A 3D image-based measurement approach for analysing dynamic foot posture and mobility

The original contribution achieved from this research was the development of a low-cost 3D high-accuracy photogrammetric technique for measuring dynamic changes in foot anthropometry during gait. In clinical settings, the approach of determining foot mobility is achieved through measuring changes in bone landmarks between the static unloaded foot and the static loaded foot. From previous reliability assessment tests, it was found that static clinical foot mobility assessments based on the dorsum bone as a point of landmark reference provides high levels of measurement reliability. However, the relationships between these static dorsum measurement techniques have not been assessed against dynamic dorsum measurements collected during foot mobility. In this thesis, two assessment techniques based on the dorsum as a point of reference; namely the Foot Mobility Magnitude (FMM) and Arch Height Index (AHI) were compared statically and dynamically. The purpose for this was to validate these static measurements against the actual foot mobility during dynamic activities. An imaging platform was developed which consisted of 12 video cameras synchronised with force plate data to continuously capture the foot during gait while simultaneously obtaining ground reaction force information. The developed system achieved measurement accuracies within 0.3 mm with high levels of measurement precisions and insignificant random and systematic errors. From the research study, it was found that the correlation between the static and dynamic FMM measurements was insignificant, whereas significant correlations were found between the static and dynamic AHI measurements. Agreements between the static and dynamic AHI measurements were higher when the dorsum measurements were normalised to the truncated foot length (AHI 1) than normalising the dorsum measurements to the total foot length (AHI 2). Another major finding from the research was the higher measurement correlations achieved when the dynamic FMM and AHI were assessed between heel-strike and mid-stance compared to between heel-strike and active propulsion. This indicates that measuring the static FMM and AHI between 10% WB and 50% WB instead of between 10% WB and 90% WB might lend better insight in determining the behaviour of the foot dynamically. The Foot Posture Index (FPI) was used to classify foot postures and the relationship between the FPI scores and the dynamic FMM and AHI were assessed. It was found that the FPI was significantly correlated to the AHI measures but no correlation was found between the FPI and the FMM. The highest correlation was found for AHI 1 at active propulsion where the FPI predicted 48.9% of the variation of the AHI 1. The only FPI classification criteria to have a significant influence on the AHI at heel-strike, mid-stance and active-propulsion was the congruence of the MLA with the highest prediction of 66.7% of the variation in the AHI 1 at heelstrike

Psychometric properties of Brief-Balance Evaluation Systems Test (Brief-BESTest) in evaluating balance performance in individuals with chronic stroke

2016-2017 > Academic research: refereed > Publication in refereed journal201804_a bcmaVersion of RecordSelf-fundedPublishe

Performance of the Intrac Wireless Activity Tracking System for the Afari Assistive Device

Afari is a mobility device that was designed to be more recreational, aesthetic, and functional outside than the typical mobility devices commonly used today such as walkers, crutches, and rollators. The Afari transfers weight from a user through the arm rests and enforces an upright posture while walking with correct adjustments to the arm rest height. In addition to assisting with walking or running, a sensor system fitted to the Afari device has been designed to analyze different aspects of activity tracking such as the dynamic loading applied to the arm rests, spatial-temporal gait parameters, speed, and distance. This includes various sensors, namely, load cells for each arm rest, an inertial measurement unit, and a speed and distance sensor that wirelessly transmit data via Bluetooth Low Energy (BLE) to either a smartphone or computer. The total distance, pitch angle, right and left loading on each armrest can be viewed in real time by the user. An algorithm was created in MATLAB to process all the raw data and compute cadence, stride length, average toe-off and heel strike angle, swing and stance time, and speed over the duration of active use. An Afari user can monitor these different aspects of their activity and adjust accordingly to potentially improve their balance or gait

Validity and reliability of the new Basic Functional Assessment protocol (BFA)

The global evaluation of motion patterns can examine the synchrony of neuromuscular control, range of motion, strength, resistance, balance and coordination needed to complete the movement. Visual assessments are commonly used to detect risk factors. However, it is essential to define standardized field-based tests that can evaluate with accuracy. The aims of the study were to design a protocol to evaluate fundamental motor patterns (FMP), and to analyze the validity and reliability of an instrument created to provide information about the quality of movement in FMP. Five tasks were selected: Overhead Squat (OHS); Hurdle Step (HS); Forward Step Down (FSD); Shoulder Mobility (SM); Active Stretching Leg Raise (ASLR). A list of variables was created for the evaluation of each task. Ten qualified judges assessed the validity of the instrument, while six external observers performed inter-intra reliability. The results show that the instrument is valid according to the experts’ opinion; however, the reliability shows values below those established. Thus, the instrument was considered unreliable, so it is recommended to repeat the reliability process by performing more training sessions for the external observers. The present study creates the basic functional assessment (BFA), a new protocol which comprises five tasks and an instrument to evaluate FMP

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