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

Recent Advances in Motion Analysis

The advances in the technology and methodology for human movement capture and analysis over the last decade have been remarkable. Besides acknowledged approaches for kinematic, dynamic, and electromyographic (EMG) analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Furthermore, computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, the synergy of classic instrumentation and novel smart devices and techniques has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, human activity recognition, and characterization and interpretation of motion metrics and behaviors from sensor data still representing a challenging problem not only in laboratories but also at home and in the community. This book addresses open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application

Latest research trends in gait analysis using wearable sensors and machine learning: a systematic review

Gait is the locomotion attained through the movement of limbs and gait analysis examines the patterns (normal/abnormal) depending on the gait cycle. It contributes to the development of various applications in the medical, security, sports, and fitness domains to improve the overall outcome. Among many available technologies, two emerging technologies that play a central role in modern day gait analysis are: A) wearable sensors which provide a convenient, efficient, and inexpensive way to collect data and B) Machine Learning Methods (MLMs) which enable high accuracy gait feature extraction for analysis. Given their prominent roles, this paper presents a review of the latest trends in gait analysis using wearable sensors and Machine Learning (ML). It explores the recent papers along with the publication details and key parameters such as sampling rates, MLMs, wearable sensors, number of sensors, and their locations. Furthermore, the paper provides recommendations for selecting a MLM, wearable sensor and its location for a specific application. Finally, it suggests some future directions for gait analysis and its applications

On the quantification and objective classification of instability in the healthy, osteoarthritic and prosthetic knee

Knee instability is a common complaint in osteoarthritis (OA), and a common reason for revision following total knee arthroplasty (TKA). Despite this, assessment of instability is hampered by the lack of a validated method of objective classification or quantification, with most research relying upon patient reports of frequency of symptoms. The aim of this thesis is to define a theoretical framework for instability in the knee, and to develop a protocol for the classification and quantification of instability in the native and prosthetic knee. Instability of the knee in this thesis is understood as the failure of the joint to return to a zero-state following perturbation using all the available active and passive mechanisms available to it, resulting in system collapse. Symptomatic instability is the awareness of reaching the boundary between the stable and unstable state. The prevalence of subjective instability in the end stage OA knee was measured from a publicly available database of pre-operative knee scores from TKA patients, while the prevalence of instability as a cause of revision was assessed from case note review of TKA revision patients from a tertiary referral orthopaedic unit. A single channel, tibia mounted accelerometer was selected for assessment of frontal plane knee movement during normal walking and a protocol developed its use. This was assessed for its repeatability and compared with standard gait analysis in healthy volunteers, and subjectively stable and unstable post-operative TKA patients. Found to be repeatable with differentiation of output between subjectively stable and unstable TKA, the protocol was adapted and used to compare subjectively stable and unstable OA knees prior to TKA. Using patient subjective assessment as classifier, wavelet transforms, Principal Component Analysis and linear regression was used to produce a classification model from the accelerometer data. The single accelerometer was found to produce classification with an accuracy of 84.6%, sensitivity of 93.3% and specificity of 72.7%, with area under the curve (AUC) of 0.797. This classification model for instability produces the basis from which the protocol can be adapted and developed to improve performance and ultimate quantify instability in the knee for use in clinical and research settings.Knee instability is a common complaint in osteoarthritis (OA), and a common reason for revision following total knee arthroplasty (TKA). Despite this, assessment of instability is hampered by the lack of a validated method of objective classification or quantification, with most research relying upon patient reports of frequency of symptoms. The aim of this thesis is to define a theoretical framework for instability in the knee, and to develop a protocol for the classification and quantification of instability in the native and prosthetic knee. Instability of the knee in this thesis is understood as the failure of the joint to return to a zero-state following perturbation using all the available active and passive mechanisms available to it, resulting in system collapse. Symptomatic instability is the awareness of reaching the boundary between the stable and unstable state. The prevalence of subjective instability in the end stage OA knee was measured from a publicly available database of pre-operative knee scores from TKA patients, while the prevalence of instability as a cause of revision was assessed from case note review of TKA revision patients from a tertiary referral orthopaedic unit. A single channel, tibia mounted accelerometer was selected for assessment of frontal plane knee movement during normal walking and a protocol developed its use. This was assessed for its repeatability and compared with standard gait analysis in healthy volunteers, and subjectively stable and unstable post-operative TKA patients. Found to be repeatable with differentiation of output between subjectively stable and unstable TKA, the protocol was adapted and used to compare subjectively stable and unstable OA knees prior to TKA. Using patient subjective assessment as classifier, wavelet transforms, Principal Component Analysis and linear regression was used to produce a classification model from the accelerometer data. The single accelerometer was found to produce classification with an accuracy of 84.6%, sensitivity of 93.3% and specificity of 72.7%, with area under the curve (AUC) of 0.797. This classification model for instability produces the basis from which the protocol can be adapted and developed to improve performance and ultimate quantify instability in the knee for use in clinical and research settings

Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice

This Special Issue shows a range of potential opportunities for the application of wearable movement sensors in motor rehabilitation. However, the papers surely do not cover the whole field of physical behavior monitoring in motor rehabilitation. Most studies in this Special Issue focused on the technical validation of wearable sensors and the development of algorithms. Clinical validation studies, studies applying wearable sensors for the monitoring of physical behavior in daily life conditions, and papers about the implementation of wearable sensors in motor rehabilitation are under-represented in this Special Issue. Studies investigating the usability and feasibility of wearable movement sensors in clinical populations were lacking. We encourage researchers to investigate the usability, acceptance, feasibility, reliability, and clinical validity of wearable sensors in clinical populations to facilitate the application of wearable movement sensors in motor rehabilitation

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

Volitional Control of Lower-limb Prosthesis with Vision-assisted Environmental Awareness

Early and reliable prediction of user’s intention to change locomotion mode or speed is critical for a smooth and natural lower limb prosthesis. Meanwhile, incorporation of explicit environmental feedback can facilitate context aware intelligent prosthesis which allows seamless operation in a variety of gait demands. This dissertation introduces environmental awareness through computer vision and enables early and accurate prediction of intention to start, stop or change speeds while walking. Electromyography (EMG), Electroencephalography (EEG), Inertial Measurement Unit (IMU), and Ground Reaction Force (GRF) sensors were used to predict intention to start, stop or increase walking speed. Furthermore, it was investigated whether external emotional music stimuli could enhance the predictive capability of intention prediction methodologies. Application of advanced machine learning and signal processing techniques on pre-movement EEG resulted in an intention prediction system with low latency, high sensitivity and low false positive detection. Affective analysis of EEG suggested that happy music stimuli significantly (

Gait analysis in neurological populations: Progression in the use of wearables

Gait assessment is an essential tool for clinical applications not only to diagnose different neurological conditions but also to monitor disease progression as it contributes to the understanding of underlying deficits. There are established methods and models for data collection and interpretation of gait assessment within different pathologies. This narrative review aims to depict the evolution of gait assessment from observation and rating scales to wearable sensors and laboratory technologies, and provide possible future directions. In this context, we first present an extensive review of current clinical outcomes and gait models. Then, we demonstrate commercially available wearable technologies with their technical capabilities along with their use in gait assessment studies for various neurological conditions. In the next sections, a descriptive knowledge for existing inertial based algorithms and a sign based guide that shows the outcomes of previous neurological gait assessment studies are presented. Finally, we state a discussion for the use of wearables in gait assessment and speculate the possible research directions by revealing the limitations and knowledge gaps in the literature

Biomedical Sensing and Imaging

This book mainly deals with recent advances in biomedical sensing and imaging. More recently, wearable/smart biosensors and devices, which facilitate diagnostics in a non-clinical setting, have become a hot topic. Combined with machine learning and artificial intelligence, they could revolutionize the biomedical diagnostic field. The aim of this book is to provide a research forum in biomedical sensing and imaging and extend the scientific frontier of this very important and significant biomedical endeavor