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Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Foreword By Peter James -- Foreword By Dr william A. Wood -- Preface -- Acknowledgements -- Author -- Contributors -- Chapter 1 Gait Analysis: A Historical Overview -- A Move to Understanding: The Classical Period -- The Rebirth of Scientific Thought -- The Emergence of Modern Scientific Thought -- Advances in the Age of Enlightenment -- 1800-1900: The Gait Century -- The Twentieth Century and Beyond -- Gait Analysis and Podiatry -- Podiatry Education and Training in Relation to Gait Analysis -- References -- Chapter 2 Observational Gait Analysis - Progression and Application -- Introduction -- Development of Gait Analysis in Podiatry -- Definition and Contexts -- Anatomical Planes/Planes of Motion -- At What Age Does Adult Human Gait Occur? -- Function of Gait -- Objective and Subjective Gait Analysis -- Intra-Rater and Inter-Rater Reliability for Observational Gait Analysis -- Workflow in Observational Gait Analysis -- Video Playback -- Parameters in Analysis of Gait from Video Footage -- Perception and Observational Gait Analysis -- Other Factors to Consider in Observational Gait Analysis -- Summary -- References -- Chapter 3 Neurological Disorders Affecting Gait -- Anatomical Considerations -- History and Examination -- Common Neurological Disorders Affecting Gait and Balance -- Stroke -- Parkinson's Disease -- Neurodegenerative Disease and Other Movement Disorders -- Multiple Sclerosis -- Non-organic and Psychogenic Gait Disorders -- Peripheral Neuropathies -- Focal Nerve Damage and Foot Drop -- Disorders of the Neuromuscular Junction -- Motor Neurone Disease -- Muscle Disease -- Ataxia -- References -- Chapter 4 Emotions and Gait -- Introduction -- Basic Concepts of the Relationship between Emotions and Gait (Perception-Processing-Expression).

Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Foreword -- Preface -- Contributors -- Chapter 1 Introduction -- Observational gait analysis from video footage -- Measurements from video footage -- Gait as a biometric -- Notes -- References -- Chapter 2 The history of the use of gait analysis as evidence -- 1839 to 1959 -- 1960 to 1999 -- 2000 to 2019 -- Gait evidence in civil proceedings -- Discussion -- Note -- References -- Chapter 3 Fundamentals of human gait and gait analysis -- The gait cycle -- The gait cycle in detail -- Event 1: Initial contact -- Loading response -- Event 2: Opposite toe off -- Mid-stance -- Event 3: Heel rise -- Event 4: Opposite initial contact -- Event 5: Toe off -- Event 6: Feet adjacent -- Event 7: Tibia vertical -- Terminal foot contact -- Temporal and spatial parameters of gait -- Speed -- Cycle time and cadence -- Stride length -- Gait analysis -- Observational gait analysis without video recording -- Video-based gait analysis -- Three-dimensional motion analysis -- Notes -- References -- Chapter 4 The legal context of forensic gait analysis -- Part 1: The legal context in North America -- Principles of the law and the legal system -- Ethical role and responsibilities of the expert witness -- References -- Part 2: The legal context in the United Kingdom -- References -- Chapter 5 The development of the forensic gait analysis quality assurance process in the UK -- Identifying the requirement for quality assurance in forensic science -- Recognition of requirements for quality assurance in forensic gait analysis -- Creation of a writing group for forensic podiatry standards -- Creation of the writing group for forensic gait analysis standards -- Methods -- Practitioners -- Facilities -- Development of the Code of practice for forensic gait analysis document -- References.

Mobile robotic platforms have made inroads in the rehabilitation area as gait assistance devices. They have rarely been used for human gait monitoring and analysis. The integration of mobile robots in this field offers the potential to develop multiple medical applications and achieve new discoveries. This study proposes the use of a mobile robotic platform based on depth cameras to perform the analysis of human gait in practical scenarios. The aim is to prove the validity of this robot and its applicability in clinical settings. The mechanical and software design of the system is presented, as well as the design of the controllers of the lane-keeping, person-following, and servoing systems. The accuracy of the system for the evaluation of joint kinematics and the main gait descriptors was validated by comparison with a Vicon-certified system. Some tests were performed in practical scenarios, where the effectiveness of the lane-keeping algorithm was evaluated. Clinical tests with patients with multiple sclerosis gave an initial impression of the applicability of the instrument in patients with abnormal walking patterns. The results demonstrate that the system can perform gait analysis with high accuracy. In the curved sections of the paths, the knee joint is affected by occlusion and the deviation of the person in the camera reference system. This issue was greatly improved by adjusting the servoing system and the following distance. The control strategy of this robot was specifically designed for the analysis of human gait from the frontal part of the participant, which allows one to capture the gait properly and represents one of the major contributions of this study in clinical practice. ; The research leading to these results received funding from PID2020-118299RB-I00— SISTEMA ROBOTICO NO INVASIVO PARA EL ANALISIS BIOMECANICO DE LA MARCHA HUMANA-, "Convocatoria 2020 Proyectos de I+D+i—RTI Tipo B", of the Government of Spain. ; Peer reviewed

