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

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

Machine Learning in Wearable Biomedical Systems

Wearable technology has added a whole new dimension in the healthcare system by real-time continuous monitoring of human body physiology. They are used in daily activities and fitness monitoring and have even penetrated in monitoring the health condition of patients suffering from chronic illnesses. There are a lot of research and development activities being pursued to develop more innovative and reliable wearable. This chapter will cover discussions on the design and implementation of wearable devices for different applications such as real-time detection of heart attack, abnormal heart sound, blood pressure monitoring, gait analysis for diabetic foot monitoring. This chapter will also cover how the signals acquired from these prototypes can be used for training machine learning (ML) algorithm to diagnose the condition of the person wearing the device. This chapter discusses the steps involved in (i) hardware design including sensors selection, characterization, signal acquisition, and communication to decision-making subsystem and (ii) the ML algorithm design including feature extraction, feature reduction, training, and testing. This chapter will use the case study of the design of smart insole for diabetic foot monitoring, wearable real-time heart attack detection, and smart-digital stethoscope system to show the steps involved in the development of wearable biomedical systems

Use of Machine Learning for Early Detection of Knee Osteoarthritis and Quantifying Effectiveness of Treatment Using Force Platform

Knee osteoarthritis is one of the most prevalent chronic diseases. It leads to pain, stiffness, decreased participation in activities of daily living and problems with balance recognition. Force platforms have been one of the tools used to analyse balance in patients. However, identification in early stages and assessing the severity of osteoarthritis using parameters derived from a force plate are yet unexplored to the best of our knowledge. Combining artificial intelligence with medical knowledge can provide a faster and more accurate diagnosis. The aim of our study is to present a novel algorithm to classify the occurrence and severity of knee osteoarthritis based on the parameters derived from a force plate. Forty-four sway movements graphs were measured. The different machine learning algorithms, such as K-Nearest Neighbours, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Decision Tree Classifier and Random Forest Classifier, were implemented on the dataset. The proposed method achieves 91% accuracy in detecting sway variation and would help the rehabilitation specialist to objectively identify the patient’s condition in the initial stage and educate the patient about disease progression

Gait rehabilitation monitor

This work presents a simple wearable, non-intrusive affordable mobile framework that allows remote patient monitoring during gait rehabilitation, by doctors and physiotherapists. The system includes a set of 2 Shimmer3 9DoF Inertial Measurement Units (IMUs), Bluetooth compatible from Shimmer, an Android smartphone for collecting and primary processing of data and persistence in a local database. Low computational load algorithms based on Euler angles and accelerometer, gyroscope and magnetometer signals were developed and used for the classification and identification of several gait disturbances. These algorithms include the alignment of IMUs sensors data by means of a common temporal reference as well as heel strike and stride detection algorithms to help segmentation of the remotely collected signals by the System app to identify gait strides and extract relevant features to feed, train and test a classifier to predict gait abnormalities in gait sessions. A set of drivers from Shimmer manufacturer is used to make the connection between the app and the set of IMUs using Bluetooth. The developed app allows users to collect data and train a classification model for identifying abnormal and normal gait types. The system provides a REST API available in a backend server along with Java and Python libraries and a PostgreSQL database. The machine-learning type is Supervised using Extremely Randomized Trees method. Frequency, time and time-frequency domain features were extracted from the collected and processed signals to train the classifier. To test the framework a set of gait abnormalities and normal gait were used to train a model and test the classifier.Este trabalho apresenta uma estrutura móvel acessível, simples e não intrusiva, que permite a monitorização e a assistência remota de pacientes durante a reabilitação da marcha, por médicos e fisioterapeutas que monitorizam a reabilitação da marcha do paciente. O sistema inclui um conjunto de 2 IMUs (Inertial Mesaurement Units) Shimmer3 da marca Shimmer, compatíveís com Bluetooth, um smartphone Android para recolha, e pré-processamento de dados e armazenamento numa base de dados local. Algoritmos de baixa carga computacional baseados em ângulos Euler e sinais de acelerómetros, giroscópios e magnetómetros foram desenvolvidos e utilizados para a classificação e identificação de diversas perturbações da marcha. Estes algoritmos incluem o alinhamento e sincronização dos dados dos sensores IMUs usando uma referência temporal comum, além de algoritmos de detecção de passos e strides para auxiliar a segmentação dos sinais recolhidos remotamente pelaappdestaframeworke identificar os passos da marcha extraindo as características relevantes para treinar e testar um classificador que faça a predição de deficiências na marcha durante as sessões de monitorização. Um conjunto de drivers do fabricante Shimmer é usado para fazer a conexão entre a app e o conjunto de IMUs através de Bluetooth. A app desenvolvida permite aos utilizadores recolher dados e treinar um modelo de classificação para identificar os tipos de marcha normais e patológicos. O sistema fornece uma REST API disponível num servidor backend recorrendo a bibliotecas Java e Python e a uma base de dados PostgreSQL. O tipo de machine-learning é Supervisionado usando Extremely Randomized Trees. Features no domínio do tempo, da frequência e do tempo-frequência foram extraídas dos sinais recolhidos e processados para treinar o classificador. Para testar a estrutura, um conjunto de marchas patológicas e normais foram utilizadas para treinar um modelo e testar o classificador

Latest research trends in gait analysis using wearable sensors and machine learning: a systematic review

Gait is the locomotion attained through the movement of limbs and gait analysis examines the patterns (normal/abnormal) depending on the gait cycle. It contributes to the development of various applications in the medical, security, sports, and fitness domains to improve the overall outcome. Among many available technologies, two emerging technologies that play a central role in modern day gait analysis are: A) wearable sensors which provide a convenient, efficient, and inexpensive way to collect data and B) Machine Learning Methods (MLMs) which enable high accuracy gait feature extraction for analysis. Given their prominent roles, this paper presents a review of the latest trends in gait analysis using wearable sensors and Machine Learning (ML). It explores the recent papers along with the publication details and key parameters such as sampling rates, MLMs, wearable sensors, number of sensors, and their locations. Furthermore, the paper provides recommendations for selecting a MLM, wearable sensor and its location for a specific application. Finally, it suggests some future directions for gait analysis and its applications