Fuel consumption of transportation vehicles account for nearly 27% of CO2 emissions from fossil fuel. The environmental impact of of transportation can be lowered with effective ECOfriendly driving. As modern vehicles are equipped with the on-board diagnostics (OBD) system that provides vehicle’s self-diagnostic data which is accessible to from the OBD-II socket in vehicles. The aim of this thesis is to design and implement such a monitor system that consists of an OBD-II reader (containing a Bluetooth communication module) to fetch data from the OBD-II socket and then, via a smartphone, send the data to a cloud server for analysis and visualisation. For this purpose, an ELM327 Bluetooth module (an off-the-shelf OBD-II dongle) was bought, an application has been created for an Android smartphone, and a cloud server has been created using ThinkSpeak (an online solution for cloud data collection and visualisation).   As a result, such monitor system has been implemented at low cost, and then tested and evaluated together with a Toyota Prius driving a round-trip between the Swedish cities of Uppsala and Arlanda. The test results have shown that the system is successful in gathering data from the OBD-II socket and then sending visualizing data. It needs, however, further work to increase the user-friendliness and accuracy of the gathered information from the vehicle. / OBD-II är en internationell standard i fordon som är obligatorisk i moderna bilar möjliggör att med hjälp av hårdvara läsa av information från bilen under färd. Det medför att man kan överföra data så som hastighet, bränsleförbrukning och motortemperaturfrån bilen till en annan tjänst där det presenteras för föraren. Metoden går ut på att transmittera informationen som läses av sensorer i bilen till en internettupkopplad mobiltelefon som för över informationen till en molntjänsst. Datan från bilen ska sedankompletteras med positionsinformation från mobilenhetens GPS enhet. Detta medför att skapandet av en applikation som kan uppfylla målen blir är en central del av projektet. ELM327 är en hårdvarumodul som är designad för att kopplas till ett OBD-II uttag. Modulen kan då läsa av data från CAN bussen i bilen och sedan konverterainformationen till en Bluetooth enhet. En mobiltelefon kan då kommunicera med Bluetooth enheten genom att skapa en Android applikation i utveklarverktyget Androidstudio. Applikationen för sedan över datan till molntjänsten ThingSpeak, som är designat för IoT project. ThingSpeak har en implementering av MatLab, en programmerings plattform för ingenjörer, som låter användaren analysera och visualiseradata i molntjänsten. En begränsning som uppstår är hur mycket pengar som investeras i projetet vilket har inflytande på hur ofta datan kan uppdateras i molntjänsten, vilket påverkar nogrannheten i resultatet. I tesen diskuteras även begränsningarna av systemet och olika utvecklingsmöjligheter av projektet.

Acute congestive heart failure is a leading cause of morbidity and mortality. Patients often undergo repeated hospitalizations with an annual cost in excess of $32B dollars. Early detection of impending acute congestion allows for pharmaceutical interdiction that prevents hospitalization, improves outcomes, and reduces healthcare spending. A subcutaneous implantable monitoring device that detects impending acute congestive heart failure by using heart sounds may provide a valuable tool that can be used to titrate heart failure medications to prevent acute heart failure requiring hospitalization. The device may be used to measure changes in the intervals between the R-wave and S1 and S2 heart sounds, and to detect evolving S3 and S4 heart sounds consistent with volume overload. The amplitudes of S1 and S3 heart sounds change as heart failure patients' symptoms worsen. Designing a non-invasive, external device, capable of recording heart sounds and EKGs in patients undergoing pharmaceutical regression of acute congestive heart failure in a hospital setting may give important insight into the nature of heart sound and EKG changes that occur in patients during progression of acute heart failure while they lead their day-to-day lives. This thesis details the design of a portable, non-invasive device, worn externally on the left-pectoral muscle, capable of continuously recording human EKG signals and heart sounds (through custom MEMS accelerometer technology) over a period of two days. Hardware testing for the scope of this thesis exclusively involved healthy volunteers.

Lower Back Disorders account for 16-19% of work related claims and 33-41% of dollars paid in workers? compensation (as cited in Marras, 1999), with impacts to society approaching $100 billion dollars annually (as cited in Marras et al., 1999). Dr. William Marras engineered a device to track trunk kinematics in order to develop a Job Risk Classification Model for predicting high-risk group probability of lower back injury. The device has been validated, but other technologies such as 3-D motion capture can potentially gather the same data. This study examined the use of motion capture to apply two- and three-plane lifting tasks to the Marras model and compare results with commonly used assessment techniques. Regardless of the fact that the Marras model results were drastically different from NIOSH and RULA, motion capture was able to gather all necessary data for running the models and has a promising future in ergonomic assessments.

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