The classification of minor gait alterations using wearable sensors and deep learning

Objective: This paper describes how non-invasive wearable sensors can be used in combination with deep learning to classify artificially induced gait alterations without the requirement for a medical professional or gait analyst to be present. This approach is motivated by the goal of diagnosing gait abnormalities on a symptom-by-symptom basis, irrespective of other neuromuscular movement disorders the patients may be affected by. This could lead to improvements in treatment and offer a greater insight into movement disorders. Methods: In-shoe pressure was measured for 12 able-bodied participants, each subject to eight artificially induced gait alterations, achieved by modifying the underside of the shoe. The data were recorded at 100 Hz over 2520 data channels and were analyzed using the deep learning architecture and the long term short term memory networks. Additionally, the rationale for the decision-making process of these networks was investigated. Conclusion: Long term short term memory networks are applicable to the classification of the gait function. The classifications can be made using only 2 s of sparse data (82.0% accuracy over 96 000 instances of test data) from participants who were not a part of the training set. Significance: This paper provides potential for the gait function to be accurately classified using non-invasive techniques, and at more regular intervals, outside of a clinical setting, without the need for healthcare professionals to be present

Using transfer learning for classification of gait pathologies

Different diseases can affect an individual’s gait in different ways and, therefore, gait analysis can provide important insights into an individual’s health and well-being. Currently, most systems that perform gait analysis using 2D video are limited to simple binary classification of gait as being either normal or impaired. While some systems do perform gait classification across different pathologies, the reported results still have a considerable margin for improvement. This paper presents a novel system that performs classification of gait across different pathologies, with considerably improved results. The system computes the walking individual’s silhouettes, which are computed from a 2D video sequence, and combines them into a representation known as the gait energy image (GEI), which provides robustness against silhouette segmentation errors. In this work, instead of using a set of handcrafted gait features, feature extraction is done using the VGG-19 convolutional neural network. The network is fine-tuned to automatically extract the features that best represent gait pathologies, using transfer learning. The use of transfer learning improves the classification accuracy while avoiding the need of a very large training set, as the network is pre-trained for generic image description, which also contributes to a better generalization when tested across different datasets. The proposed system performs the final classification using linear discriminant analysis (LDA). Obtained results show that the proposed system outperforms the state-of-the-art, achieving a classification accuracy of 95% on a dataset containing gait sequences affected by diplegia, hemiplegia, neuropathy and Parkinson’s disease, along with normal gait sequences.info:eu-repo/semantics/acceptedVersio

Wearables for Movement Analysis in Healthcare

Quantitative movement analysis is widely used in clinical practice and research to investigate movement disorders objectively and in a complete way. Conventionally, body segment kinematic and kinetic parameters are measured in gait laboratories using marker-based optoelectronic systems, force plates, and electromyographic systems. Although movement analyses are considered accurate, the availability of specific laboratories, high costs, and dependency on trained users sometimes limit its use in clinical practice. A variety of compact wearable sensors are available today and have allowed researchers and clinicians to pursue applications in which individuals are monitored in their homes and in community settings within different fields of study, such movement analysis. Wearable sensors may thus contribute to the implementation of quantitative movement analyses even during out-patient use to reduce evaluation times and to provide objective, quantifiable data on the patients’ capabilities, unobtrusively and continuously, for clinical purposes

The classification of multiple interacting gait abnormalities using insole sensors and machine learning

In this work we investigate the effectiveness of a wireless in-shoe pressure sensing system used in combination with a type of machine learning referred to as long term short term memory networks (LSTMs) to classify multiple interacting gait perturbations. Artificially induced gait perturbations consisted of restricted knee extension and altered under foot centre of pressure (COP). The primary aim was to assess the capacity to diagnose gait abnormalities without the need to attend a gait laboratory or visit a clinical healthcare professional, through the use of technology. Ultimately, such a system could be used to autonomously generate therapeutic guidance and provide healthcare professionals with accurate up to date information about a patients gait. The results show that LSTMs are capable of classifying complex interacting gait perturbations using in-shoe pressure data. When testing, 11 of 12 perturbation conditions were correctly classified overall and 58.8% of all data instances were correctly classified (8.3% is random classification). This work illustrates that an automated low cost, non-invasive gait diagnosis system with minimal sensors can be used to identify interacting gait abnormalities in individuals and has further potential to be used in a healthcare setting

