Quantifying Performance of Bipedal Standing with Multi-channel EMG

Spinal cord stimulation has enabled humans with motor complete spinal cord injury (SCI) to independently stand and recover some lost autonomic function. Quantifying the quality of bipedal standing under spinal stimulation is important for spinal rehabilitation therapies and for new strategies that seek to combine spinal stimulation and rehabilitative robots (such as exoskeletons) in real time feedback. To study the potential for automated electromyography (EMG) analysis in SCI, we evaluated the standing quality of paralyzed patients undergoing electrical spinal cord stimulation using both video and multi-channel surface EMG recordings during spinal stimulation therapy sessions. The quality of standing under different stimulation settings was quantified manually by experienced clinicians. By correlating features of the recorded EMG activity with the expert evaluations, we show that multi-channel EMG recording can provide accurate, fast, and robust estimation for the quality of bipedal standing in spinally stimulated SCI patients. Moreover, our analysis shows that the total number of EMG channels needed to effectively predict standing quality can be reduced while maintaining high estimation accuracy, which provides more flexibility for rehabilitation robotic systems to incorporate EMG recordings

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

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

Neurophysiological markers predicting recovery of standing in humans with chronic motor complete spinal cord injury

The appropriate selection of individual-specific spinal cord epidural stimulation (scES) parameters is crucial to re-enable independent standing with self-assistance for balance in individuals with chronic, motor complete spinal cord injury, which is a key achievement toward the recovery of functional mobility. To date, there are no available algorithms that contribute to the selection of scES parameters for facilitating standing in this population. Here, we introduce a novel framework for EMG data processing that implements spectral analysis by continuous wavelet transform and machine learning methods for characterizing epidural stimulation-promoted EMG activity resulting in independent standing. Analysis of standing data collected from eleven motor complete research participants revealed that independent standing was promoted by EMG activity characterized by lower median frequency, lower variability of median frequency, lower variability of activation pattern, lower variability of instantaneous maximum power, and higher total power. Additionally, the high classification accuracy of assisted and independent standing allowed the development of a prediction algorithm that can provide feedback on the effectiveness of muscle-specific activation for standing promoted by the tested scES parameters. This framework can support researchers and clinicians during the process of selection of epidural stimulation parameters for standing motor rehabilitation

IEEE Trans Neural Syst Rehabil Eng

After above-knee amputation, the missing biological knee and ankle are commonly replaced with a passive prosthesis, which cannot provide net-positive energy to assist the user. During activities such as sit-to-stand, above-knee amputees must compensate for this lack of power using their upper body, intact limb, and residual limb, resulting in slower, less symmetric, and higher effort movements. Previous studies have shown that powered prostheses can improve symmetry and speed by providing positive assistive power. However, we still lack a systematic investigation of the effect of powered prosthesis assistance. Without this knowledge, researchers and clinicians have no framework for tuning powered prostheses to optimally assist users. Here we show that varying the assistive knee torque significantly affected weight-bearing symmetry, effort, and speed during the stand-up movement in eight above-knee amputees. Specifically, we observed improvements in the index of asymmetry of the vertical ground reaction force at the point approximating maximum vertical center of mass acceleration, the integral of the intact vastus medialis activation measured using electromyography, and the stand-up duration compared to the passive prosthesis. We saw significant improvements in all three metrics when subjects used the powered prosthesis compared to the passive prosthesis. We saw improvements in all three metrics with increasing assistive torque levels commanded by the powered prosthesis. We also observed increased weight-bearing asymmetry at the end of movement, and increased kinematic asymmetry with increasing assistance from the powered prosthesis. These results show that powered prostheses can improve functional mobility, potentially increasing quality of life for millions of people living with above-knee amputations.R01 HD098154/HD/NICHD NIH HHSUnited States/T42 OH008414/OH/NIOSH CDC HHSUnited States

A divided-attention timed stepping accuracy task as a procedural learning intervention improves balance and functional performance in healthy older adults

For both healthy individuals and individuals at high risk of falling, certain environments, such as a dual-task situation, require more resources than others to prevent a loss of balance. Stepping assessment tasks can be used to predict falls, and it has been suggested that impaired voluntary stepping may be a contributing factor to falls (Lord & Fitzpatrick, 2001). In this research, a stepping task was used not as an assessment, but as a therapeutic intervention. The purpose of this research was to determine how training with a task that provides a procedural learning environment can affect balance and functional outcomes. The divided-attention timed stepping accuracy task required participants to step to and from 16 targets in a random order as quickly and accurately as possible. The physical stepping task was performed simultaneously with a cognitive information-processing task that involved attending to verbal cues to determine the next target while visually monitoring the environment to ensure accuracy requirements were met. Training sessions lasted approximately 30 min and were performed three times per week for 6 weeks. In experiment 1, a single-case experimental design, an individual with a 4 year history of an incomplete cervical spinal cord injury demonstrated improvements in balance, endurance, and functional tasks. In experiment 2, a pretest, posttest control group design, healthy older adults aged 65 years and above had significant improvements in the areas of balance, divided-attention performance, functional task performance, endurance and strength. In experiment 1, it was hypothesized that a procedural learning environment had been established and this was substantiated in experiment 2. The results from experiment 2 indicate that strength and endurance may have accounted for some of the improvements seen, but there is sufficient evidence that much of the improvement could be accounted for by procedural learning. In experiment 2, training resulted in simultaneous improvements in both the physical and cognitive aspects of the task. This research has immediate clinical applications and future studies may substantiate other potential clinical applications

