Motion Cues Analysis for Parkinson Gait Recognition

Background: Previous assessment methods for PG recognition used sensor mechanisms for PG that may cause discomfort. In order to avoid stress of applying wearable sensors, computer vision (CV) based diagnostic systems for PG recognition have been proposed. Main constraints in these methods are the laboratory setup procedures: Novel colored dresses for the patients were specifically designed to segment the test body from a specific colored background. Objective: To develop an image processing tool for home-assessment of Parkinson Gait(PG) by analyzing motion cues extracted during the gait cycles. Methods: The system is based on the idea that a normal body attains equilibrium during the gait by aligning the body posture with the axis of gravity. Due to the rigidity in muscular tone, persons with PD fail to align their bodies with the axis of gravity. The leaned posture of PD patients appears to fall forward. Whereas a normal posture exhibits a constant erect posture throughout the gait. Patients with PD walk with shortened stride angle (less than 15 degrees on average) with high variability in the stride frequency. Whereas a normal gait exhibits a constant stride frequency with an average stride angle of 45 degrees. In order to analyze PG, levodopa-responsive patients and normal controls were videotaped with several gait cycles. First, the test body is segmented in each frame of the gait video based on the pixel contrast from the background to form a silhouette. Next, the center of gravity of this silhouette is calculated. This silhouette is further skeletonized from the video frames to extract the motion cues. Two motion cues were stride frequency based on the cyclic leg motion and the lean frequency based on the angle between the leaned torso tangent and the axis of gravity. The differences in the peaks in stride and lean frequencies between PG and normal gait are calculated using Cosine Similarity measurements. Results: High cosine dissimilarity was observed in the stride and lean frequencies between PG and normal gait. High variations are found in the stride intervals of PG whereas constant stride intervals are found in the normal gait. Conclusions: We propose an algorithm as a source to eliminate laboratory constraints and discomfort during PG analysis. Installing this tool in a home computer with a webcam allows assessment of gait in the home environment.

Empirical investigation of decision tree ensembles for monitoring cardiac complications of diabetes

Cardiac complications of diabetes require continuous monitoring since they may lead to increased morbidity or sudden death of patients. In order to monitor clinical complications of diabetes using wearable sensors, a small set of features have to be identified and effective algorithms for their processing need to be investigated. This article focuses on detecting and monitoring cardiac autonomic neuropathy (CAN) in diabetes patients. The authors investigate and compare the effectiveness of classifiers based on the following decision trees: ADTree, J48, NBTree, RandomTree, REPTree, and SimpleCart. The authors perform a thorough study comparing these decision trees as well as several decision tree ensembles created by applying the following ensemble methods: AdaBoost, Bagging, Dagging, Decorate, Grading, MultiBoost, Stacking, and two multi-level combinations of AdaBoost and MultiBoost with Bagging for the processing of data from diabetes patients for pervasive health monitoring of CAN. This paper concentrates on the particular task of applying decision tree ensembles for the detection and monitoring of cardiac autonomic neuropathy using these features. Experimental outcomes presented here show that the authors' application of the decision tree ensembles for the detection and monitoring of CAN in diabetes patients achieved better performance parameters compared with the results obtained previously in the literature.

Biomedical sensing analyzer (BSA) for mobile-health (mHealth)-LTE

The rapid expansion of mobile-based systems, the capabilities of smartphone devices, as well as the radio access and cellular network technologies are the wind beneath the wing of mobile health (mHealth). In this paper, the concept of biomedical sensing analyzer (BSA) is presented, which is a novel framework, devised for sensor-based mHealth applications. The BSA is capable of formulating the Quality of Service (QoS) measurements in an end-to-end sense, covering the entire communication path (wearable sensors, link-technology, smartphone, cell-towers, mobile-cloud, and the end-users). The characterization and formulation of BSA depend on a number of factors, including the deployment of application-specific biomedical sensors, generic link-technologies, collection, aggregation, and prioritization of mHealth data, cellular network based on the Long-Term Evolution (LTE) access technology, and extensive multidimensional delay analyses. The results are studied and analyzed in a LabView 8.5 programming environment.