Recent Advances in Motion Analysis

The advances in the technology and methodology for human movement capture and analysis over the last decade have been remarkable. Besides acknowledged approaches for kinematic, dynamic, and electromyographic (EMG) analysis carried out in the laboratory, more recently developed devices, such as wearables, inertial measurement units, ambient sensors, and cameras or depth sensors, have been adopted on a wide scale. Furthermore, computational intelligence (CI) methods, such as artificial neural networks, have recently emerged as promising tools for the development and application of intelligent systems in motion analysis. Thus, the synergy of classic instrumentation and novel smart devices and techniques has created unique capabilities in the continuous monitoring of motor behaviors in different fields, such as clinics, sports, and ergonomics. However, real-time sensing, signal processing, human activity recognition, and characterization and interpretation of motion metrics and behaviors from sensor data still representing a challenging problem not only in laboratories but also at home and in the community. This book addresses open research issues related to the improvement of classic approaches and the development of novel technologies and techniques in the domain of motion analysis in all the various fields of application

Recognition of gait patterns in human motor disorders using a machine learning approach

Dissertação de mestrado em Industrial Electronics and Computers EngineeringWith advanced age, the occurrence of motor disturbances becomes more prevalent and can lead to gait pathologies, increasing the risk of falls. Currently, there are many available gait monitoring systems that can aid in gait disorder diagnosis by extracting relevant data from a subject’s gait. This increases the amount of data to be processed in working time. To accelerate this process and provide an objective tool for a systematic clinical diagnosis support, Machine Learning methods are a powerful addition capable of processing great amounts of data and uncover non-linear relationships in data. The purpose of this dissertation is the development of a gait pattern recognition system based on a Machine Learning approach for the support of clinical diagnosis of post-stroke gait. This includes the development of a data estimation tool capable of computing several features from inertial sensors. Four different neural networks were be added to the classification tool: Feed-Forward (FFNN), convolutional (CNN) and two recurrent neural networks (LSTM and CLSTM). The performance of all classification models was analyzed and compared in order to select the most effective method of gait analysis. The performance metric used is Matthew’s Correlation Coefficient. The classifiers that exhibit the best performance where Support Vector Machines (SVM), k-Nearest Neighbors (KNN), CNN, LSTM and CLSTM, with a Matthew’s correlation coeficient of 1 in the test set. Despite the first two classifiers reaching the same performance of the three neural networks, the later reached this performance systematically and without the need of explicit dimensionality reduction methods.Com o avançar da idade, a ocorrência de distúrbios motores torna-se mais prevalente, conduzindo a patologias na marcha e aumentando o risco de quedas. Atualmente, muitos sistemas de monitorização de marcha extraem grandes quantidades de dados biomecânicos para apoio ao diagnóstico clínico, aumentando a quantidade de dados a ser processados em tempo útil. Para acelerar esse processo e proporcionar uma ferramenta objetiva de apoio sistemático ao diagnóstico clínico, métodos de Machine Learning são uma poderosa adição, processando grandes quantidades de dados e descobrindo relações não-lineares entre dados. Esta dissertação tem o objetivo de desenvolver um sistema de reconhecimento de padrões de marcha com uma abordagem de Machine Learning para apoio ao diagnóstico clínico da marcha de vitimas de AVC. Isso inclui o desenvolvimento de uma ferramenta de estimação de dados biomecânicos e cálculo de features, a partir de sensores inerciais. Quatro redes neuronais foram implementadas numa ferramenta de classificação: uma rede Feed-Forward (FFNN), uma convolucinal (CNN), e duas redes recorrentes (LSTM e CLSTM). O desempenho de todos os modelos de classificação foi analisado. A métrica de desempenho usada é o coeficiente de correlação de Matthew. Os classificadores com melhor performance foram: Support Vector Machines (SVM), k-Nearest Neighbors (KNN), CNN, LSTM e CLSTM. Todos com uma performance igual a 1 no conjunto de teste. Apesar de os dois primeiros classificadores atingirem a mesma performance das redes neuronais, estas atingiram esta performance repetidamente e sem necessitar de métodos de redução de dimensionalidade

Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders

The aging population and the increased prevalence of neurological diseases have raised the issue of gait and balance disorders as a major public concern worldwide. Indeed, gait and balance disorders are responsible for a high healthcare and economic burden on society, thus, requiring new solutions to prevent harmful consequences. Recently, wearable sensors have provided new challenges and opportunities to address this issue through innovative diagnostic and therapeutic strategies. Accordingly, the book “Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders” collects the most up-to-date information about the objective evaluation of gait and balance disorders, by means of wearable biosensors, in patients with various types of neurological diseases, including Parkinson’s disease, multiple sclerosis, stroke, traumatic brain injury, and cerebellar ataxia. By adopting wearable technologies, the sixteen original research articles and reviews included in this book offer an updated overview of the most recent approaches for the objective evaluation of gait and balance disorders

