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

Demonstration of the Effect of Centre of Mass Height on Postural Sway Using Accelerometry for Balance Analysis

The effect of center of mass (COM) height on stand-still postural sway analysis was studied. For this purpose, a measurement apparatus was set up that included an accelerometry unit attached to a rod: three plumb lines, positioned at 50 cm, 75 cm, and 100 cm to the end of the rod, each supported a plumb bob. Using a vice mechanism, the rod was inclined from vertical (0 degree inclination) in steps of 5 degrees to 90 degrees. For each inclination, the corresponding inclination angle was manually measured by a protractor, and the positions of the three plumb bobs on the ground surface were also manually measured using a tape measure. Algebraic operations were used to calculate the inclination angle and the associated displacements of the plumb bobs on the ground surface from the accelerometry data. For each inclination angle, the manual and accelerometry calculated ground displacement produced by each plumb bulb were close. It was demonstrated that the height of COM, where the measurement was taken, affected the projected displacement on the ground surface. A higher height produced a greater displacement. This effect has an implication in postural sway analysis where the accelerometry readings may need comparison amongst subjects with different COM heights. To overcome this, a method that normalized the accelerometry readings by considering the COM height was proposed, and the associated results were presented

Gait Analysis Using Wearable Sensors

Gait analysis using wearable sensors is an inexpensive, convenient, and efficient manner of providing useful information for multiple health-related applications. As a clinical tool applied in the rehabilitation and diagnosis of medical conditions and sport activities, gait analysis using wearable sensors shows great prospects. The current paper reviews available wearable sensors and ambulatory gait analysis methods based on the various wearable sensors. After an introduction of the gait phases, the principles and features of wearable sensors used in gait analysis are provided. The gait analysis methods based on wearable sensors is divided into gait kinematics, gait kinetics, and electromyography. Studies on the current methods are reviewed, and applications in sports, rehabilitation, and clinical diagnosis are summarized separately. With the development of sensor technology and the analysis method, gait analysis using wearable sensors is expected to play an increasingly important role in clinical applications

Use of an inertial/magnetic sensor module for pedestrian tracking during normal walking

The ability to track pedestrians without any infrastructure support is required by numerous applications in the healthcare, augmented reality, and entertainment industries. In this paper, we present a simple self-contained pedestrian tracking method using a foot-mounted inertial and magnetic sensor module. Traditional methods normally incorporate double integration of the measured acceleration, but such methods are susceptible to the acceleration noise and integration drift. To avoid this issue, alternative approaches which make use of walking dynamics to aggregate individual stride have been explored. The key for stride aggregating is to accurately and reliably detect stride boundary and estimate the associated heading direction for each stride, but it is still not well solved yet due to sensor noise and external disturbance. In this paper, we propose to make use of the inertial sensor and magnetometer measurements for stride detection and heading direction determination. In our method, a simple and reliable stride detection method, which is resilient to random bouncing motions and sensor noise, is designed based on gyroscope and accelerometer measurements. Heading direction is then determined from the foot's orientation which fuses all the three types of sensor information together. The proposed pedestrian tracking method has been evaluated using experiments, including both short distance walking with different patterns and long distance walking performed indoors and outdoors. The good experimental results have illustrated the effectiveness of the proposed pedestrian tracking method

Evaluation of Angular Velocity Data from Inertial Measurement Units for Use in Clinical Settings

Evaluating the human gait cycle with inertial measurement units (IMU) may prove beneficial for applications such as diagnoses of musculoskeletal diseases and assessment of rehabilitation regimes. An IMU system is potentially applicable for diagnosing and assessing rehabilitation outcomes for a variety of neuromuscular diseases since it is small, portable, and less expensive than a camera system. IMUs directly measure angular velocity, whereas position data from a camera system must be processed twice to obtain this information. The purpose of this research is to determine repeatability of IMU angular velocity data, and agreement between angular velocity data from an IMU system and a camera system during normal gait. From this data, the feasibility of using IMU systems in clinical or rehabilitative settings for obtaining reliable angular velocity data will be determined. Lower limb motion data was collected simultaneously from six XSens MTx IMUs (XSens Technologies, Enschede, The Netherlands) and an 8-camera Qualisys Motion Capture system (Pro-Reflex, 240 Hz system). Each IMU consists of three orthogonal accelerometers, gyroscopes, and magnetometers. Data from 4 subjects (3 males, 2 females) were collected after an initialization technique before each trial to reduce effects of electro-magnetic interference with the IMUs. Knee joint angular velocities (Gx, Gy, Gz) corresponding to appropriate knee joint angles (flexion/extension, adduction/abduction, and internal/external rotations) from both systems were used in this analysis. Coefficients of variation (COV) were calculated for both IMU and camera data to determine variability of data from both systems. Knee joint Average angular velocities from both systems for each subject and limb were plotted together to visually evaluate correlation between data sets. F-test analyses were performed on linear models of the data to determine areas of co-linearity within the gait cycle, and at different intervals of angular velocities. The IMUs had lower COV\u27s than the camera system, likely due to the fact that the IMUs directly measure angular velocity, and camera system derives angular velocity from position data. However, these differences were not statistically different, likely due to variability within trials for individual subjects. Linearity between camera system and IMU angular velocity was visually observed only about the flexion/extension axis during segments of the gait cycle occurring from 0-4% (heel strike) and 65-100% (swing phase) of the gait cycle. Comparisons about the adduction/abduction and internal/external axes showed evidence of linearity for lower angular velocities. Linear regression statistics showed that the only correlational trend between the two systems was around 8-12% of the gait cycle for all three rotational axes. This may be due to drift of the IMU data. Although the camera system is the \u27gold standard\u27 in motion analysis, IMUs may be used for applications in which angular velocity for a flexion-extension movement at low joint angles is being evaluated. Future studies will include a larger sample population, and evaluate specific movements within human gait that affect drift of the IMUs. In addition, other IMU system designs could be evaluated for clinical use, and other algorithms that further reduce the effects of drift should be implemented

Production of biodiesel from palm oil

The present researches study the alternative fuel to replace the diesel fuel and how to produce the alternative fuel. Diesel fuel which is made from fossil fuel due the anaerobic decomposition through million years cause many harmful effect to the environment and human health such as the greenhouse effect, air pollution, acidification and more. The aim of the study is to produce the biodiesel from vegetable oil which is palm oil. It starts with mixing the 50 ml of ethanol and 0.5 g of sodium hydroxide as catalyst. The dissolved catalyst then will be poured into the heated 250 ml of palm oil and be stirred for 30 minutes. After the content is mixed the transesterification method is carried out. The content then transferred to separating funnel for separating process. At the end, two layers which the bottom layer will be by- product and the upper layer will be biodiesel. The biodiesel will enter purification method which rinse it with hot distilled water and ready to be test which are the density, kinematic viscosity and heating value of the biodiesel. By using biodiesel as an alternative fuel, the environment and human health will be secure more and it may attract people to more concern about the benefits of the biodiesel

Wearable inertial sensor system towards daily human kinematic gait analysis: benchmarking analysis to MVN BIOMECH

This paper presents a cost- and time-effective wearable inertial sensor system, the InertialLAB. It includes gyroscopes and accelerometers for the real-time monitoring of 3D-angular velocity and 3D-acceleration of up to six lower limbs and trunk segment and sagittal joint angle up to six joints. InertialLAB followed an open architecture with a low computational load to be executed by wearable processing units up to 200 Hz for fostering kinematic gait data to third-party systems, advancing similar commercial systems. For joint angle estimation, we developed a trigonometric method based on the segments’ orientation previously computed by fusion-based methods. The validation covered healthy gait patterns in varying speed and terrain (flat, ramp, and stairs) and including turns, extending the experiments approached in the literature. The benchmarking analysis to MVN BIOMECH reported that InertialLAB provides more reliable measures in stairs than in flat terrain and ramp. The joint angle time-series of InertialLAB showed good waveform similarity (>0.898) with MVN BIOMECH, resulting in high reliability and excellent validity. User-independent neural network regression models successfully minimized the drift errors observed in InertialLAB’s joint angles (NRMSE < 0.092). Further, users ranked InertialLAB as good in terms of usability. InertialLAB shows promise for daily kinematic gait analysis and real-time kinematic feedback for wearable third-party systems.This work has been supported in part by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under Grant SFRH/BD/108309/2015 and SFRH/BD/147878/2019, by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from FCT with the project SmartOs under Grant NORTE-01-0145-FEDER-030386, and through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI)—with the Reference Project under Grant POCI-01-0145-FEDER-006941