Monitoring System Resources During Automated Testing in Symbian Operating System

Tässä diplomityössä esitellään ohjelmistotestauksen ja verifioinnin yleisiä periaatteita sekä käsitellään tarkemmin älypuhelinohjelmistojen verifiointia. Työssä esitellään myös älypuhelimissa käytettävä Symbian-käyttöjärjestelmä. Työn käytännön osuudessa suunniteltiin ja toteutettiin Symbian-käyttöjärjestelmässä toimiva palvelin, joka tarkkailee ja tallentaa järjestelmäresurssien käyttöä. Verifiointi on tärkeä ja kuluja aiheuttava tehtävä älypuhelinohjelmistojen kehityssyklissä. Kuluja voidaan vähentää automatisoimalla osa verifiointiprosessista. Toteutettu palvelin automatisoijärjestelmäresurssien tarkkailun tallentamalla tietoja niistä tiedostoon testien ajon aikana. Kun testit ajetaan uudestaan, uusia tuloksia vertaillaan lähdetallenteeseen. Jos tulokset eivät ole käyttäjän asettamien virherajojen sisällä, siitä ilmoitetaan käyttäjälle. Virherajojen ja lähdetallenteen määrittäminen saattaa osoittautua vaikeaksi. Kuitenkin, jos ne määritetään sopivasti, palvelin tuottaa hyödyllistä tietoa poikkeamista järjestelmäresurssien kulutuksessa testaajille.

Enhancing physical activity coaching through personalized motivational strategies and self-adaptive goal-setting: development of self-adaptive processes in a monitoring and coaching smartphone application

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Classificação de atividades físicas através do uso do acelerómetro do smartphone

Dissertação de natureza científica realizada para a obtenção do grau de Mestre em Engenharia de redes de comunicação e Multimédia

Sistema de localização de veículos para smartphone android

Atualmente os sistemas Automatic Vehicle Location (AVL) fazem parte do dia-a-dia de muitas empresas. Esta tecnologia tem evoluído significativamente ao longo da última década, tornando-se mais acessível e fácil de utilizar. Este trabalho consiste no desenvolvimento de um sistema de localização de veículos para smartphone Android. Para tal, foram desenvolvidas duas aplicações: uma aplicação de localização para smarphone Android e uma aplicação WEB de monitorização. A aplicação de localização permite a recolha de dados de localização GPS e estabelecer uma rede piconet Bluetooth, admitindo assim a comunicação simultânea com a unidade de controlo de um veículo (ECU) através de um adaptador OBDII/Bluetooth e com até sete sensores/dispositivos Bluetooth que podem ser instalados no veículo. Os dados recolhidos pela aplicação Android são enviados periodicamente (intervalo de tempo definido pelo utilizador) para um servidor Web No que diz respeito à aplicação WEB desenvolvida, esta permite a um gestor de frota efetuar a monitorização dos veículos em circulação/registados no sistema, podendo visualizar a posição geográfica dos mesmos num mapa interativo (Google Maps), dados do veículo (OBDII) e sensores/dispositivos Bluetooth para cada localização enviada pela aplicação Android. O sistema desenvolvido funciona tal como esperado. A aplicação Android foi testada inúmeras vezes e a diferentes velocidades do veículo, podendo inclusive funcionar em dois modos distintos: data logger e data pusher, consoante o estado da ligação à Internet do smartphone. Os sistemas de localização baseados em smartphone possuem vantagens relativamente aos sistemas convencionais, nomeadamente a portabilidade, facilidade de instalação e baixo custo.

