. To address muscle fatigue, the stimulation levels are gradually increased based on a model-based fatigue estimate. A dynamic surface control technique, modified with a delay compensation term, is used to address the actuator dynamics and EMD in the control derivation. A Lyapunov-based stability approach is used to derive the controllers and guarantee their stability. The outcome of this research is the development of a human motor control-inspired control framework for the hybrid neuroprosthesis where both FES and electric motors can be simultaneously coordinated to reproduce gait. Multiple experiments were conducted on both able-bodied subjects and persons with SCI to validate the derived controllers

Development of an Active Elbow Motion Simulator and Coordinate Systems to Evaluate Kinematics in Multiple Positions

Elbow disorders are common as a consequence of both traumatic and degenerative conditions. Relative to disorders of the lower limb, there is comparatively little evidence to direct the treatment of many elbow disorders. Biomechanical studies are required to develop and validate the optimal treatment of elbow disorders prior to their application in patients. Clinically relevant simulation of elbow motion in the laboratory can be a powerful tool to advance our knowledge of elbow disorders. This work was undertaken with the rationale that simulation and quantification of elbow motion could be improved significantly. This treatise includes the development and evaluation of an in-vitro elbow motion simulator which, with the humerus horizontally positioned, is the first to achieve active flexion and extension in a vertical plane. Additionally, it is capable of operating in the vertical, varus and valgus positions, and while maintaining full forearm pronation or supination. The simulator controller employs a Cascade PID configuration with feedforward transfer functions, which achieves unified control of flexion angle and muscle tension for multiple muscles. Feedback of the elbow joint angle and muscle tension is utilized to achieve closed-loop control. A performance evaluation in a full series of specimens clearly demonstrated that the actual joint angle is not more than 5 degrees removed from the desired setpoint during flexion or extension in any position. Also, a new method for creating upper extremity bone segment coordinate systems which are derived from elbow flexion and forearm rotation was developed and tested. This produced joint kinematics with significantly less inter-subject variability than traditional anatomy-derived coordinate systems. This minimally-invasive method also provides increased statistical power for laboratory based studies and may prove useful for clinical applications. The new simulation techniques developed herein were applied to an in-vitro investigation of olecranon fracture repair with clinical significance. This study revealed valuable insights into a common repair procedure. This was made possible by the previously unattainable measurements that these new techniques now provide. These developments will assist surgeons and other investigators in the design and evaluation of treatments for elbow disorders, and contribute to the betterment of patient care

Development and Implementation of a Computational Surgical Planning Model for Pre-Operative Planning and Post-Operative Assessment and Analysis of Total Hip Arthroplasty

Total hip arthroplasty (THA) is most often used to treat osteoarthritis of the hip joint. Due to lack of a better alternative, newer designs are evaluated experimentally using mechanical simulators and cadavers. These evaluation techniques, though necessary, are costly and time-consuming, limiting testing on a broader population. Due to the advancement in technology, the current focus has been to develop patient-specific solutions. The hip joint can be approximated as encompassing a bone socket geometry, and therefore the shapes of the implant are well constrained. The variability of performance after the surgery is mostly driven by surgical procedures. It is believed that placing the acetabular component within the “safe zone” will commonly lead to successful surgical outcomes [1]. Unfortunately, recent research has revealed problems with the safe zone concept, and there is a need for a better tool which can aid surgeons in planning for surgery.With the advancement of computational power, more recent focus has been applied to the development of simulation tools that can predict implant performances. In this endeavor, a virtual hip simulator is being developed at the University of Tennessee Knoxville to provide designers and surgeons alike instant feedback about the performance of the hip implants. The mathematical framework behind this tool has been developed.In this dissertation, the primary focus is to further expand the capabilities of the existing hip model and develop the front-end that can replicate a total hip arthroplasty surgery procedure pre-operatively, intra-operatively, and post-operatively. This new computer-assisted orthopaedic surgical tool will allow surgeons to simulate surgery, then predict, compare, and optimize post-operative THA outcomes based on component placement, sizing choices, reaming and cutting locations, and surgical methods. This more advanced mathematical model can also reveal more information pre-operatively, allowing a surgeon to gain ample information before surgery, especially with difficult and revision cases. Moreover, this tool could also help during the implant development design process as designers can instantly simulate the performance of their new designs, under various surgical, simulated in vivo conditions

Design and Evaluation of AIRGAIT Exoskeleton\u27s Leg Orthosis: Development of a Control Scheme and Strategy for a Noble Control of Antagonistic Mono- and Bi-Articular Actuators

芝浦工業大学201

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

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