A machine learning approach for clinical gait analysis and classification of polymyalgia rheumatica using myoelectric sensors

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe study focuses on Polymyalgia Rheumatica (PMR), an autoimmune musculoskeletal disease primarily affecting the shoulder blade and hip muscles in older adults, particularly women aged 50 and above. The research aims to address two main challenges: the need for more clarity on the disease's pathophysiology and the challenge of identifying disease severity in patients. The study introduces a novel approach involving movement assessment, by designing a low-cost MyoTracker system, and using electromyography (EMG) features to understand the impact on patients' hip muscles. A clinical trial was conducted at Komfo Anokye Teaching Hospital in Ghana, where the study employed a qualitative research approach to monitor movement patterns. Participants were tasked to perform exercises comprising of gait, knee lifting, and knee extension with sensors attached to the hip muscles. This research unfolds in three iterations, the first investigation involved hip muscular imbalances where the significant difference between patients and healthy controls in the maximum voluntary contraction (MVC) values was recorded. The bilateral difference computed between the left and right hip in patients exhibited 15% MVC on average compared to the healthy control group's 6%, indicating substantial hip muscular imbalances. The second iteration involved a movement assessment to identify specific movement patterns in patients. Support Vector Machine (SVM) achieves 85% accuracy for gait exercises, while Decision Tree (DT) performs less efficiently at 70%. SVM also excels in knee lifting exercises (70% accuracy), outperforming DT (60%). Based on hip muscle activation, patients' movement patterns significantly differ from healthy controls. In the third iteration, deep learning techniques, specifically RNN-LSTM and Vision Transformer (ViT), classify PMR disease severity based on EMG features. The study's results carry significant clinical implications with the evidence of hip muscular imbalances aiding in designing tailored rehabilitation protocols. Importantly, this study uses a cost-effective method for determining disease severity, enabling predictions about patients with severe PMR conditions. The key contribution of this thesis is the identification of patients’ specific movement patterns and the determination of PMR severity among patients. Other contributions are the detection of hip muscular imbalance in patients and the design of rehabilitation protocols to address hip muscular imbalances and improve patients' range of motion, enhancing overall well-being. In conclusion, this comprehensive study leverages innovative approaches, from a MyoTracker system for movement assessment to deep learning models, to unravel the complexities of PMR disease. The collaboration with medical experts emphasises the potential real-world impact of this research in enhancing the treatment and recovery processes for individuals.Ghana Scholarship Secretaria

A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction

Musculoskeletal and neurological disorders are the most common causes of walking problems among older people, and they often lead to diminished quality of life. Analyzing walking motion data manually requires trained professionals and the evaluations may not always be objective. To facilitate early diagnosis, recent deep learning-based methods have shown promising results for automated analysis, which can discover patterns that have not been found in traditional machine learning methods. We observe that existing work mostly applies deep learning on individual joint features such as the time series of joint positions. Due to the challenge of discovering inter-joint features such as the distance between feet (i.e. the stride width) from generally smaller-scale medical datasets, these methods usually perform sub-optimally. As a result, we propose a solution that explicitly takes both individual joint features and inter-joint features as input, relieving the system from the need of discovering more complicated features from small data. Due to the distinctive nature of the two types of features, we introduce a two-stream framework, with one stream learning from the time series of joint position and the other from the time series of relative joint displacement. We further develop a mid-layer fusion module to combine the discovered patterns in these two streams for diagnosis, which results in a complementary representation of the data for better prediction performance. We validate our system with a benchmark dataset of 3D skeleton motion that involves 45 patients with musculoskeletal and neurological disorders, and achieve a prediction accuracy of 95.56%, outperforming state-of-the-art methods

Gait Monitoring and Analysis: A Mathematical Approach

Gait abnormalities are common in the elderly and individuals diagnosed with Parkinson’s, often leading to reduced mobility and increased fall risk. Monitoring and assessing gait patterns in these populations play a crucial role in understanding disease progression, early detection of motor impairments, and developing personalized rehabilitation strategies. In particular, by identifying gait irregularities at an early stage, healthcare professionals can implement timely interventions and personalized therapeutic approaches, potentially delaying the onset of severe motor symptoms and improving overall patient outcomes. In this paper, we studied older adults affected by chronic diseases and/or Parkinson’s disease by monitoring their gait due to wearable devices that can accurately detect a person’s movements. In our study, about 50 people were involved in the trial (20 with Parkinson’s disease and 30 people with chronic diseases) who have worn our device for at least 6 months. During the experimentation, each device collected 25 samples from the accelerometer sensor for each second. By analyzing those data, we propose a metric for the “gait quality” based on the measure of entropy obtained by applying the Fourier transform