GoCARB: Smartphone-App zur Kohlenhydratberechnung bei Typ-1-Diabetes

Smartphone-App zur Kohlenhydratberechnung Neue Technologien wie Blutzuckersensoren und moderne Insulinpumpen prägten die Therapie des Typ-1-Diabetes (T1D) in den letzten Jahren in wesentlichem Ausmaß. Smartphones sind aufgrund ihrer rasanten technischen Entwicklung eine weitere Plattform für Applikationen zur Therapieunterstützung bei T1D. GoCARB Hierbei handelt es sich um ein zur Kohlenhydratberechnung entwickeltes System für Personen mit T1D. Die Basis für Endanwender stellt ein Smartphone mit Kamera dar. Zur Berechnung werden 2 mit dem Smartphone aus verschiedenen Winkeln aufgenommene Fotografien einer auf einem Teller angerichteten Mahlzeit benötigt. Zusätzlich ist eine neben dem Teller platzierte Referenzkarte erforderlich. Die Grundlage für die Kohlenhydratberechnung ist ein Computer-Vision-gestütztes Programm, das die Mahlzeiten aufgrund ihrer Farbe und Textur erkennt. Das Volumen der Mahlzeit wird mit Hilfe eines dreidimensional errechneten Modells bestimmt. Durch das Erkennen der Art der Mahlzeiten sowie deren Volumen kann GoCARB den Kohlenhydratanteil unter Einbeziehung von Nährwerttabellen berechnen. Für die Entwicklung des Systems wurde eine Bilddatenbank von mehr als 5000 Mahlzeiten erstellt und genutzt. Resümee Das GoCARB-System befindet sich aktuell in klinischer Evaluierung und ist noch nicht für Patienten verfügbar.

Development of a human movement monitoring system based on wearable devices

It is essential to remotely and continuously monitor the movements of individuals in many social areas, for example, taking care of aging people, physical therapy, athletic training etc. Many methods have been used, such as video record, motion analysis or sensor-based methods. Due to the limitations in remote communication, power consumption, portability and so on, most of them are not able to fulfill the requirements. The development of wearable technology and cloud computing provides a new efficient way to achieve this goal. This paper presents an intelligent human movement monitoring system based on a smartwatch, an Android smartphone and a distributed data management engine. This system includes advantages of wide adaptability, remote and long-term monitoring capacity, high portability and flexibility. The structure of the system and its principle are introduced. Four experiments are designed to prove the feasibility of the system. The results of the experiments demonstrate the system is able to detect different actions of individuals with adequate accuracy.

Human activity recognition by inertial signals obtained from a smartphone

Human Activity Recognition (HAR) is an emerging research field with the aim to identify the actions carried out by a person given a set of observations and the surrounding environment. The wide growth in this research field inside the scientific community is mainly explained by the high number of applications that are arising in the last years. A great part of the most promising applications are related to the healthcare field, where it is possible to track the mobility of patients with motor dysfunction as also the physical activity in patients with cardiovascular risk. Until a few years ago, by using distinct kind of sensors, a patient follow-up was possible. However, far from being a long-term solution and with the smartphone irruption, that monitoring can be achieved in a non-invasive way by using the embedded smartphone’s sensors. For these reasons this Final Degree Project arises with the main target to evaluate new feature extraction techniques in order to carry out an activity and user recognition, and also an activity segmentation. The recognition is done thanks to the inertial signals integration obtained by two widespread sensors in the greater part of smartphones: accelerometer and gyroscope. In particular, six different activities are evaluated walking, walking-upstairs, walking-downstairs, sitting, standing and lying. Furthermore, a segmentation task is carried out taking into account the activities performed by thirty users. This can be done by using Hidden Markov Models and also a set of tools tested satisfactory in speech recognition: HTK (Hidden Markov Model Toolkit).

Monitoring Infants By Automatic Video Processing

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.

Detecting and monitoring the symptoms of Parkinson's disease using smartphones:a pilot study

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.

