Parkinson\u27s Symptoms quantification using wearable sensors

Parkinson’s disease (PD) is a common neurodegenerative disorder affecting more than one million people in the United States and seven million people worldwide. Motor symptoms such as tremor, slowness of movements, rigidity, postural instability, and gait impairment are commonly observed in PD patients. Currently, Parkinsonian symptoms are usually assessed in clinical settings, where a patient has to complete some predefined motor tasks. Then a physician assigns a score based on the United Parkinson’s Disease Rating Scale (UPDRS) after observing the motor task. However, this procedure suffers from inter subject variability. Also, patients tend to show fewer symptoms during clinical visit, which leads to false assumption of the disease severity. The objective of this study is to overcome this limitations by building a system using Inertial Measurement Unit (IMU) that can be used at clinics and in home to collect PD symptoms data and build algorithms that can quantify PD symptoms more effectively. Data was acquired from patients seen at movement disorders Clinic at Sanford Health in Fargo, ND. Subjects wore Physilog IMUs and performed tasks for tremor, bradykinesia and gait according to the protocol approved by Sanford IRB. The data was analyzed using modified algorithm that was initially developed using data from normal subjects emulating PD symptoms. For tremor measurement, the study showed that sensor signals collected from the index finger more accurately predict tremor severity compared to signals from a sensor placed on the wrist. For finger tapping, a task measuring bradykinesia, the algorithm could predict with more than 80% accuracy when a set of features were selected to train the prediction model. Regarding gait, three different analysis were done to find the effective parameters indicative of severity of PD. Gait speed measurement algorithm was first developed using treadmill as a reference. Then, it was shown that the features selected could predict PD gait with 85.5% accuracy

Development of Markerless Systems for Automatic Analysis of Movements and Facial Expressions: Applications in Neurophysiology

This project is focused on the development of markerless methods for studying facial expressions and movements in neurology, focusing on Parkinson’s disease (PD) and disorders of consciousness (DOC). PD is a neurodegenerative illness that affects around 2% of the population over 65 years old. Impairments of voice/speech are among the main signs of PD. This set of impairments is called hypokinetic dysarthria, because of the reduced range of movements involved in speech. This reduction can be visible also in other facial muscles, leading to a hypomimia. Despite the high percentage of patients that suffer from dysarthria and hypomimia, only a few of them undergo speech therapy with the aim to improve the dynamic of articulatory/facial movements. The main reason is the lack of low cost methodologies that could be implemented at home. DOC after coma are Vegetative State (VS), characterized by the absence of self-awareness and awareness of the environment, and Minimally Conscious State (MCS), in which certain behaviors are sufficiently reproducible to be distinguished from reflex responses. The differential diagnosis between VS and MCS can be hard and prone to a high rate of misdiagnosis (~40%). This differential diagnosis is mainly based on neuro-behavioral scales. A key role to plan the rehabilitation in DOC patients is played by the first diagnosis after coma. In fact, MCS patients are more prone to a consciousness recovery than VS patients. Concerning PD the aim is the development of contactless systems that could be used to study symptoms related to speech and facial movements/expressions. The methods proposed here, based on acoustical analysis and video processing techniques could support patients during speech therapy also at home. Concerning DOC patients the project is focused on the assessment of reflex and cognitive responses to standardized stimuli. This would allow objectifying the perceptual analysis performed by clinicians

Систем за подршку одлучивању, евалуацију и праћење стања пацијената оболелих од неуродегенеративних болести

