Meningsfull sysselsättning i två typer av dagliga verksamheter : -en jämförelse mellan en brukarstyrd och en kommunalt driven daglig verksamhet för människor med psykiska funktionshinder

Daglig sysselsättning för människor med ett psykiskt funktionshinder drivs av olika huvudmän såsom kommuner, intresseorganisationer och kooperativ. Psykiatrireformens syfte är att förbättra livsvillkoren för den studera målgruppen samt att öka delaktigheten i samhällslivet . Vi som genomför denna studie har tidigare varit yrkesverksamma inom kommunal socialpsykiatri. Genom dessa erfarenheter har vi också erhållit ett intresse av människor med ett långvarigt psykiskt funktionshinder. Vår förförståelse bygger på tidigare erfarenheter i yrket som säger oss att det finns ett begränsat utbud till aktivitet och sysselsättning för den studerade gruppen. I vårt arbete har strävan varit att ta reda på vad det är som gör att människor med ett psykiskt funktionshinder känner meningsfullhet i sin dagliga sysselsättning. Vår ambition har också varit att jämföra mellan en brukarstyrd och en kommunalt driven verksamhet. Detta för att se om det finns likheter och/eller skillnader i vad människor anser meningsfullt i den dagliga sysselsättningen.

First-principle data-driven models for assessment of motor disorders in Parkinson’s disease

Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic.  The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait. The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.

Evaluation of a thermally driven heat pump for solar heating and cooling applications

Exploiting solar energy technology for both heating and cooling purposes has the potential of meeting an appreciable portion of the energy demand in buildings throughout the year. By developing an integrated, multi-purpose solar energy system, that can operate all twelve months of the year, a high utilisation factor can be achieved which translates to more economical systems. However, there are still some techno-economic barriers to the general commercialisation and market penetration of such technologies. These are associated with high system and installation costs, significant system complexity, and lack of knowledge of system implementation and expected performance. A sorption heat pump module that can be integrated directly into a solar thermal collector has thus been developed in order to tackle the aforementioned market barriers. This has been designed for the development of cost-effective pre-engineered solar energy system kits that can provide both heating and cooling. This thesis summarises the characterisation studies of the operation of individual sorption modules, sorption module integrated solar collectors and a full solar heating and cooling system employing sorption module integrated collectors. Key performance indicators for the individual sorption modules showed cooling delivery for 6 hours at an average power of 40 W and a temperature lift of 21°C. Upon integration of the sorption modules into a solar collector, measured solar radiation energy to cooling energy conversion efficiencies (solar cooling COP) were between 0.10 and 0.25 with average cooling powers between 90 and 200 W/m2 collector aperture area. Further investigations of the sorption module integrated collectors implementation in a full solar heating and cooling system yielded electrical cooling COP ranging from 1.7 to 12.6 with an average of 10.6 for the test period. Additionally, simulations were performed to determine system energy and cost saving potential for various system sizes over a full year of operation for a 140 m2 single-family dwelling located in Madrid, Spain. Simulations yielded an annual solar fraction of 42% and potential cost savings of €386 per annum for a solar heating and cooling installation employing 20m2 of sorption integrated collectors.

A data driven approach for automating vehicle activated signs

Vehicle activated signs (VAS) display a warning message when drivers exceed a particular threshold. VAS are often installed on local roads to display a warning message depending on the speed of the approaching vehicles. VAS are usually powered by electricity; however, battery and solar powered VAS are also commonplace. This thesis investigated devel-opment of an automatic trigger speed of vehicle activated signs in order to influence driver behaviour, the effect of which has been measured in terms of reduced mean speed and low standard deviation. A comprehen-sive understanding of the effectiveness of the trigger speed of the VAS on driver behaviour was established by systematically collecting data. Specif-ically, data on time of day, speed, length and direction of the vehicle have been collected for the purpose, using Doppler radar installed at the road. A data driven calibration method for the radar used in the experiment has also been developed and evaluated. Results indicate that trigger speed of the VAS had variable effect on driv-ers’ speed at different sites and at different times of the day. It is evident that the optimal trigger speed should be set near the 85th percentile speed, to be able to lower the standard deviation. In the case of battery and solar powered VAS, trigger speeds between the 50th and 85th per-centile offered the best compromise between safety and power consump-tion. Results also indicate that different classes of vehicles report differ-ences in mean speed and standard deviation; on a highway, the mean speed of cars differs slightly from the mean speed of trucks, whereas a significant difference was observed between the classes of vehicles on lo-cal roads. A differential trigger speed was therefore investigated for the sake of completion. A data driven approach using Random forest was found to be appropriate in predicting trigger speeds respective to types of vehicles and traffic conditions. The fact that the predicted trigger speed was found to be consistently around the 85th percentile speed justifies the choice of the automatic model.