Human Gait Analysis in Neurodegenerative Diseases: a Review

This paper reviews the recent literature on technologies and methodologies for quantitative human gait analysis in the context of neurodegnerative diseases. The use of technological instruments can be of great support in both clinical diagnosis and severity assessment of these pathologies. In this paper, sensors, features and processing methodologies have been reviewed in order to provide a highly consistent work that explores the issues related to gait analysis. First, the phases of the human gait cycle are briefly explained, along with some non-normal gait patterns (gait abnormalities) typical of some neurodegenerative diseases. The work continues with a survey on the publicly available datasets principally used for comparing results. Then the paper reports the most common processing techniques for both feature selection and extraction and for classification and clustering. Finally, a conclusive discussion on current open problems and future directions is outlined

Automatic recognition of gait patterns in human motor disorders using machine learning: A review

Background: automatic recognition of human movement is an effective strategy to assess abnormal gait patterns. Machine learning approaches are mainly applied due to their ability to work with multidimensional nonlinear features. Purpose: to compare several machine learning algorithms employed for gait pattern recognition in motor disorders using discriminant features extracted from gait dynamics. Additionally, this work highlights procedures that improve gait recognition performance. Methods: we conducted an electronic literature search on Web of Science, IEEE, and Scopus, using “human recognition”, “gait patterns’’, and “feature selection methods” as relevant keywords. Results: analysis of the literature showed that kernel principal component analysis and genetic algorithms are efficient at reducing dimensional features due to their ability to process nonlinear data and converge to global optimum. Comparative analysis of machine learning performance showed that support vector machines (SVMs) exhibited higher accuracy and proper generalization for new instances. Conclusions: automatic recognition by combining dimensional data reduction, cross-validation and normalization techniques with SVMs may offer an objective and rapid tool for investigating the subject's clinical status. Future directions comprise the real-time application of these tools to drive powered assistive devices in free-living conditions.This work was supported by the FCT - Fundação para a Ciência e Tecnologia - with the reference scholarship SFRH/BD/108309/2015, and the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalização (POCI) - with the reference project POCI-01-0145-FEDER-006941. Also, this work was partially supported by grant RYC-2014-16613 by Spanish Ministry of Economy and Competitiveness

Diagnosis of Neuro-Degenerative Diseases Using Probabilistic Neural Network

In recent years researchers have been given attention to present a non-invasive approach to deal with the diseases quickly and all the more unequivocally. The major cause of death of patients is due to the wrong diagnosis. Thus a right method is to be identified which would help the physicians to concentrate on the cause of illness and its diagnosis so as to abstain from squandering valuable time- that may be precious for the patient- on diagnosis. In this particular thesis, we have tried to build up a computerized methodology to deal with patients having problems in walking by analysing their gait signal. We chose four groups of patients, namely patients suffering from neuro-degenerative diseases such as Huntington’s disease, Parkinson’s disease and Amyotrophic Lateral Sclerosis and a group of healthy control subjects. So we have utilized Probabilistic Neural Network (PNN) as a classifier, to identify or differentiate the different patients and define the type of disease that they are suffering from, with accuracy in the range of 85- 95% so as to diagnose the diseases correctly and as a result, minimize the death rate

a comparative study of machine learning algorithms for physiological signal classification

Abstract The present work aims at the evaluation of the effectiveness of different machine learning algorithms on a variety of clinical data, derived from small, medium, and large publicly available databases. To this end, several algorithms were tested, and their performance, both in terms of accuracy and time required for the training and testing phases, are here reported. Sometimes a data preprocessing phase was also deemed necessary to improve the performance of the machine learning procedures, in order to reduce the problem size. In such cases a detailed analysis of the compression strategy and results is also presented

Gait analysis under the lens of statistical physics

Human gait; Irreversibility; Multi-fractal analysisMarcha humana; Irreversibilidad; Análisis multifractalMarxa humana; Irreversibilitat; Anàlisi multifractalHuman gait is a fundamental activity, essential for the survival of the individual, and an emergent property of the interactions between complex physical and cognitive processes. Gait is altered in many situations, due both to external constraints, as e.g. paced walk, and to physical and neurological pathologies. Its study is therefore important as a way of improving the quality of life of patients, but also as a door to understanding the inner working of the human nervous system. In this review we explore how four statistical physics concepts have been used to characterise normal and pathological gait: entropy, maximum Lyapunov exponent, multi-fractal analysis and irreversibility. Beyond some basic definitions, we present the main results that have been obtained in this field, as well as a discussion of the main limitations researchers have dealt and will have to deal with. We finally conclude with some biomedical considerations and avenues for further development.This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 851255). M.Z. and F.O. acknowledges the Spanish State Research Agency through Grant MDM-2017–0711 funded by MCIN/AEI/10.13039/501100011033. Authors acknowledge support from the Escuela Universitaria de Fisioterapia de la ONCE

