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.

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.

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.

Caracterización y estudio de los sistemas operativos de tiempo real para sistemas embebidos FreeRTOS y ChibiOS/RT sobre un procesador SAM3X8E

Máster Universitario en Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI)

Propuesta de aplicación de una red de sensores en emergencias

El auge y evolución de los sistemas de comunicaciones móviles y de las redes inalámbricas avanzadas, sucedido desde principios del siglo XXI, han propiciado el uso de Redes de Sensores Inalámbricos (RSI) en múltiples ámbitos de interés. Dichas redes están típicamente compuestas por dispositivos inalámbricos autónomos que incorporan sensores para la recogida de datos de distinta naturaleza. Las RSI se caracterizan por su escalabilidad, ausencia de cableado, pequeño tamaño, bajo consumo, gran variedad de magnitudes físico/químicas medibles, entre otras, cuyas cualidades las hace muy interesantes para su aplicación en multitud de escenarios de la Sociedad de la Información, tales como domótica, agricultura y ganadería, medioambiente, salud, procesos industriales, logística, seguridad o ciudades inteligentes, ente otras. En este Trabajo Fin de Máster, se propone el uso de las RSI en el escenario de Emergencias donde cobra gran importancia la usabilidad, la fiabilidad, la disponibilidad, y la robustez de los sistemas a emplear en condiciones hostiles, especialmente en las de bomberos. Es por ello que se analizarán previamente los trabajos de RSI desarrollados para estos entornos y que sugieren qué aplicaciones garantizan el cumplimiento de los requerimientos mencionados. Se aborda la utilización de una primera RSI para la monitorización ambiental de tres Centros de Procesado de Datos (CPD) del departamento de TI de Emergencias, siendo este un entorno sin movilidad, más controlado y que aporta la adquisición de experiencia en la utilización de las RSI de cara a un entorno móvil más complejo. A continuación, para el entorno móvil se ha desarrollado y validado un prototipo experimental de RSI para el seguimiento de salida de parques de bomberos de vehículos con su dotación. Así mismo se implementa un prototipo para la ayuda a la localización de bomberos y/o personas en un siniestro. Estas RSI se desarrollan e implantan en el entorno de Emergencias del Ayuntamiento de Madrid, entidad sin cuyo apoyo habría sido imposible la aplicación práctica de este trabajo. SUMMARY. The rise and evolution of mobile communication systems and advanced wireless networks in early XXI century have allowed to taking advantage of Wireless Sensor Networks (WSN). These networks are composed of independent wireless devices that incorporate sensors for collecting data of different nature. The WSN is characterized by its scalability, no wiring, small size, low power consumption, wide range of physical magnitudes measurable, among others. These qualities make them very interesting for application in many scenarios to the Information Society, such as, domotic, agriculture, smart environment, ehealth, industrial control, logistics, security and smart cities, among others. This work proposes to use WSN in the emergency scenario where is very important the usability, reliability, availability, and robustness of the systems to be used in hostile conditions, especially in fire-fighters environment. That is why WSN works in emergency will be studied to tackle what applications compliance with the above requirements. The first WSN developed will be environmental monitoring of three CPDs IT department Emergency. This scenario is a non-mobile environment, more controlled and bring gaining experience in the use of WSN to face mobile environment which is more complex. Then, for the mobile environment is developed an experimental prototype of WSN for tracking fire vehicles living fire stations with their equipment. Another prototype is foreseen to be implemented to assist fire-fighters location and / or people in a disaster. These WSN are developed and implemented for Madrid City Emergency, whose involvement was critical to put this research into stage.

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 development for wearable wireless sensing systems

Embedded wireless sensor network (WSN) systems have been developed and used in a wide variety of applications such as local automatic environmental monitoring; medical applications analysing aspects of fitness and health energy metering and management in the built environment as well as traffic pattern analysis and control applications. While the purpose and functions of embedded wireless sensor networks have a myriad of applications and possibilities in the future, a particular implementation of these ambient sensors is in the area of wearable electronics incorporated into body area networks and everyday garments. Some of these systems will incorporate inertial sensing devices and other physical and physiological sensors with a particular focus on the application areas of athlete performance monitoring and e-health. Some of the important physical requirements for wearable antennas are that they are light-weight, small and robust and should also use materials that are compatible with a standard manufacturing process such as flexible polyimide or fr4 material where low cost consumer market oriented products are being produced. The substrate material is required to be low loss and flexible and often necessitates the use of thin dielectric and metallization layers. This paper describes the development of such a wearable, flexible antenna system for ISM band wearable wireless sensor networks. The material selected for the development of the wearable system in question is DE104i characterized by a dielectric constant of 3.8 and a loss tangent of 0.02. The antenna feed line is a 50 Ohm microstrip topology suitable for use with standard, high-performance and low-cost SMA-type RF connector technologies, widely used for these types of applications. The desired centre frequency is aimed at the 2.4GHz ISM band to be compatible with IEEE 802.15.4 Zigbee communication protocols and the Bluetooth standard which operate in this band.

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.