Data Mining in Clinical Practice for the Quantification of Motor Impairment in Parkinson's Disease

Advances in biomedical signal acquisition systems for motion analysis have led to lowcost and ubiquitous wearable sensors which can be used to record movement data in different settings. This implies the potential availability of large amounts of quantitative data. It is then crucial to identify and to extract the information of clinical relevance from the large amount of available data. This quantitative and objective information can be an important aid for clinical decision making. Data mining is the process of discovering such information in databases through data processing, selection of informative data, and identification of relevant patterns. The databases considered in this thesis store motion data from wearable sensors (specifically accelerometers) and clinical information (clinical data, scores, tests). The main goal of this thesis is to develop data mining tools which can provide quantitative information to the clinician in the field of movement disorders. This thesis will focus on motor impairment in Parkinson's disease (PD). Different databases related to Parkinson subjects in different stages of the disease were considered for this thesis. Each database is characterized by the data recorded during a specific motor task performed by different groups of subjects. The data mining techniques that were used in this thesis are feature selection (a technique which was used to find relevant information and to discard useless or redundant data), classification, clustering, and regression. The aims were to identify high risk subjects for PD, characterize the differences between early PD subjects and healthy ones, characterize PD subtypes and automatically assess the severity of symptoms in the home setting.

Identificazione di attività motorie a partire da misure con sensori indossabili: una revisione critica della letteratura

La revisione qui riportata valuta tutte le modalità di identificazione di task motori e posturali attraverso l'uso di sensori indossabili, principalmente accelerometri. Essa ha lo scopo di illustrare i sensori e gli algoritmi utilizzati in 23 articoli scelti in base alla loro qualità secondo una metodologia personalizzata di ricerca per fare il punto degli studi in questo campo, fino a questo momento. I dati estratti vengono utilizzati per individuare gli aspetti chiave riportati negli articoli, specialmente riguardanti l'algoritmo, focus della nostra revisione. Secondo questo criterio vengono selezionati 13 articoli, i quali si soffermano maggiormente sui modelli di approccio utilizzati, al fine di ottenere la più elevata accuratezza nell'identificazione. Questa in generale varia tra l'80-90% per i task motori più conosciuti(camminata, corsa e altri) mentre rimane limitata, intorno al 60-70% quando vengono analizzati i movimenti specifici degli arti superiori o inferiori.

Sviluppo e test di una rete di sensori indossabili per il monitoraggio e la riabilitazione di pazienti affetti da morbo di parkinson

Il recente sviluppo commerciale di smartphone, tablet e simili dispositivi, ha portato alla ricerca di soluzioni hardware e software dotate di un alto livello di integrazione, in grado di supportare una potenza di calcolo e una versatilità di utilizzo sempre più crescenti, pur mantenendo bassi i consumi e le dimensioni dei dispositivi. Questo sviluppo ha consentito parallelamente a simili tecnologie di trovare applicazione in tanti altri settori, tra i quali quello biomedicale. Il lavoro esposto in questa tesi si inserisce nel contesto appena descritto e, in particolare, consiste nello sviluppo di un sistema WBAN ideato per garantire maggiore flessibilità, controllo e personalizzazione nella terapia riabilitativa dei pazienti affetti da Morbo di Parkinson. In questo campo è stata dimostrata l'efficacia, in termini di miglioramento delle condizioni di vita dell'individuo, dell'esercizio fisico e in particolare di una serie di fisioterapie riabilitative specifiche. Tuttavia manca ancora uno strumento in grado di garantire più indipendenza, continuità e controllo,per le persone affette da MP, durante l'esecuzione di questi esercizi; senza che sia strettamente necessario l'intervento di personale specializzato per ogni seduta fisioterapeutica. Inoltre manca un sistema che possa essere comodamente trasportato dal paziente nelle attività di tutti i giorni e che consenta di registrare e trasmettere eventi particolari legati alla patologia, come blocchi motori e cadute accidentali. Il presente lavoro di tesi tratta della realizzazione di un Firmware per la gestione di un Nodo Centrale che funge da master in una rete WBAN a tre nodi. L'obbiettivo è quello di integrare in tale firmware le funzioni di acquisizione dati dai sensori on-board, comunicazione tra i nodi della rete e gestione delle periferiche hardware secondarie; in particolare per lo sviluppo è stato usato un Sistema Operativo Real-Time (RTOS) del quale sono esposti vantaggi e svantaggi dell’utilizzo.

