992 resultados para home monitoring


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The widespread of low cost embedded electronics makes it easier to implement the smart devices that can understand either the environment or the user behaviors. The main object of this project is to design and implement home use portable smart electronics, including the portable monitoring device for home and office security and the portable 3D mouse for convenient use. Both devices in this project use the MPU6050 which contains a 3 axis accelerometer and a 3 axis gyroscope to sense the inertial motion of the door or the human hands movement. For the portable monitoring device for home and office security, MPU6050 is used to sense the door (either home front door or cabinet door) movement through the gyroscope, and Raspberry Pi is then used to process the data it receives from MPU6050, if the data value exceeds the preset threshold, Raspberry Pi would control the USB Webcam to take a picture and then send out an alert email with the picture to the user. The advantage of this device is that it is a small size portable stand-alone device with its own power source, it is easy to implement, really cheap for residential use, and energy efficient with instantaneous alert. For the 3D mouse, the MPU6050 would use both the accelerometer and gyroscope to sense user hands movement, the data are processed by MSP430G2553 through a digital smooth filter and a complementary filter, and then the filtered data will pass to the personal computer through the serial COM port. By applying the cursor movement equation in the PC driver, this device can work great as a mouse with acceptable accuracy. Compared to the normal optical mouse we are using, this mouse does not need any working surface, with the use of the smooth and complementary filter, it has certain accuracy for normal use, and it is easy to be extended to a portable mouse as small as a finger ring.

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BACKGROUND AND PURPOSE Visit-to-visit variability in systolic blood pressure (SBP) is associated with an increased risk of stroke and was reduced in randomized trials by calcium channel blockers and diuretics but not by renin-angiotensin system inhibitors. However, time of day effects could not be determined. Day-to-day variability on home BP readings predicts stroke risk and potentially offers a practical method of monitoring response to variability-directed treatment. METHODS SBP mean, maximum, and variability (coefficient of variation=SD/mean) were determined in 500 consecutive transient ischemic attack or minor stroke patients on 1-month home BP monitoring (3 BPs, 3× daily). Hypertension was treated to a standard protocol. Differences in SBP variability from 3 to 10 days before to 8 to 15 days after starting or increasing calcium channel blockers/diuretics versus renin-angiotensin system inhibitors versus both were compared by general linear models, adjusted for risk factors and baseline BP. RESULTS Among 288 eligible interventions, variability in SBP was reduced after increased treatment with calcium channel blockers/diuretics versus both versus renin-angiotensin system inhibitors (-4.0 versus 6.9 versus 7.8%; P=0.015), primarily because of effects on maximum SBP (-4.6 versus -1.0 versus -1.0%; P=0.001), with no differences in effect on mean SBP. Class differences were greatest for early-morning SBP variability (3.6 versus 17.0 versus 38.3; P=0.002) and maximum (-4.8 versus -2.0 versus -0.7; P=0.001), with no effect on midmorning (P=0.29), evening (P=0.65), or diurnal variability (P=0.92). CONCLUSIONS After transient ischemic attack or minor stroke, calcium channel blockers and diuretics reduced variability and maximum home SBP, primarily because of effects on morning readings. Home BP readings enable monitoring of response to SBP variability-directed treatment in patients with recent cerebrovascular events.

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The Houston region is home to arguably the largest petrochemical and refining complex anywhere. The effluent of this complex includes many potentially hazardous compounds. Study of some of these compounds has led to recognition that a number of known and probable carcinogens are at elevated levels in ambient air. Two of these, benzene and 1,3-butadiene, have been found in concentrations which may pose health risk for residents of Houston.^ Recent popular journalism and publications by local research institutions has increased the interest of the public in Houston's air quality. Much of the literature has been critical of local regulatory agencies' oversight of industrial pollution. A number of citizens in the region have begun to volunteer with air quality advocacy groups in the testing of community air. Inexpensive methods exist for monitoring of ozone, particulate matter and airborne toxic ambient concentrations. This study is an evaluation of a technique that has been successfully applied to airborne toxics.^ This technique, solid phase microextraction (SPME), has been used to measure airborne volatile organic hydrocarbons at community-level concentrations. It is has yielded accurate and rapid concentration estimates at a relatively low cost per sample. Examples of its application to measurement of airborne benzene exist in the literature. None have been found for airborne 1,3-butadiene. These compounds were selected for an evaluation of SPME as a community-deployed technique, to replicate previous application to benzene, to expand application to 1,3-butadiene and due to the salience of these compounds in this community. ^ This study demonstrates that SPME is a useful technique for quantification of 1,3-butadiene at concentrations observed in Houston. Laboratory background levels precluded recommendation of the technique for benzene. One type of SPME fiber, 85 μm Carboxen/PDMS, was found to be a sensitive sampling device for 1,3-butadiene under temperature and humidity conditions common in Houston. This study indicates that these variables affect instrument response. This suggests the necessity of calibration within specific conditions of these variables. While deployment of this technique was less expensive than other methods of quantification of 1,3-butadiene, the complexity of calibration may exclude an SPME method from broad deployment by community groups.^

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We performed the field-work during the dry (March 2014) and reainy season (May and June 2014) at the species type locality: Cerro Socopó, located at central-west region between Falcón, Lara and Zulia states, Venezuela. Socopó is a small and isolated mountain (1.571 m) belonging to the Ziruma mountains, and represents a relict of tropical mountain forest surrounded by semi-arid vegetation and grassland. This area is home to 312 species of vertebrates, including endangered and endemic amphibians species like Mannophryne lamarcai, Leptodactylus magistris and Dendropsophus amicorum. These forest and species are severely threatened by cattle ranch and illegal timber extraction, with forest formations only above 1000 meters. Despite this, no legal protected figure has been established in the area. We identified a 2.5 km secondary road transect within the study area based on the following criteria: 1) it cover different habitat types (streams and lagoons); and 2) it within the altitudinal gradient described for the specie (1,040 to 1,363 m). We identified three sampling points throughout the transect located in the vicinity of wetland habits: socopo1, socopo2 and socopo4. We did two types of recordings: 1) high quality recordings to characterize the advertisement call for M. lamarcai, non described to date (socopo4), and 2) recordings in different sampling points to evaluate call detectors performance in different acoustic scenarios (in all three localities).

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CO2 capture and storage (CCS) projects are presently developed to reduce the emission of anthropogenic CO2 into the atmosphere. CCS technologies are expected to account for the 20% of the CO2 reduction by 2050. One of the main concerns of CCS is whether CO2 may remain confined within the geological formation into which it is injected since post-injection CO2 migration in the time scale of years, decades and centuries is not well understood. Theoretically, CO2 can be retained at depth i) as a supercritical fluid (physical trapping), ii) as a fluid slowly migrating in an aquifer due to long flow path (hydrodynamic trapping), iii) dissolved into ground waters (solubility trapping) and iv) precipitated secondary carbonates. Carbon dioxide will be injected in the near future (2012) at Hontomín (Burgos, Spain) in the frame of the Compostilla EEPR project, led by the Fundación Ciudad de la Energía (CIUDEN). In order to detect leakage in the operational stage, a pre-injection geochemical baseline is presently being developed. In this work a geochemical monitoring design is presented to provide information about the feasibility of CO2 storage at depth.

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The work presented in this paper comprises the methodology and results of a pilot study on the feasibility of a wireless health monitoring system designed under main EU challenges for the promotion of healthy and active ageing. The system is focused on health assessment, prevention and lifestyle promotion of elderly people. Over a hundred participants including elderly users and caregivers tested the system in four pilot sites across Europe. Tests covered several scenarios in senior centers and real home environments, including performance and usability assessment. Results indicated strong satisfactoriness on usability, usefulness and user friendliness, and the acceptable level of reliability obtained supports future investigation on the same direction for further improvement and transfer of conclusions to the real world in the healthcare delivery.

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The deployment of the Ambient Intelligence (AmI) paradigm requires designing and integrating user-centered smart environments to assist people in their daily life activities. This research paper details an integration and validation of multiple heterogeneous sensors with hybrid reasoners that support decision making in order to monitor personal and environmental data at a smart home in a private way. The results innovate on knowledge-based platforms, distributed sensors, connected objects, accessibility and authentication methods to promote independent living for elderly people. TALISMAN+, the AmI framework deployed, integrates four subsystems in the smart home: (i) a mobile biomedical telemonitoring platform to provide elderly patients with continuous disease management; (ii) an integration middleware that allows context capture from heterogeneous sensors to program environment¿s reaction; (iii) a vision system for intelligent monitoring of daily activities in the home; and (iv) an ontologies-based integrated reasoning platform to trigger local actions and manage private information in the smart home. The framework was integrated in two real running environments, the UPM Accessible Digital Home and MetalTIC house, and successfully validated by five experts in home care, elderly people and personal autonomy.

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In this paper, the authors introduce a novel mechanism for data management in a middleware for smart home control, where a relational database and semantic ontology storage are used at the same time in a Data Warehouse. An annotation system has been designed for instructing the storage format and location, registering new ontology concepts and most importantly, guaranteeing the Data Consistency between the two storage methods. For easing the data persistence process, the Data Access Object (DAO) pattern is applied and optimized to enhance the Data Consistency assurance. Finally, this novel mechanism provides an easy manner for the development of applications and their integration with BATMP. Finally, an application named "Parameter Monitoring Service" is given as an example for assessing the feasibility of the system.

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As environmental standards become more stringent (e.g. European Directive 2008/50/EC), more reliable and sophisticated modeling tools are needed to simulate measures and plans that may effectively tackle air quality exceedances, common in large cities across Europe, particularly for NO2. Modeling air quality in urban areas is rather complex since observed concentration values are a consequence of the interaction of multiple sources and processes that involve a wide range of spatial and temporal scales. Besides a consistent and robust multi-scale modeling system, comprehensive and flexible emission inventories are needed. This paper discusses the application of the WRF-SMOKE-CMAQ system to the Madrid city (Spain) to assess the contribution of the main emitting sectors in the region. A detailed emission inventory was compiled for this purpose. This inventory relies on bottom-up methods for the most important sources. It is coupled with the regional traffic model and it makes use of an extensive database of industrial, commercial and residential combustion plants. Less relevant sources are downscaled from national or regional inventories. This paper reports the methodology and main results of the source apportionment study performed to understand the origin of pollution (main sectors and geographical areas) and define clear targets for the abatement strategy. Finally the structure of the air quality monitoring is analyzed and discussed to identify options to improve the monitoring strategy not only in the Madrid city but the whole metropolitan area.

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El uso de técnicas para la monitorización del movimiento humano generalmente permite a los investigadores analizar la cinemática y especialmente las capacidades motoras en aquellas actividades de la vida cotidiana que persiguen un objetivo concreto como pueden ser la preparación de bebidas y comida, e incluso en tareas de aseo. Adicionalmente, la evaluación del movimiento y el comportamiento humanos en el campo de la rehabilitación cognitiva es esencial para profundizar en las dificultades que algunas personas encuentran en la ejecución de actividades diarias después de accidentes cerebro-vasculares. Estas dificultades están principalmente asociadas a la realización de pasos secuenciales y al reconocimiento del uso de herramientas y objetos. La interpretación de los datos sobre la actitud de este tipo de pacientes para reconocer y determinar el nivel de éxito en la ejecución de las acciones, y para ampliar el conocimiento en las enfermedades cerebrales, sus consecuencias y severidad, depende totalmente de los dispositivos usados para la captura de esos datos y de la calidad de los mismos. Más aún, existe una necesidad real de mejorar las técnicas actuales de rehabilitación cognitiva contribuyendo al diseño de sistemas automáticos para crear una especie de terapeuta virtual que asegure una vida más independiente de estos pacientes y reduzca la carga de trabajo de los terapeutas. Con este objetivo, el uso de sensores y dispositivos para obtener datos en tiempo real de la ejecución y estado de la tarea de rehabilitación es esencial para también contribuir al diseño y entrenamiento de futuros algoritmos que pudieran reconocer errores automáticamente para informar al paciente acerca de ellos mediante distintos tipos de pistas como pueden ser imágenes, mensajes auditivos o incluso videos. La tecnología y soluciones existentes en este campo no ofrecen una manera totalmente robusta y efectiva para obtener datos en tiempo real, por un lado, porque pueden influir en el movimiento del propio paciente en caso de las plataformas basadas en el uso de marcadores que necesitan sensores pegados en la piel; y por otro lado, debido a la complejidad o alto coste de implantación lo que hace difícil pensar en la idea de instalar un sistema en el hospital o incluso en la casa del paciente. Esta tesis presenta la investigación realizada en el campo de la monitorización del movimiento de pacientes para proporcionar un paso adelante en términos de detección, seguimiento y reconocimiento del comportamiento de manos, gestos y cara mediante una manera no invasiva la cual puede mejorar la técnicas actuales de rehabilitación cognitiva para la adquisición en tiempo real de datos sobre el comportamiento del paciente y la ejecución de la tarea. Para entender la importancia del marco de esta tesis, inicialmente se presenta un resumen de las principales enfermedades cognitivas y se introducen las consecuencias que tienen en la ejecución de tareas de la vida diaria. Más aún, se investiga sobre las metodologías actuales de rehabilitación cognitiva. Teniendo en cuenta que las manos son la principal parte del cuerpo para la ejecución de tareas manuales de la vida cotidiana, también se resumen las tecnologías existentes para la captura de movimiento de manos. Una de las principales contribuciones de esta tesis está relacionada con el diseño y evaluación de una solución no invasiva para detectar y seguir las manos durante la ejecución de tareas manuales de la vida cotidiana que a su vez involucran la manipulación de objetos. Esta solución la cual no necesita marcadores adicionales y está basada en una cámara de profundidad de bajo coste, es robusta, precisa y fácil de instalar. Otra contribución presentada se centra en el reconocimiento de gestos para detectar el agarre de objetos basado en un sensor infrarrojo de última generación, y también complementado con una cámara de profundidad. Esta nueva técnica, y también no invasiva, sincroniza ambos sensores para seguir objetos específicos además de reconocer eventos concretos relacionados con tareas de aseo. Más aún, se realiza una evaluación preliminar del reconocimiento de expresiones faciales para analizar si es adecuado para el reconocimiento del estado de ánimo durante la tarea. Por su parte, todos los componentes y algoritmos desarrollados son integrados en un prototipo simple para ser usado como plataforma de monitorización. Se realiza una evaluación técnica del funcionamiento de cada dispositivo para analizar si es adecuada para adquirir datos en tiempo real durante la ejecución de tareas cotidianas reales. Finalmente, se estudia la interacción con pacientes reales para obtener información del nivel de usabilidad del prototipo. Dicha información es esencial y útil para considerar una rehabilitación cognitiva basada en la idea de instalación del sistema en la propia casa del paciente al igual que en el hospital correspondiente. ABSTRACT The use of human motion monitoring techniques usually let researchers to analyse kinematics, especially in motor strategies for goal-oriented activities of daily living, such as the preparation of drinks and food, and even grooming tasks. Additionally, the evaluation of human movements and behaviour in the field of cognitive rehabilitation is essential to deep into the difficulties some people find in common activities after stroke. This difficulties are mainly associated with sequence actions and the recognition of tools usage. The interpretation of attitude data of this kind of patients in order to recognize and determine the level of success of the execution of actions, and to broaden the knowledge in brain diseases, consequences and severity, depends totally on the devices used for the capture of that data and the quality of it. Moreover, there is a real need of improving the current cognitive rehabilitation techniques by contributing to the design of automatic systems to create a kind of virtual therapist for the improvement of the independent life of these stroke patients and to reduce the workload of the occupational therapists currently in charge of them. For this purpose, the use of sensors and devices to obtain real time data of the execution and state of the rehabilitation task is essential to also contribute to the design and training of future smart algorithms which may recognise errors to automatically provide multimodal feedback through different types of cues such as still images, auditory messages or even videos. The technology and solutions currently adopted in the field don't offer a totally robust and effective way for obtaining real time data, on the one hand, because they may influence the patient's movement in case of marker-based platforms which need sensors attached to the skin; and on the other hand, because of the complexity or high cost of implementation, which make difficult the idea of installing a system at the hospital or even patient's home. This thesis presents the research done in the field of user monitoring to provide a step forward in terms of detection, tracking and recognition of hand movements, gestures and face via a non-invasive way which could improve current techniques for cognitive rehabilitation for real time data acquisition of patient's behaviour and execution of the task. In order to understand the importance of the scope of the thesis, initially, a summary of the main cognitive diseases that require for rehabilitation and an introduction of the consequences on the execution of daily tasks are presented. Moreover, research is done about the actual methodology to provide cognitive rehabilitation. Considering that the main body members involved in the completion of a handmade daily task are the hands, the current technologies for human hands movements capture are also highlighted. One of the main contributions of this thesis is related to the design and evaluation of a non-invasive approach to detect and track user's hands during the execution of handmade activities of daily living which involve the manipulation of objects. This approach does not need the inclusion of any additional markers. In addition, it is only based on a low-cost depth camera, it is robust, accurate and easy to install. Another contribution presented is focused on the hand gesture recognition for detecting object grasping based on a brand new infrared sensor, and also complemented with a depth camera. This new, and also non-invasive, solution which synchronizes both sensors to track specific tools as well as recognize specific events related to grooming is evaluated. Moreover, a preliminary assessment of the recognition of facial expressions is carried out to analyse if it is adequate for recognizing mood during the execution of task. Meanwhile, all the corresponding hardware and software developed are integrated in a simple prototype with the purpose of being used as a platform for monitoring the execution of the rehabilitation task. Technical evaluation of the performance of each device is carried out in order to analyze its suitability to acquire real time data during the execution of real daily tasks. Finally, a kind of healthcare evaluation is also presented to obtain feedback about the usability of the system proposed paying special attention to the interaction with real users and stroke patients. This feedback is quite useful to consider the idea of a home-based cognitive rehabilitation as well as a possible hospital installation of the prototype.