Effect of Parkinson's Disease on the Step Response to a Backwards Pull

This study was designed to identify parameters of balance recovery that are sensitive to postural instability early in the progression of Parkinson's disease. The response to a backwards pull was analyzed in a group of healthy controls and a group of adults diagnosed with mild Parkinson's disease (H&Y 2). Video, motion, EMG, and force plate data were analyzed to determine differences in strategy, temporal, kinematic, kinetic, and center of pressure parameters for the first step in the response. In the PD group, subjects were less consistent in their choice of stepping limb over multiple trials, had a longer weight shift time, and showed different motion at the ankle joint prior to liftoff. In addition, the center of pressure was further posterior at landing in the PD group. These results suggest that further investigation focused on the movement preparation stage may be able to identify early markers of postural instability

Assist-as-needed EMG-based control strategy for wearable powered assistive devices

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)Robotic-based gait rehabilitation and assistance using Wearable Powered Assistive Devices (WPADs), such as orthosis and exoskeletons, has been growing in the rehabilitation area to recover and augment the motor function of neurologically impaired subjects. These WPADs should provide a personalized assistance, since physical condition and muscular fatigue modify from patient to patient. In this field, electromyography (EMG) signals have been used to control WPADs given their ability to infer the user’s motion intention. However, in cases of motor disability conditions, EMG signals present lower magnitudes when compared to EMG signals under healthy conditions. Thus, the use of WPADs managed by EMG signals may not have potential to provide the assistance that the patient requires. The main goal of this dissertation aims the development of an Assisted-As-Needed (AAN) EMG-based control strategy for a future insertion in a Smart Active Orthotic System (SmartOs). To achieve this goal, the following elements were developed and validated: (i) an EMG system to acquire muscle activity signals from the most relevant muscles during the motion of the ankle joint; (ii) machine learning-based tool for ankle joint torque estimation to serve as reference in the AAN EMG-based control strategy; and (iii) a tool for real EMG-based torque estimation using Tibialis Anterior (TA) and Gastrocnemius Lateralis (GASL) muscles and real ankle joint angles. EMG system showed satisfactory pattern correlations with a commercial system. The reference ankle joint torque was generated based on predicted reference ankle joint kinematics, walking speed information (from 1 to 4 km/h) and anthropometric data (body height from 1.51 m to 1.83 m and body mass from 52.0 kg to 83.7 kg), using five machine learning algorithms: Support Vector Regression (SVR), Random Forest (RF), Multilayer Perceptron (MLP), Long-Short Term Memory (LSTM) and Convolutional Neural Network (CNN). CNN provided the best performance, predicting the reference ankle joint torque with fitting curves ranging from 74.7 to 89.8 % and Normalized Root Mean Square Errors (NRMSEs) between 3.16 and 8.02 %. EMG-based torque estimation beneficiates of a higher number of muscles, since EMG data from TA and GASL are not enough to estimate the real ankle joint torque.A assistência e reabilitação robótica usando dispositivos de assistência ativos vestíveis (WPADs), como ortóteses e exosqueletos, tem crescido na área da reabilitação com o fim de recuperar e aumentar a função motora de sujeitos com alterações neurológicas. Estes dispositivos devem fornecer uma assistência personalizada, uma vez que a condição física e a fadiga muscular variam de paciente para paciente. Nesta área, sinais de eletromiografia (EMG) têm sido usados para controlar WPADs, dada a sua capacidade de inferir a intenção de movimento do utilizador. Contudo, em casos de deficiência motora, os sinais de EMG apresentam menor amplitude quando comparados com sinais de EMG em condições saudáveis e, portanto, o uso de WPADs geridos por sinais de EMG pode não oferecer a assistência que o paciente necessita. O principal objetivo desta dissertação visa o desenvolvimento de uma estratégia de controlo baseada em EMG capaz de fornecer assistência quando necessário, para futura integração num sistema ortótico ativo e inteligente (SmartOs). Para atingir este objetivo foram desenvolvidos e validados os seguintes elementos: (i) sistema de EMG para adquirir sinais de atividade muscular dos músculos mais relevantes no movimento da articulação do tornozelo; (ii) ferramenta de machine learning para estimação do binário da articulação do tornozelo para servir como referência na estratégia de controlo; e (iii) ferramenta de estimação do binário real do tornozelo considerando sinais de EMG dos músculos Tibialis Anterior (TA) e Gastrocnemius Lateralis (GASL) e ângulo real do tornozelo. O sistema de EMG apresentou correlações satisfatórias com um sistema comercial. O binário de referência para o tornozelo foi gerado com base no ângulo de referência da mesma articulação, velocidade de marcha (de 1 até 4 km/h) e dados antropométricos (alturas de 1.51 m até 1.83 e massas de 52.0 kg até 83.7 kg), usando cinco algoritmos de machine learning: Support Vector Machine, Random Forest, Multilayer Perceptron, Long-Short Term Memory e Convolutional Neural Network. CNN apresentou a melhor performance, prevendo binários de referência do tornozelo com um fit entre 74.7 e 89.8 % e Normalized Root Mean Square Errors (NRMSE) entre 3.16 e 8.02 %. A estimativa do torque com base em sinais de EMG requer a inclusão de um maior número de músculos, uma vez que sinais de EMG dos músculos TA e GASL não foram suficientes

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