This paper presents a novel calibration procedure as a simple, yet powerful, method to place and align inertial sensors with body segments. The calibration can be easily replicated without the need of any additional tools. The proposed method is validated in three different applications: a computer mathematical simulation; a simplified joint composed of two semi-spheres interconnected by a universal goniometer; and a real gait test with five able-bodied subjects. Simulation results demonstrate that, after the calibration method is applied, the joint angles are correctly measured independently of previous sensor placement on the joint, thus validating the proposed procedure. In the cases of a simplified joint and a real gait test with human volunteers, the method also performs correctly, although secondary plane errors appear when compared with the simulation results. We believe that such errors are caused by limitations of the current inertial measurement unit (IMU) technology and fusion algorithms. In conclusion, the presented calibration procedure is an interesting option to solve the alignment problem when using IMUs for gait analysis. ; The work presented in this paper has been carried out with the financial support from: (i) the Commission of the European Union, within H2020, specific RTD programee "ICT 24 A. Research & Innovation Actions to Advance Key Technologies Relevant for Industrial and Service Robotics", under contract H2020-ICT24-2016-688175, "XoSoft, Soft Modular Biomimetic Exoskeleton to Assist People with Mobility Impairments"; (ii) Brazilian funding agencies CNPq (308529/2013-8), CAPES (PGPTA 3457/2014, PVE A126/2013) and FAPES (67566480, 72982608). We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI).

BackgroundMobility limitations are of particular interest in people with intellectual disabilities. The aim of this study was to present feasibility and mean values of gait parameters in people with intellectual disability and it was hypothesized that several trials would be necessary to gain stable values for this cohort.Material and MethodsThirty‐two adults with intellectual disability living in a residential facility were asked to do four walks in both normal and fast speed conditions over an instrumented walkway. Mean values of gait parameters were calculated over two different trial combinations, with TC1&2 representing the mean value of trials 1 and 2, and TC1‐4 representing the mean over all four trials.ResultsAll participants (Mage = 59.6 years; SD = 16.71) successfully performed the protocol. Gait parameters remained constant over all trials. No significant differences were found between the trial combination TC1&2 and trial combination TC1–4.ConclusionGait analysis was found to be feasible in people with intellectual disability. Overall, the present authors recommend performing two trials and to calculate the means of gait parameters over these two trials to gain stable values.

Climbing stairs is a fundamental part of daily life, adding additional demands on the postural control system compared to level walking. Although real-world gait analysis studies likely contain stair ambulation sequences, algorithms dedicated to the analysis of such activities are still missing. Therefore, we propose a new gait analysis pipeline for foot-worn inertial sensors, which can segment, parametrize, and classify strides from continuous gait sequences that include level walking, stair ascending, and stair descending. For segmentation, an existing approach based on the hidden Markov model and a feature-based gait event detection were extended, reaching an average segmentation F1 score of 98.5% and gait event timing errors below ±10ms for all conditions. Stride types were classified with an accuracy of 98.2% using spatial features derived from a Kalman filter-based trajectory reconstruction. The evaluation was performed on a dataset of 20 healthy participants walking on three different staircases at different speeds. The entire pipeline was additionally validated end-to-end on an independent dataset of 13 Parkinson's disease patients. The presented work aims to extend real-world gait analysis by including stair ambulation parameters in order to gain new insights into mobility impairments that can be linked to clinically relevant conditions such as a patient's fall risk and disease state or progression. This work was supported by the Mobilise-D project that has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 820820. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations (EFPIA). Content in this publication reflects the authors' view and neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein.