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

The use of wearable/portable digital sensors in Huntington’s disease: a systematic review

In chronic neurological conditions, wearable/portable devices have potential as innovative tools to detect subtle early disease manifestations and disease fluctuations for the purpose of clinical diagnosis, care and therapeutic development. Huntington’s disease (HD) has a unique combination of motor and non-motor features which, combined with recent and anticipated therapeutic progress, gives great potential for such devices to prove useful. The present work aims to provide a comprehensive account of the use of wearable/portable devices in HD and of what they have contributed so far. We conducted a systematic review searching MEDLINE, Embase, and IEEE Xplore. Thirty references were identified. Our results revealed large variability in the types of sensors used, study design, and the measured outcomes. Digital technologies show considerable promise for therapeutic research and clinical management of HD. However, more studies with standardized devices and harmonized protocols are needed to optimize the potential applicability of wearable/portable devices in HD

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

Quantitative Gait and Balance Outcomes for Ataxia Trials: Consensus Recommendations by the Ataxia Global Initiative Working Group on Digital-Motor Biomarkers

\ua9 2023, The Author(s).With disease-modifying drugs on the horizon for degenerative ataxias, ecologically valid, finely granulated, digital health measures are highly warranted to augment clinical and patient-reported outcome measures. Gait and balance disturbances most often present as the first signs of degenerative cerebellar ataxia and are the most reported disabling features in disease progression. Thus, digital gait and balance measures constitute promising and relevant performance outcomes for clinical trials. This narrative review with embedded consensus will describe evidence for the sensitivity of digital gait and balance measures for evaluating ataxia severity and progression, propose a consensus protocol for establishing gait and balance metrics in natural history studies and clinical trials, and discuss relevant issues for their use as performance outcomes

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

An Active Knee Orthesis for the Physical Therapy of NEurological Disorders

This paper presents the design of a new robotic orthotic solution aimed at improving the rehabilitation of a number of neurological disorders (Multiple Sclerosis, Post-Polio and Stroke). These neurological disorders are the most expensive for the European Health Systems, and the personalization of the therapy will contribute to a 47% cost reduction. Most orthotic devices have been evaluated as an aid to in-hospital training and rehabilitation in patients with motor disorders of various origins. The advancement of technology opens the possibility of new active orthoses able to improve function in the usual environment of the patient, providing added benefits to state-of-the-art devices in life quality. The active knee orthosis aims to serve as a basis to justify the prescription and adaptation of robotic orthoses in patients with impaired gait resulting from neurological processes.Peer Reviewe

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