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The aim of this study was to assess the way volleyball teams score with regard to: whether or not they won the game, whether they were the home or away team, the level of the opposing teams, and the type of confrontation. The sample was composed of 118,083 plays from 794 men’s volleyball matches and 125,751 plays from 719 women’s matches of Spain’s first division clubs (from the 2002-2003 season to the 2006-2007 season). The variables studied were: the way points were obtained in each play, being the home or away team, the level of the teams, the result of the match, and the type of confrontation between the teams with regard to their level. The results demonstrate that for both men’s and women’s teams, the majority of the points were obtained in attack and by opponent errors. Differences were found with regard to the way points were obtained when winning or losing the match was taken into account as well as when considering the level of the teams. This paper discusses the differences found with regard to whether the team is home or visiting and the type of confrontation.

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Thesis (Master's)--University of Washington, 2016-06

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We conducted a systematic review of the literature to identify studies in home telehealth that compared a home telehealth intervention with a non-telehealth standard/usual care alternative in terms of administrative changes, patient management decisions, patient outcomes, caregiver outcomes, economic impact or social impact on patients. A search of various databases produced 6643 references. Of these 769 papers were selected for more detailed investigation. These papers, combined with hand searching of relevant telehealth journals and cross-referencing of citations in identified publications, resulted in 138 papers referring to 130 projects for review. In this preliminary analysis we used a quality appraisal approach that took into account the study design. An additional analysis of patient numbers was then used to calculate a net evidence score. A large proportion of studies (80%) were randomised controlled trials. Only 22 projects (17%) reported economic data deemed to be sufficient for appraisal. Evidence exists for the clinical effectiveness of home telehealth in diabetes, the general area of mental health, high risk pregnancy monitoring, heart failure and cardiac disease.