VISUAL-INERTIAL INTEGRATION FOR HUMAN MOTION TRACKING AND NAVIGATION IN FREE-LIVING ENVIRONMENTS

The aging population has become a burning issue for all modern societies around the world recently. There are two important issues existing now to be solved. One is how to continuously monitor the movements of those people having suffered a stroke in natural living environment for providing more valuable feedback to guide clinical interventions. The other one is how to guide those old people effectively when they are at home or inside other buildings and to make their life easier and convenient. Therefore, human motion tracking and navigation have been active research fields with the increasing number of elderly people. However, motion capture has been extremely challenging to go beyond laboratory environments and obtain accurate measurements of human physical activity especially in free-living environments, and navigation in free-living environments also poses some problems such as the denied GPS signal and the moving objects commonly presented in free-living environments. This thesis seeks to develop new technologies to enable accurate motion tracking and positioning in free-living environments. This thesis comprises three specific goals using our developed IMU board and the camera from the imaging source company: (1) to develop a robust and real-time orientation algorithm using only the measurements from IMU; (2) to develop a robust distance estimation in static free-living environments to estimate people’s position and navigate people in static free-living environments and simultaneously the scale ambiguity problem, usually appearing in the monocular camera tracking, is solved by integrating the data from the visual and inertial sensors; (3) in case of moving objects viewed by the camera existing in free-living environments, to firstly design a robust scene segmentation algorithm and then respectively estimate the motion of the vIMU system and moving objects. To achieve real-time orientation tracking, an Adaptive-Gain Orientation Filter (AGOF) is proposed in this thesis based on the basic theory of deterministic approach and frequency-based approach using only measurements from the newly developed MARG (Magnet, Angular Rate, and Gravity) sensors. To further obtain robust positioning, an adaptive frame-rate vision-aided IMU system is proposed to develop and implement fast vIMU ego-motion estimation algorithms, where the orientation is estimated in real time from MARG sensors in the first step and then used to estimate the position based on the data from visual and inertial sensors. In case of the moving objects viewed by the camera existing in free-living environments, a robust scene segmentation algorithm is firstly proposed to obtain position estimation and simultaneously the 3D motion of moving objects. Finally, corresponding simulations and experiments have been carried out.

Preliminary results of ON/OFF detection using an integrated system for Parkinson's disease monitoring

This paper describes the experimental set up of a system composed by a set of wearable sensors devices for the recording of the motion signals and software algorithms for the signal analysis. This system is able to automatically detect and assess the severity of bradykinesia, tremor, dyskinesia and akinesia motor symptoms. Based on the assessment of the akinesia, the ON-OFF status of the patient is determined for each moment. The assessment performed through the automatic evaluation of the akinesia is compared with the status reported by the patients in their diaries. Preliminary results with a total recording period of 32 hours with two PD patients are presented, where a good correspondence (88.2 +/- 3.7 %) was observed. Best (93.7 por ciento) and worst (87 por ciento) correlation results are illustrated, together with the analysis of the automatic assessment of the akinesia symptom leading to the status determination. The results obtained are promising, and if confirmed with further data, this automatic assessment of PD motor symptoms will lead to a better adjustment of medication dosages and timing, cost savings and an improved quality of life of the patients.

Closed-Loop Deep Brain Stimulation: Bidirectional Neuroprosthetics for Tremor and BCI

Thesis (Ph.D.)--University of Washington, 2016-06

Security considerations for wireless carrier agonistic bio-monitoring systems

Advances in information and communications technology has led to a significant advances in noncontact portable devices capable of monitoring vital signals of patients. These wearable and implantable bio-monitoring systems allow collections of wearable sensors to be constructed as a Body Area Network (BAN) to record biological data for a subject. Such systems can be used to improve the quality of life and treatment outcomes for patients. One of the main uses for a bio-monitoring system is to record biological data values from a subject and provide them to a doctor or other medical professional. However, wearable bio-monitoring systems raise unique security considerations. In this paper, we discuss some of the security considerations that have arisen in our work around communications agnostic bio-monitoring, and how we have addressed these concerns. Furthermore, the issues related to the identifying and trusting sender and receiver entities are discussed.

Towards real-time profiling of sprints using wearable pressure sensors

On-body sensor systems for sport are challenging since the sensors must be lightweight and small to avoid discomfort, and yet robust and highly accurate to withstand and capture the fast movements associated with sport. In this work, we detail our experience of building such an on-body system for track athletes. The paper describes the design, implementation and deployment of an on-body sensor system for sprint training sessions. We autonomously profile sprints to derive quantitative metrics to improve training sessions. Inexpensive Force Sensitive Resistors (FSRs) are used to capture foot events that are subsequently analysed and presented back to the coach. We show how to identify periods of sprinting from the FSR data and how to compute metrics such as ground contact time. We evaluate our system using force plates and show that millisecond-level accuracy is achievable when estimating contact times. © 2012 Elsevier B.V. All rights reserved.

Antenna tuning for wearable wireless sensors

When miniaturized wireless sensors are placed on or close to the human body, they can experience a significant loss inperformance due to antenna detuning, resulting in degradationof wireless performance as well as decreased battery lifetime.Several antenna tuning technologies have been proposed formobile wireless devices but devices suitable for widespread integration have yet to emerge. This paper highlights the possible advantages of antenna tuning for wearable wireless sensors and presents the design and characterization of a prototype 433MHz tuner module.