Системи за подршку клиничком одлучивању представљају рачунарске алате који применом напредних технологија могу утицати на доношење одлука у вези са пацијентима. У овој дисертацији представљени су истраживање и развој новог система за подршку одлучивању, евалуацију и праћење стања пацијената оболелих од неуродегенеративних болести. Анализа клинички релевантних и свакодневних покрета чини основу овог система. Обрасци ових покрета снимљени су помоћу бежичних, носивих сензора малих димензија и тежине, који не захтевају компликовану поставку и могу се једноставно применити у било ком окружењу. Први део система намењен је (раном) препознавању Паркинсонове болести (ПБ) на основу анализе хода и алгоритама дубоког учења. Резултати су показали да је ПБ пацијенте могуће препознати са високом тачношћу. Други део система посвећен је праћењу симптома ПБ брадикинезије применом резоновања који се базира на знању. Представљена је метода за анализу покрета који се користе за евалуацију брадикинезије. Поред тога, применом различитих метода обраде сигнала развијена је нова метрика за квантификацију важних карактеристика ових покрета. Предикција степена развоја симптома се заснива на новом експертском систему који у потпуности објективизује клиничке евалуационе критеријуме. Валидација је урађена на примеру покрета тапкања прстију, који је снимљен на пацијенатима са типичним и атипичним паркинсонизимом. Показана је висока усаглашеност у поређењу са клиничким подацима. Развијени систем је објективан, аутоматизован, једноставно се користи, садржи интуитиван графички и параметарски приказ резултата и значајно доприноси унапређењу клиничких процедура за евалуацију и праћење стања пацијената са неуродегенеративним болестима.Clinical decision support system represents a computer-aided tool that utilizes advanced technologies for influencing clinical decisions about patients. This dissertation presents research and development of a new decision support system for the assessment of patients with neurodegenerative diseases. The analysis of movements that are part of standard clinical scales or everyday activities represents the basis of the system. These movements are recorded using small and lightweight wearable, wireless sensors, which do not require complicated setup and can be easily applied in any environment. The first part of system is dedicated to the (early) recognition of Parkinson’s disease (PD) based on gait analysis and deep learning algorithms. PD patients could be identified with a high accuracy. The other part of the system is dedicated to the assessment of PD symptoms, more specifically, bradykinesia, utilizing the knowledge-based reasoning. A method for analysis of bradykinesia related movements is defined and presented. Moreover, by applying different signal processing techniques, new metrics have been developed to quantify the essential characteristics of these movements. The prediction of symptom severity was performed using new expert system that completely objectified the clinical evaluation criteria. Validation was performed on the example of the finger-tapping movement of patients with typical and atypical parkinsonism. A high compliance rate was obtained compared to clinical data. The developed system is objective, automated, easy to use, contains an intuitive graphical and parametric presentation of results, and significantly contributes to the improvement of clinical assessment of patients with neurodegenerative diseases

A machine learning approach to objective measurement of tremor severity in Parkinson's disease: clinical and user perspectives on wearable devices

Tremor is the most typical common and simply recognised symptom of Parkinson's disease (PD) and presents in 70% - 90% of PD patients. In addition, tremor severity often indicates PD progress and severity and it can be used to evaluate treatment efficiency. Currently, the severity of Parkinson's tremor is scored based on the Movement Disorders Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS), however, the MDS-UPDRS is subjective and can be a lengthy process. Advances in wearable technologies combined with Machine Learning (ML) techniques have enabled the development of new approaches for the objective assessment of PD motor symptoms. A limited number of commercial systems are available with limited adoption and implementation due to the apparent lack of clinicians' and patients' perspectives. The goal of this research is to develop and validate a comprehensive solution to measure and quantify PD tremor severity objectively that incorporates the analysis of the perspective of the patients and healthcare professionals and provide an appropriate technology based solution. A holistic approach was adopted comprising of qualitative and quantitative methods divided into three stages. Firstly, a qualitative method using semi-structured interviews identified the perspectives of both healthcare professionals and patients linked to current assessment methods and their requirements for wearable devices. The results showed that a well-known assessment process such as MDS-UPDRS was not used routinely in clinics as it is time consuming, subjective, inaccurate and dependent on patients' memories. Participants suggested that objective assessment methods are needed to increase the chance of effective treatment. The participants' perspectives were positive toward using wearable devices. Healthcare professionals stated a need for an economical solution that provides concise information and is easy to use and interpret and should mimic the current scale. Secondly, a novel framework is proposed to enhance the tremor severity classification. The proposed approach is a combination of signal processing and resampling techniques integrated with well-known classifiers. The results show that over-sampling techniques performed better than other resampling techniques. The proposed approach has solved the imbalanced data problem and it has improved tremor severity detection significantly without neglecting minority classes and achieved 95:04% overall accuracy, 96% G-mean, 93% IBA and 99% AUC with Artificial Neural Network based on Multi-Layer Perceptron (ANN-MLP) with Borderline SMOTE. Finally a recommended system was identified to measure tremor severity. The system comprises of recommended tasks, classier, classier hyper-parameters and resampling technique. In this stage, a novel comprehensive method is developed to discriminate tasks' effect on tremor severity detection by developing an efficient and unique metric rule-based algorithm to identify recommended and non recommended tasks to be performed for tremor data collection. This establishes a novel quantitative framework that is based on an exhaustive sequential filtering algorithm that takes into consideration various combinations based on different advanced metrics instead of depending on a single metric. Results showed that ADL tasks that involve direct wrist movements are not suitable for tremor severity assessment. The findings of this research suggest that the recommended system is the SVM classier combined with Borderline SMOTE over-sampling technique and the tasks are sitting, stairs up and down, walking straight, walking while counting, and standing and achieved 98% accuracy, 98% F1-score, 97% IBA, 98% G-mean and 99% AUC. The novel system solutions and the results presented in this thesis demonstrate a significant contribution towards the objective measurement of tremors in Parkinson's disease. New data is also presented for policy-makers and healthcare professionals which provides new perspectives in relation to the objective assessment of PD in current clinical practice

