Smart Technology for Telerehabilitation: A Smart Device Inertial-sensing Method for Gait Analysis

The aim of this work was to develop and validate an iPod Touch (4th generation) as a potential ambulatory monitoring system for clinical and non-clinical gait analysis. This thesis comprises four interrelated studies, the first overviews the current available literature on wearable accelerometry-based technology (AT) able to assess mobility-related functional activities in subjects with neurological conditions in home and community settings. The second study focuses on the detection of time-accurate and robust gait features from a single inertial measurement unit (IMU) on the lower back, establishing a reference framework in the process. The third study presents a simple step length algorithm for straight-line walking and the fourth and final study addresses the accuracy of an iPod’s inertial-sensing capabilities, more specifically, the validity of an inertial-sensing method (integrated in an iPod) to obtain time-accurate vertical lower trunk displacement measures. The systematic review revealed that present research primarily focuses on the development of accurate methods able to identify and distinguish different functional activities. While these are important aims, much of the conducted work remains in laboratory environments, with relatively little research moving from the “bench to the bedside.” This review only identified a few studies that explored AT’s potential outside of laboratory settings, indicating that clinical and real-world research significantly lags behind its engineering counterpart. In addition, AT methods are largely based on machine-learning algorithms that rely on a feature selection process. However, extracted features depend on the signal output being measured, which is seldom described. It is, therefore, difficult to determine the accuracy of AT methods without characterizing gait signals first. Furthermore, much variability exists among approaches (including the numbers of body-fixed sensors and sensor locations) to obtain useful data to analyze human movement. From an end-user’s perspective, reducing the amount of sensors to one instrument that is attached to a single location on the body would greatly simplify the design and use of the system. With this in mind, the accuracy of formerly identified or gait events from a single IMU attached to the lower trunk was explored. The study’s analysis of the trunk’s vertical and anterior-posterior acceleration pattern (and of their integrands) demonstrates, that a combination of both signals may provide more nuanced information regarding a person’s gait cycle, ultimately permitting more clinically relevant gait features to be extracted. Going one step further, a modified step length algorithm based on a pendulum model of the swing leg was proposed. By incorporating the trunk’s anterior-posterior displacement, more accurate predictions of mean step length can be made in healthy subjects at self-selected walking speeds. Experimental results indicate that the proposed algorithm estimates step length with errors less than 3% (mean error of 0.80 ± 2.01cm). The performance of this algorithm, however, still needs to be verified for those suffering from gait disturbances. Having established a referential framework for the extraction of temporal gait parameters as well as an algorithm for step length estimations from one instrument attached to the lower trunk, the fourth and final study explored the inertial-sensing capabilities of an iPod Touch. With the help of Dr. Ian Sheret and Oxford Brookes’ spin-off company ‘Wildknowledge’, a smart application for the iPod Touch was developed. The study results demonstrate that the proposed inertial-sensing method can reliably derive lower trunk vertical displacement (intraclass correlations ranging from .80 to .96) with similar agreement measurement levels to those gathered by a conventional inertial sensor (small systematic error of 2.2mm and a typical error of 3mm). By incorporating the aforementioned methods, an iPod Touch can potentially serve as a novel ambulatory monitor system capable of assessing gait in clinical and non-clinical environments

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

Wearable sensor-based rehabilitation exercise assessment for post-stroke rehabilitation

This thesis focuses on the use of wearable sensors (WS) and machine learning (ML) algorithms in post-stroke rehabilitation assessment. The conventional approach to rehabilitation involves subjective clinical assessments and frequent therapy sessions, which are time-consuming, costly, and often limited in availability. To address these limitations, WS have emerged as a portable and cost-effective solution, enabling patients to perform rehabilitation exercises at home. These sensors provide quantitative data on patients' movements, allowing for continuous monitoring and assessment. Additionally, ML algorithms offer the potential to enhance the accuracy and efficiency of rehabilitation assessment by processing the data collected from WS. The research presented in this thesis first aims to analyse recent developments in WS-based post-stroke rehabilitation assessment, identify limitations in the field, and propose state-of-the-art ML algorithms to improve assessment performance. The primary motivation is to provide a more comprehensive, personalised, and objective evaluation of motor function and mobility, leading to improved rehabilitation outcomes and quality of life for stroke survivors. Chapter 2 provides a comprehensive literature review that examines the current state-of-the-art in post-stroke rehabilitation assessment, specifically focusing on the utilisation of wearable sensors and machine learning techniques. The review encompasses a thorough examination of commonly employed sensors, targeted body limbs, outcome measures, study designs, and machine learning approaches. Furthermore, the review highlights the limitations encountered by researchers in the field, particularly pertaining to the accuracy of assessment algorithms and the availability of data. Subsequent chapters in this thesis address these identified limitations by proposing innovative solutions. Chapter 3 presents an approach aimed at enhancing the accuracy of assessment algorithms by adapting widely used computer vision algorithms to the time-series domain. This adaptation enables more precise and reliable analysis of the collected time-series data, thereby improving the assessment process. In Chapter 4, a novel methodology is introduced, which involves the transformation of time-series data into images and the subsequent utilisation of computer vision algorithms for assessment purposes. Furthermore, a linear interpolation methodology is implemented to adjust the size of the encoded images, allowing for an increase or decrease in dimensions. A comprehensive comparative analysis is then conducted to evaluate the impact of image size on the performance of the assessment algorithm. Finally, Chapter 5 introduces a novel algorithm that generates heterogeneous and realistic data, which serves to enhance the rehabilitation assessment process. By generating synthetic data that closely resembles real-world scenarios, this algorithm addresses the limitation of limited data availability, ultimately leading to more robust and accurate assessments. The contributions of each chapter provide insights into the current state-of-the-art in WS-based rehabilitation assessment, algorithm optimisation, data encoding techniques, and data augmentation strategies. The findings of this research aim to advance post-stroke rehabilitation outcomes and contribute to a more accurate and personalised assessment for stroke survivors