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This work has, as its objective, the development of non-invasive and low-cost systems for monitoring and automatic diagnosing specific neonatal diseases by means of the analysis of suitable video signals. We focus on monitoring infants potentially at risk of diseases characterized by the presence or absence of rhythmic movements of one or more body parts. Seizures and respiratory diseases are specifically considered, but the approach is general. Seizures are defined as sudden neurological and behavioural alterations. They are age-dependent phenomena and the most common sign of central nervous system dysfunction. Neonatal seizures have onset within the 28th day of life in newborns at term and within the 44th week of conceptional age in preterm infants. Their main causes are hypoxic-ischaemic encephalopathy, intracranial haemorrhage, and sepsis. Studies indicate an incidence rate of neonatal seizures of 0.2% live births, 1.1% for preterm neonates, and 1.3% for infants weighing less than 2500 g at birth. Neonatal seizures can be classified into four main categories: clonic, tonic, myoclonic, and subtle. Seizures in newborns have to be promptly and accurately recognized in order to establish timely treatments that could avoid an increase of the underlying brain damage. Respiratory diseases related to the occurrence of apnoea episodes may be caused by cerebrovascular events. Among the wide range of causes of apnoea, besides seizures, a relevant one is Congenital Central Hypoventilation Syndrome (CCHS) \cite{Healy}. With a reported prevalence of 1 in 200,000 live births, CCHS, formerly known as Ondine's curse, is a rare life-threatening disorder characterized by a failure of the automatic control of breathing, caused by mutations in a gene classified as PHOX2B. CCHS manifests itself, in the neonatal period, with episodes of cyanosis or apnoea, especially during quiet sleep. The reported mortality rates range from 8% to 38% of newborn with genetically confirmed CCHS. Nowadays, CCHS is considered a disorder of autonomic regulation, with related risk of sudden infant death syndrome (SIDS). Currently, the standard method of diagnosis, for both diseases, is based on polysomnography, a set of sensors such as ElectroEncephaloGram (EEG) sensors, ElectroMyoGraphy (EMG) sensors, ElectroCardioGraphy (ECG) sensors, elastic belt sensors, pulse-oximeter and nasal flow-meters. This monitoring system is very expensive, time-consuming, moderately invasive and requires particularly skilled medical personnel, not always available in a Neonatal Intensive Care Unit (NICU). Therefore, automatic, real-time and non-invasive monitoring equipments able to reliably recognize these diseases would be of significant value in the NICU. A very appealing monitoring tool to automatically detect neonatal seizures or breathing disorders may be based on acquiring, through a network of sensors, e.g., a set of video cameras, the movements of the newborn's body (e.g., limbs, chest) and properly processing the relevant signals. An automatic multi-sensor system could be used to permanently monitor every patient in the NICU or specific patients at home. Furthermore, a wire-free technique may be more user-friendly and highly desirable when used with infants, in particular with newborns. This work has focused on a reliable method to estimate the periodicity in pathological movements based on the use of the Maximum Likelihood (ML) criterion. In particular, average differential luminance signals from multiple Red, Green and Blue (RGB) cameras or depth-sensor devices are extracted and the presence or absence of a significant periodicity is analysed in order to detect possible pathological conditions. The efficacy of this monitoring system has been measured on the basis of video recordings provided by the Department of Neurosciences of the University of Parma. Concerning clonic seizures, a kinematic analysis was performed to establish a relationship between neonatal seizures and human inborn pattern of quadrupedal locomotion. Moreover, we have decided to realize simulators able to replicate the symptomatic movements characteristic of the diseases under consideration. The reasons is, essentially, the opportunity to have, at any time, a 'subject' on which to test the continuously evolving detection algorithms. Finally, we have developed a smartphone App, called 'Smartphone based contactless epilepsy detector' (SmartCED), able to detect neonatal clonic seizures and warn the user about the occurrence in real-time.

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Background: Remote, non-invasive and objective tests that can be used to support expert diagnosis for Parkinson's disease (PD) are lacking. Methods: Participants underwent baseline in-clinic assessments, including the Unified Parkinson's Disease Rating Scale (UPDRS), and were provided smartphones with an Android operating system that contained a smartphone application that assessed voice, posture, gait, finger tapping, and response time. Participants then took the smart phones home to perform the five tasks four times a day for a month. Once a week participants had a remote (telemedicine) visit with a Parkinson disease specialist in which a modified (excluding assessments of rigidity and balance) UPDRS performed. Using statistical analyses of the five tasks recorded using the smartphone from 10 individuals with PD and 10 controls, we sought to: (1) discriminate whether the participant had PD and (2) predict the modified motor portion of the UPDRS. Results: Twenty participants performed an average of 2.7 tests per day (68.9% adherence) for the study duration (average of 34.4 days) in a home and community setting. The analyses of the five tasks differed between those with Parkinson disease and those without. In discriminating participants with PD from controls, the mean sensitivity was 96.2% (SD 2%) and mean specificity was 96.9% (SD 1.9%). The mean error in predicting the modified motor component of the UPDRS (range 11-34) was 1.26 UPDRS points (SD 0.16). Conclusion: Measuring PD symptoms via a smartphone is feasible and has potential value as a diagnostic support tool.