Spinocerebellar Ataxia

This book is about spinocerebellar ataxia (SCA), which is among the most challenging pathologies in the neurological landscape. It covers basic concepts, functional classification, and new approaches to medical and non-medical treatment including rehabilitation/palliative care approaches. The volume also describes a wide spectrum of generalities and particularities about various forms of clinical and genetic presentations of ACS that have life-threatening characteristics and long-standing presentation with tremendous variability in presentation and clinical severity. In addition, the book presents important aspects of cerebellar anatomy, nutrition impact, genetic subtypes, and functional classification of medical and non-medical interventions related to stem cells, rehabilitation, and palliative care

Motor patterns evaluation of people with neuromuscular disorders for biomechanical risk management and job integration/reintegration

Neurological diseases are now the most common pathological condition and the leading cause of disability, progressively worsening the quality of life of those affected. Because of their high prevalence, they are also a social issue, burdening both the national health service and the working environment. It is therefore crucial to be able to characterize altered motor patterns in order to develop appropriate rehabilitation treatments with the primary goal of restoring patients' daily lives and optimizing their working abilities. In this thesis, I present a collection of published scientific articles I co-authored as well as two in progress in which we looked for appropriate indices for characterizing motor patterns of people with neuromuscular disorders that could be used to plan rehabilitation and job accommodation programs. We used instrumentation for motion analysis and wearable inertial sensors to compute kinematic, kinetic and electromyographic indices. These indices proved to be a useful tool for not only developing and validating a clinical and ergonomic rehabilitation pathway, but also for designing more ergonomic prosthetic and orthotic devices and controlling collaborative robots

Kinematic and kinetic analysis of transfemoral prosthesis

The feasibility of using transfemoral prosthesis Otto bock with 3R80 knee and articulated ankle1C30 “Trias” was analyzed from the perspective of dynamics and clinic. The kinematic and kinetic study of gait were performed on 5 amputated volunteers and 5 controls using videography techniques and force platform. Kinetic asymmetry gait is one of the main causes of hip joint degeneration. Combining kinematic and kinetic variables, we can draw important conclusions related to the dynamic imbalance of the main causes of hip degenerative diseases through the clinical trials of radiography film and density measurement, which has become an important tool to evaluate the feasibility of prosthetic design

Machine Learning and Similarity Network Approaches to Support Automatic Classification of Parkinson’s Diseases Using Accelerometer-based Gait Analysis

Parkinson’s Disease is a worldwide health problem, causing movement disorder and gait deficiencies. Automatic noninvasive techniques for Parkinson\u27s disease diagnosis is appreciated by patients, clinicians and neuroscientists. Gait offers many advantages compared to other biometrics specifically when data is collected using wearable devices; data collection can be performed through inexpensive technologies, remotely, and continuously. In this study, a new set of gait features associated with Parkinson’s Disease are introduced and extracted from accelerometer data. Then, we used a feature selection technique called maximum information gain minimum correlation (MIGMC). Using MIGMC, features are first reduced based on Information Gain method and then through Pearson correlation analysis and Tukey post-hoc multiple comparison test. The ability of several machine learning methods, including Support Vector Machine, Random Forest, AdaBoost, Bagging, and Naïve Bayes are investigated across different feature sets. Similarity Network analysis is also performed to validate our optimal feature set obtained using MIGMC technique. The effect of feature standardization is also investigated. Results indicates that standardization could improve all classifiers’ performance. In addition, the feature set obtained using MIGMC provided the highest classification performance. It is shown that our results from Similarity Network analysis are consistent with our results from the classification task, emphasizing on the importance of choosing an optimal set of gait features to help objective assessment and automatic diagnosis of Parkinson’s disease. Results illustrate that ensemble methods and specifically boosting classifiers had better performances than other classifiers. In summary, our preliminary results support the potential benefit of accelerometers as an objective tool for diagnostic purposes in PD

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions