Sensor Approach for Brain Pathophysiology of Freezing of Gait in Parkinson\u27s Disease Patients

Parkinson\u27s Disease (PD) affects over 1% of the population over 60 years of age and is expected to reach 1 million in the USA by the year 2020, growing by 60 thousand each year. It is well understood that PD is characterized by dopaminergic loss, leading to decreased executive function causing motor symptoms such as tremors, bradykinesia, dyskinesia, and freezing of gait (FoG) as well as non-motor symptoms such as loss of smell, depression, and sleep abnormalities. A PD diagnosis is difficult to make since there is no worldwide approved test and difficult to manage since its manifestations are widely heterogeneous among subjects. Thus, understanding the patient subsets and the neural biomarkers that set them apart will lead to improved personalized care. To explore the physiological alternations caused by PD on neurological pathways and their effect on motor control, it is necessary to detect the neural activity and its dissociation with healthy physiological function. To this effect, this study presents a custom ultra-wearable sensor solution, consisting of electroencephalograph, electromyograph, ground reaction force, and symptom measurement sensors for the exploration of neural biomarkers during active gait paradigms. Additionally, this study employed novel de-noising techniques for dealing with the motion artifacts associated with active gait EEG recordings and compared time-frequency features between a group of PD with FoG and a group of age-matched controls and found significant differences between several EEG frequency bands during start and end of normal walking (with a p\u3c0.05)

The detection of freezing of gait in Parkinson's disease using asymmetric basis function TV-ARMA time-frequency spectral estimation method

Freezing of gait (FOG) is an episodic gait disturbance affecting locomotion in Parkinson’s disease. As a biomarker to detect FOG, the Freeze index (FI), which is defined as the ratio of the areas under power spectra in ‘freeze’ band and in ‘locomotion’ band, can negatively be affected by poor time and frequency resolution of time-frequency spectrum estimate when short-time Fourier transform (STFT) or Wavelet transform (WT) is used. In this study, a novel high-resolution parametric time-frequency spectral estimation method is proposed to improve the accuracy of FI. A time-varying autoregressive moving average model (TV-ARMA) is first identified where the time-varying parameters are estimated using an asymmetric basis function expansion method. The TV-ARMA model is then transformed into frequency domain to estimate the time-frequency spectrum and calculate the FI. Results evaluated on the Daphnet Freezing of Gait Dataset show that the new method improves the time and frequency resolutions of the time-frequency spectrum and the associate FI has better performance in the detection of FOG than its counterparts based on STFT and WT methods do. Moreover, FOGs can be predicted in advance of its occurrence in most cases using the new method

Freezing of Gait Prediction From Accelerometer Data Using a Simple 1D-Convolutional Neural Network -- 8th Place Solution for Kaggle's Parkinson's Freezing of Gait Prediction Competition

Freezing of Gait (FOG) is a common motor symptom in patients with Parkinson's disease (PD). During episodes of FOG, patients suddenly lose their ability to stride as intended. Patient-worn accelerometers can capture information on the patient's movement during these episodes and machine learning algorithms can potentially classify this data. The combination therefore holds the potential to detect FOG in real-time. In this work I present a simple 1-D convolutional neural network that was trained to detect FOG events in accelerometer data. Model performance was assessed by measuring the success of the model to discriminate normal movement from FOG episodes and resulted in a mean average precision of 0.356 on the private leaderboard on Kaggle. Ultimately, the model ranked 8th out of 1379 teams in the Parkinson's Freezing of Gait Prediction competition. The results underscore the potential of Deep Learning-based solutions in advancing the field of FOG detection, contributing to improved interventions and management strategies for PD patients.Comment: 5 pages, 2 figures, competition report, for associated code see: https://github.com/janbrederecke/fo

Free-living monitoring of Parkinson’s disease: lessons from the field

Wearable technology comprises miniaturized sensors (e.g. accelerometers) worn on the body and/or paired with mobile devices (e.g. smart phones) allowing continuous patient monitoring in unsupervised, habitual environments (termed free-living). Wearable technologies are revolutionising approaches to healthcare due to their utility, accessibility and affordability. They are positioned to transform Parkinson’s disease (PD) management through provision of individualised, comprehensive, and representative data. This is particularly relevant in PD where symptoms are often triggered by task and free-living environmental challenges that cannot be replicated with sufficient veracity elsewhere. This review concerns use of wearable technology in free-living environments for people with PD. It outlines the potential advantages of wearable technologies and evidence for these to accurately detect and measure clinically relevant features including motor symptoms, falls risk, freezing of gait, gait, functional mobility and physical activity. Technological limitations and challenges are highlighted and advances concerning broader aspects are discussed. Recommendations to overcome key challenges are made. To date there is no fully validated system to monitor clinical features or activities in free living environments. Robust accuracy and validity metrics for some features have been reported, and wearable technology may be used in these cases with a degree of confidence. Utility and acceptability appears reasonable, although testing has largely been informal. Key recommendations include adopting a multi-disciplinary approach for standardising definitions, protocols and outcomes. Robust validation of developed algorithms and sensor-based metrics is required along with testing of utility. These advances are required before widespread clinical adoption of wearable technology can be realise

Wearable Platform for Automatic Recognition of Parkinson Disease by Muscular Implication Monitoring

The need for diagnostic tools for the characterization of progressive movement disorders - as the Parkinson Disease (PD) - aiming to early detect and monitor the pathology is getting more and more impelling. The parallel request of wearable and wireless solutions, for the real-time monitoring in a non-controlled environment, has led to the implementation of a Quantitative Gait Analysis platform for the extraction of muscular implications features in ordinary motor action, such as gait. The here proposed platform is used for the quantification of PD symptoms. Addressing the wearable trend, the proposed architecture is able to define the real-time modulation of the muscular indexes by using 8 EMG wireless nodes positioned on lower limbs. The implemented system “translates” the acquisition in a 1-bit signal, exploiting a dynamic thresholding algorithm. The resulting 1-bit signals are used both to define muscular indexes both to drastically reduce the amount of data to be analyzed, preserving at the same time the muscular information. The overall architecture has been fully implemented on Altera Cyclone V FPGA. The system has been tested on 4 subjects: 2 affected by PD and 2 healthy subjects (control group). The experimental results highlight the validity of the proposed solution in Disease recognition and the outcomes match the clinical literature results

Is the timed-up and go test feasible in mobile devices? A systematic review

The number of older adults is increasing worldwide, and it is expected that by 2050 over 2 billion individuals will be more than 60 years old. Older adults are exposed to numerous pathological problems such as Parkinson’s disease, amyotrophic lateral sclerosis, post-stroke, and orthopedic disturbances. Several physiotherapy methods that involve measurement of movements, such as the Timed-Up and Go test, can be done to support efficient and effective evaluation of pathological symptoms and promotion of health and well-being. In this systematic review, the authors aim to determine how the inertial sensors embedded in mobile devices are employed for the measurement of the different parameters involved in the Timed-Up and Go test. The main contribution of this paper consists of the identification of the different studies that utilize the sensors available in mobile devices for the measurement of the results of the Timed-Up and Go test. The results show that mobile devices embedded motion sensors can be used for these types of studies and the most commonly used sensors are the magnetometer, accelerometer, and gyroscope available in off-the-shelf smartphones. The features analyzed in this paper are categorized as quantitative, quantitative + statistic, dynamic balance, gait properties, state transitions, and raw statistics. These features utilize the accelerometer and gyroscope sensors and facilitate recognition of daily activities, accidents such as falling, some diseases, as well as the measurement of the subject's performance during the test execution.info:eu-repo/semantics/publishedVersio

Kvantitativna i kvalitativna procena obrasca hoda kod bolesnika sa Parkinsonovom bolešću

Background/Aim. Postural impairments and gait disorders in Parkinson's disease (PD) affect limits of stability, impaire postural adjustment, and evoke poor responses to perturbation. In the later stage of the disease, some patients can suffer from episodic features such as freezing of gait (FOG). Objective gait assessment and monitoring progress of the disease can give clinicians and therapist important information about changes in gait pattern and potential gait deviations, in order to prevent concomitant falls. The aim of this study was to propose a method for identification of freezing episodes and gait disturbances in patients with PD. A wireless inertial sensor system can be used to provide follow-up of the treatment effects or progress of the disease. Methods. The system is simple for mounting a subject, comfortable, simple for installing and recording, reliable and provides high-quality sensor data. A total of 12 patients were recorded and tested. Software calculates various gait parameters that could be estimated. User friendly visual tool provides information about changes in gait characteristics, either in a form of spectrogram or by observing spatiotemporal parameters. Based on these parameters, the algorithm performs classification of strides and identification of FOG types. Results. The described stride classification was merged with an algorithm for stride reconstruction resulting in a useful graphical tool that allows clinicians to inspect and analyze subject's movements. Conclusion. The described gait assessment system can be used for detection and categorization of gait disturbances by applying rule-based classification based on stride length, stride time, and frequency of the shank segment movements. The method provides an valuable graphical interface which is easy to interpret and provides clinicians and therapists with valuable information regarding the temporal changes in gait.Uvod/Cilj. Poremećaji hoda i ravnoteže kod bolesnika sa Parkinsonovom bolešću (PD) uključuju i poremećaje stabilnosti, održavanja ravnoteže prilikom hoda i nemogućnost adekvatne reakcije na iznenadne perturbacije. U kasnijim fazama bolesti neki bolesnici razvijaju i epizode motornog bloka, odnosno 'frizing' tokom hoda. Objektivno praćenje i merenje karakteristika hoda i promena obrasca hoda tokom progresije bolesti mogu pomoći kliničarima jer ukazuju na promene koje bi dovele do padova i ugrozile bolesnika. Cilj rada bio je razvoj metode koja bi identifikovala ovakve epizode kod bolesnika sa Parkinsonovom bolesti. Razvijeni bežični sistem sa senzorima mogao bi se koristiti za posmatranje efekata terapije ili progresije bolesti. Metode. U radu je prikazan sistem za objektivnu procenu obrasca hoda. Korišćenjem bežičnog senzorskog sistema koji koristi akcelerometre, žiroskope i senzore sile, moguće je dobiti procenu parametara hoda, ali i identifikovati 'frizing' epizode karakteristične za PD. Uz pomoć ovog sistema snimljeno je 12 bolesnika, te je na osnovu snimljenih signala razvijen novi softverski alat koji omogućava praćenje parametara hoda. Rezultati. Na osnovu dužine koraka, trajanja koraka i frekvencije pokreta, razvijen je algoritam za klasifikaciju tipova koraka i uočavanje promena frekvencija pokreta tokom hoda. Prikaz rezultata ovog sistema je dat kroz primer jednog bolesnika. Zaključak. Opisani sistem za procenu hoda može biti korišćen za kategorizaciju poremećaja hoda kroz posmatranje promena u dužini i trajanju koraka, kao i frekvencija segmenata noge. Razvijeni metod omogućava iliustrativni prikaz i grafički interfejs koji je jednostavan za interpretaciju i omogućava dobijanje informacija koje kliničarima mogu ukazati na trenutne promene u obrascu hoda

Optimization Algorithms for Integrating Advanced Facility-Level Healthcare Technologies into Personal Healthcare Devices

Healthcare is one of the most important services to preserve the quality of our daily lives, and it is capable of dealing with issues such as global aging, increase in the healthcare cost, and changes to the medical paradigm, i.e., from the in-facility cure to the prevention and cure outside the facility. Accordingly, there has been growing interest in the smart and personalized healthcare systems to diagnose and care themselves. Such systems are capable of providing facility-level diagnosis services by using smart devices (e.g., smartphones, smart watches, and smart glasses). However, in realizing the smart healthcare systems, it is very difficult, albeit impossible, to directly integrate high-precision healthcare technologies or scientific theories into the smart devices due to the stringent limitations in the computing power and battery lifetime, as well as environmental constraints. In this dissertation, we propose three optimization methods in the field of cell counting systems and gait-aid systems for Parkinson's disease patients that address the problems that arise when integrating a specialized healthcare system used in the facilities into mobile or wearable devices. First, we present an optimized cell counting algorithm based on heuristic optimization, which is a key building block for realizing the mobile point-of-care platforms. Second, we develop a learning-based cell counting algorithm that guarantees high performance and efficiency despite the existence of blurry cells due to out-focus and varying brightness of background caused by the limitation of lenses free in-line holographic apparatus. Finally, we propose smart gait-aid glasses for Parkinson’s disease patients based on mathematical optimization. ⓒ 2017 DGISTopenI. Introduction 1-- 1.1 Global Healthcare Trends 1-- 1.2 Smart Healthcare System 2-- 1.3 Benefits of Smart Healthcare System 3-- 1.4 Challenges of Smart Healthcare. 4-- 1.5 Optimization 6-- 1.6 Aims of the Dissertation 7-- 1.7 Dissertation Organization 8-- II.Optimization of a cell counting algorithm for mobile point-of-care testing platforms 9-- 2.1 Introduction 9-- 2.2 Materials and Methods. 13-- 2.2.1 Experimental Setup. 13-- 2.2.2 Overview of Cell Counting. 16-- 2.2.3 Cell Library Optimization. 18-- 2.2.4 NCC Approximation. 20-- 2.3 Results 21-- 2.3.1 Cell Library Optimization. 21-- 2.3.2 NCC Approximation. 23-- 2.3.3 Measurement Using an Android Device. 28-- 2.4 Summary 32-- III.Human-level Blood Cell Counting System using NCC-Deep learning algorithm on Lens-free Shadow Image. 33-- 3.1 Introduction 33-- 3.2 Cell Counting Architecture 36-- 3.3 Methods 37-- 3.3.1 Candidate Point Selection based on NCC. 37-- 3.3.2 Reliable Cell Counting using CNN. 40-- 3.4 Results 43-- 3.4.1 Subjects . 43-- 3.4.2 Evaluation for the cropped cell image 44-- 3.4.3 Evaluation on the blood sample image 46-- 3.4.4 Elapsed-time evaluation 50-- 3.5 Summary 50-- IV.Smart Gait-Aid Glasses for Parkinson’s Disease Patients 52-- 4.1 Introduction 52-- 4.2 Related Works 54-- 4.2.1 Existing FOG Detection Methods 54-- 4.2.2 Existing Gait-Aid Systems 56-- 4.3 Methods 57-- 4.3.1 Movement Recognition. 59-- 4.3.2 FOG Detection On Glasses. 62-- 4.3.3 Generation of Visual Patterns 66-- 4.4 Experiments . 67-- 4.5 Results 69-- 4.5.1 FOG Detection Performance. 69-- 4.5.2 Gait-Aid Performance. 71-- 4.6 Summary 73-- V. Conclusion 75-- Reference 77-- 요약문 89본 논문은 의료 관련 연구시설 및 병원 그리고 실험실 레벨에서 사용되는 전문적인 헬스케어 시스템을 개인의 일상생활 속에서 사용할 수 있는 스마트 헬스케어 시스템에 적용시키기 위한 최적화 문제에 대해 다룬다. 현대 사회에서 의료비용 증가 세계적인 고령화에 따라 의료 패러다임은 질병이 발생한 뒤 시설 내에서 치료 받는 방식에서 질병이나 건강관리에 관심있는 환자 혹은 일반인이 휴대할 수 있는 개인용 디바이스를 이용하여 의료 서비스에 접근하고, 이를 이용하여 질병을 미리 예방하는 방식으로 바뀌었다. 이에 따라 언제, 어디서나 스마트 디바이스(스마트폰, 스마트워치, 스마트안경 등)를 이용하여 병원 수준의 예방 및 진단을 실현하는 스마트 헬스케어가 주목 받고 있다. 하지만, 스마트 헬스케어 서비스 실현을 위하여 기존의 전문 헬스케어 장치 및 과학적 이론을 스마트 디바이스에 접목하는 데에는 스마트 디바이스의 제한적인 컴퓨팅 파워와 배터리, 그리고 연구소나 실험실에서 발생하지 않았던 환경적인 제약조건으로 인해 적용 할 수 없는 문제가 있다. 따라서 사용 환경에 맞춰 동작 가능하도록 최적화가 필요하다. 본 논문에서는 Cell counting 분야와 파킨슨 환자의 보행 보조 분야에서 전문 헬스케어 시스템을 스마트 헬스케어에 접목시키는데 발생하는 세 가지 문제를 제시하고 문제 해결을 위한 세 가지 최적화 알고리즘(Heuristic optimization, Learning-based optimization, Mathematical optimization) 및 이를 기반으로 하는 시스템을 제안한다.DoctordCollectio

Discrete wavelet transform based freezing of gait detection in Parkinson's disease

Wearable on body sensors have been employed in many applications including ambulatory monitoring and pervasive computing systems. In this work, a wearable assistant has been created for people suffering from Parkinson’s disease (PD), specifically with the Freezing of Gait (FoG) symptom. Wearable accelerometers were placed on the person’s body and used for movement measure. When FoG is detected, a rhythmic audio signal was given from the wearable assistant to motivate the wearer to continue walking. Long term monitoring results in collecting huge amounts of complex raw data; therefore, data analysis becomes impractical or infeasible resulting in the need for data reduction. In the present study, Discrete Wavelet Transform (DWT) has been used to extract the main features inherent in the key movement indicators for FoG detection. The discrimination capacities of these features were assessed using, i) Support Vector Machine (SVM) using a linear kernel function, and ii) Artificial Neural Network (ANN) with a two-layer feed-forward with hidden layer of 20 neurons that trained with conjugate gradient back- propagation. Using these two different machine learning techniques, we were capable of detecting FoG with an accuracy of 87.50% and 93.8%, respectively. Additionally, the comparison between the extracted features from DWT coefficients with those using Fast Fourier Transform (FFT) established accuracies of 93.8% and 81.3%, respectively. Finally, the discriminative features extracted from DWT yield to a robust multidimensional classification model compared to models in the literature based on a single feature. The work presented paves the way for reliable, real-time wearable sensors to aid people with PD