Implementation of a drive-by monitoring system for transport infrastructure utilising GNSS

Ageing and deterioration of infrastructure is a challenge facing transport authorities. In particular, there is a need for increased bridge monitoring in order to provide adequate maintenance, prioritise allocation of funds and guarantee acceptable levels of transport safety. Existing bridge structural health monitoring (SHM) techniques typically involve direct instrumentation of the bridge with sensors and equipment for the measurement of properties such as frequencies of vibration. These techniques are important as they can indicate the deterioration of the bridge condition. However, they can be labour intensive and expensive due to the requirement for on-site installations. In recent years, alternative low-cost indirect vibrationbased SHM approaches have been proposed which utilise the dynamic response of a vehicle to carry out “drive-by” pavement and/or bridge monitoring. The vehicle is fitted with sensors on its axles thus reducing the need for on-site installations. This paper investigates the use of low-cost sensors incorporating global navigation satellite systems (GNSS) for implementation of the drive-by system in practice, via field trials with an instrumented vehicle. The potential of smartphone technology to be harnessed for drive by monitoring is established, while smartphone GNSS tracking applications are found to compare favourably in terms of accuracy, cost and ease of use to professional GNSS devices.

Integrating a mobile accessible electronic system into dockside monitoring: How can small-scale fisheries data collection programs transition from paper-based to digital data collection?

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

User interaction monitoring and analysis framework

An overview is given of a user interaction monitoring and analysis framework called BaranC. Monitoring and analysing human-digital interaction is an essential part of developing a user model as the basis for investigating user experience. The primary human-digital interaction, such as on a laptop or smartphone, is best understood and modelled in the wider context of the user and their environment. The BaranC framework provides monitoring and analysis capabilities that not only records all user interaction with a digital device (e.g. smartphone), but also collects all available context data (such as from sensors in the digital device itself, a fitness band or a smart appliances). The data collected by BaranC is recorded as a User Digital Imprint (UDI) which is, in effect, the user model and provides the basis for data analysis. BaranC provides functionality that is useful for user experience studies, user interface design evaluation, and providing user assistance services. An important concern for personal data is privacy, and the framework gives the user full control over the monitoring, storing and sharing of their data.

Monitoramento de parâmetros imunológicos em atletas adolescentes de basquetebol durante e após a temporada esportiva= : Monitoring of immunological parameters in adolescent basketball athletes during and after a sports season

Universidade Estadual de Campinas . Faculdade de Educação Física

A feasibility study of cell phone and landline phone interviews for monitoring of risk and protection factors for chronic diseases in Brazil

The study objective was to evaluate the feasibility of interviews by cell phone as a complement to interviews by landline to estimate risk and protection factors for chronic non-communicable diseases. Adult cell phone users were evaluated by random digit dialing. Questions asked were: age, sex, education, race, marital status, ownership of landline and cell phones, health condition, weight and height, medical diagnosis of hypertension and diabetes, physical activity, diet, binge drinking and smoking. The estimates were calculated using post-stratification weights. The cell phone interview system showed a reduced capacity to reach elderly and low educated populations. The estimates of the risk and protection factors for chronic non-communicable diseases in cell phone interviews were equal to the estimates obtained by landline phone. Eligibility, success and refusal rates using the cell phone system were lower than those of the landline system, but loss and cost were much higher, suggesting it is unsatisfactory as a complementary method in such a context.

Monitoring Millennium Development Goals in Brazilian municipalities: challenges to be met in facing up to iniquities

The Healthy Cities and Agenda 21 programs improve living and health conditions and affect social and economic determinants of health. The Millennium Development Goals (MDG) indicators can be used to assess the impact of social agendas. A data search was carried out for the period 1997 to 2006 to obtain 48 indicators proposed by the United Nations and a further 74 proposed by the technical group for the MDGin Brazil. There is a scarcity of studies concerned with assessing the MDG at the municipal level. Data from Brazilian health information systems are not always consistent or accurate for municipalities. The lack of availability and reliable data led to the substitution of some indicators. The information systems did not always provide annual data; national household surveys could not be disaggregated at the municipal level and there were also modifications on conceptual definitions over time. As a result, the project created an alternative list with 29 indicators. MDG monitoring at the local community can be important to measure the performance of actions toward improvements in quality of life and social iniquities.