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Gait Analysis for Early Neurodegenerative Diseases Classification Through the Kinematic Theory of Rapid Human Movements

Neurodegenerative diseases are particular diseases whose decline can partially or completely compromise the normal course of life of a human being. In order to increase the quality of patient's life, a timely diagnosis plays a major role. The analysis of neurodegenerative diseases, and their stage, is also carried out by means of gait analysis. Performing early stage neurodegenerative disease assessment is still an open problem. In this paper, the focus is on modeling the human gait movement pattern by using the kinematic theory of rapid human movements and its sigma-lognormal model. The hypothesis is that the kinematic theory of rapid human movements, originally developed to describe handwriting patterns, and used in conjunction with other spatio-temporal features, can discriminate neurodegenerative diseases patterns, especially in early stages, while analyzing human gait with 2D cameras. The thesis empirically demonstrates its effectiveness in describing neurodegenerative patterns, when used in conjunction with state-of-the-art pose estimation and feature extraction techniques. The solution developed achieved 99.1% of accuracy using velocity-based, angle-based and sigma-lognormal features and left walk orientation

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Machine Learning for Gait Classification

Machine learning is a powerful tool for making predictions and has been widely used for solving various classification problems in last decades. As one of important applications of machine learning, gait classification focuses on distinguishing different gait patterns by investigating the quality of gait of individuals and categorizing them as belonging to particular classes. The most studied gait pattern classes are the normal gait patterns of healthy people, i.e., gait of people who do not have any gait disability caused by an illness or an injury, and the pathological gait of patients suffering from illnesses which cause gait disorders such as neurodegenerative diseases (NDDs). There has been significant research work trying to solve the gait classification problems using advanced machine learning techniques, as the results may be beneficial for the early detection of underlined NDDs and for the monitoring of the gait rehabilitation progress. Despite the huge development in the field of gait analysis and classification, there are still a number of challenges open to further research. One challenge is the optimization of applied machine learning strategies to achieve better classification results. Another challenge is to solve gait classification problems even in the case when only limited amount of data are available. Further, a challenge is the development of machine learning-based methods that could provide more precise results to evaluate the level of gait quality or gait disorder, in contrast of just classifying gait pattern as belonging to healthy or pathological gait. The focus of this thesis is on the development, implementation and evaluation of a novel and reliable solution for the complex gait classification problems by addressing the current challenges. This solution is presented as a classification framework that can be applied to different types of gait signals, such as lower-limbs joint angle signals, trunk acceleration signals, and stride interval signals. Developed framework incorporates a hybrid solution which combines two models to enhance the classification performance. In order to provide a large number of samples for training the models, a sample generation method is developed which could segments the gait signals into smaller fragments. Classification is firstly performed on the data sample level, and then the results are utilized to generate the subject-level results using a majority voting scheme. Besides the class labels, a confidence score is computed to interpret the level of gait quality. In order to significantly improve the gait classification performances, in this thesis a novel feature extraction methods are also proposed using statistical methods, as well as machine learning approaches. Gaussian mixture model (GMM), least square regression, and k-nearest neighbors (kNN) are employed to provide additional significant features. Promising classification results are achieved using the proposed framework and the extracted features. The framework is ultimately applied to the management of patients and their rehabilitation, and is proved to be feasible in many clinical scenarios, such as the evaluation of medication effect on Parkinsona s disease (PD) patientsa gait, the long-term gait monitoring of the hereditary spastic paraplegia (HSP) patient under physical therapy

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems