Watch Me Calibrate My Force-Sensing Shoes!

This paper presents a novel method for smaller-sized humanoid robots to self-calibrate their foot force sensors. The method consists of two steps: 1. The robot is commanded to move along planned whole-body trajectories in different double support configurations. 2. The sensor parameters are determined by minimizing the error between the measured and modeled center of pressure (CoP) and ground reaction force (GRF) during the robot's movement using optimization. This is the first proposed autonomous calibration method for foot force-sensing devices in smaller humanoid robots. Furthermore, we introduce a high-accuracy manual calibration method to establish CoP ground truth, which is used to validate the measured CoP using self-calibration. The results show that the self-calibration can accurately estimate CoP and GRF without any manual intervention. Our method is demonstrated using a NAO humanoid platform and our previously presented force-sensing shoes

Application of Machine Learning Methods for Human Gait Analysis

The majority of human gait analysis methods are limited to clinical gait laboratories. The calculation of gait parameters for athletes, during running in open environment, has endless possibilities of performance analysis to keep track of training. This thesis demonstrates a method to capture three-dimensional measurements of multidimensional human body movements during walking and running by means of GPS-aided-INS equipped data logger and also describes the two-dimensional (forward and vertical) analysis of captured three-dimensional movement. The gait segmentation based on the vertical velocity has been presented and the built data processing software can compute majority of traditional gait metrics such as stride duration, average speed, stride length, cadence and vertical oscillation. The equipment uses inexpensive pressure insoles to generate foot pressure data for model training and indirect estimation of vertical ground reaction force and ground contact time. Both machine and deep learning approaches are detailed for indirect estimation of vertical ground reaction force and ground contact time. The possibilities are also explored to make interpersonal gait parameter estimation by means of generalised prediction models. Both machine leaning and deep learning solution are presented to generate continuous vertical ground reaction force curves along with gait components. The methods, presented in this thesis, help to analyse human motion by means of gait segmentation and to calculate or estimate numerous spatio-temporal gait parameters. The intra-step variations in motion parameters are great help to analyse the different aspects of running in outdoor. The encouraging results reported in this thesis demonstrate the feasibility of device that provides detailed analysis about the performance of an athlete in outdoor running environment

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

Comprehensive and accurate estimation of lower body movement using few wearable sensors

Human pose estimation involves tracking the position and orientation (i.e., pose) of body segments, and estimating joint kinematics. It finds application in robotics, virtual reality, animation, and healthcare. Recent miniaturisation of inertial measurement units (IMUs) has paved the path towards inertial motion capture systems (MCS) suitable for use in unstructured environments. However, commercial inertial MCS attach one-sensor-per-body-segment (OSPS) which can be too cumbersome for daily use. A reduced-sensor-count configuration, where IMUs are placed on a subset of body segments, can improve user comfort while also reducing setup time and system cost. This work aims to develop pose estimation algorithms that track lower body motion using as few sensors as possible, towards developing a comfortable MCS for daily routine use. Such a tool can facilitate interactive rehabilitation, performance improvement, and the study of movement disorder progression to potentially enable predictive diagnostics. This thesis presents pose estimation algorithms that utilise biomechanical constraints, additional distance measurements, and balance assumptions to infer information lost from using less sensors. Specifically, it presents a novel use of Lie group representation of pose alongside a constrained extended Kalman filter for estimating pelvis, thigh, shank, and foot kinematics using only two or three IMUs. The algorithms iteratively use linear kinematic equations to predict the next state, leverage indirect observations and assumptions (e.g., pelvis height, zero-velocity update, flat-floor assumption, inter-IMU distance), and enforce biomechanical constraints (e.g., constant body segment length, hinged knee joints, range of motion). The algorithm was comprehensively evaluated on nine healthy subjects who performed free walking, jogging, and other random movements within a 4x4 m2 room using benchmark optical and inertial (i.e., OSPS) MCS. In contrast to existing benchmark datasets, both direct kinematics (e.g., Vicon plug-in gait commonly used in gait analysis) and inverse kinematics (used in robotics and musculoskeletal modelling) pose reconstruction, along with the corresponding measurements, are shared publicly. The mean position root-mean-square error relative to the mid-pelvis origin was 5.3±1.0 cm, while the sagittal knee and hip joint angle correlation coefficients were 0.85±0.05 and 0.89±0.05 indicating promising performance for joint kinematics in the sagittal plane

Biomechanical Spectrum of Human Sport Performance

Writing or managing a scientific book, as it is known today, depends on a series of major activities, such as regrouping researchers, reviewing chapters, informing and exchanging with contributors, and at the very least, motivating them to achieve the objective of publication. The idea of this book arose from many years of work in biomechanics, health disease, and rehabilitation. Through exchanges with authors from several countries, we learned much from each other, and we decided with the publisher to transfer this knowledge to readers interested in the current understanding of the impact of biomechanics in the analysis of movement and its optimization. The main objective is to provide some interesting articles that show the scope of biomechanical analysis and technologies in human behavior tasks. Engineers, researchers, and students from biomedical engineering and health sciences, as well as industrial professionals, can benefit from this compendium of knowledge about biomechanics applied to the human body

Bio-inspired soft robotic systems: Exploiting environmental interactions using embodied mechanics and sensory coordination

Despite the widespread development of highly intelligent robotic systems exhibiting great precision, reliability, and dexterity, robots remain incapable of performing basic manipulation tasks that humans take for granted. Manipulation in unstructured environments continues to be acknowledged as a significant challenge. Soft robotics, the use of less rigid materials in robots, has been proposed as one means of addressing these limitations. The technique enables more compliant interactions with the environment, allowing for increasingly adaptive behaviours better suited to more human-centric applications. Embodied intelligence is a biologically inspired concept in which intelligence is a function of the entire system, not only the controller or `brain'. This thesis focuses on the use of embodied intelligence for the development of soft robots, with a particular focus on how it can aid both perception and adaptability. Two main hypotheses are raised: first, that the mechanical design and fabrication of soft-rigid hybrid robots can enable increasingly environmentally adaptive behaviours, and second, that sensing materials and morphology can provide intelligence that assists perception through embodiment. A number of approaches and frameworks for the design and development of embodied systems are presented that address these hypotheses. It is shown how embodiment in soft sensor morphology can be used to perform localised processing and thereby distribute the intelligence over the body of a system. Specifically in soft robots, sensor morphology utilises the directional deformations created by interactions with the environment to aid in perception. Building on and formalising these ideas, a number of morphology-based frameworks are proposed for detecting different stimuli. The multifaceted role of materials in soft robots is demonstrated through the development of materials capable of both sensing and changes in material property. Such materials provide additional functionality beyond their integral scaffolding and static mechanical characteristics. In particular, an integrated material has been created exhibiting both sensing capabilities and also variable stiffness and `tack’ force, thereby enabling complex single-point grasping. To maximise the intelligence that can be gained through embodiment, a design approach to soft robots, `soft-rigid hybrid' design is introduced. This approach exploits passive behaviours and body dynamics to provide environmentally adaptive behaviours and sensing. It is leveraged by multi-material 3D printing techniques and novel approaches and frameworks for designing mechanical structures. The findings in this thesis demonstrate that an embodied approach to soft robotics provides capabilities and behaviours that are not currently otherwise achievable. Utilising the concept of `embodiment' results in softer robots with an embodied intelligence that aids perception and adaptive behaviours, and has the potential to bring the physical abilities of robots one step closer to those of animals and humans.EPSR