Road network and GPS tracking with data processing and quality assessment

GPS technology has been embedded into portable, low-cost electronic devices nowadays to track the movements of mobile objects. This implication has greatly impacted the transportation field by creating a novel and rich source of traffic data on the road network. Although the promise offered by GPS devices to overcome problems like underreporting, respondent fatigue, inaccuracies and other human errors in data collection is significant; the technology is still relatively new that it raises many issues for potential users. These issues tend to revolve around the following areas: reliability, data processing and the related application. This thesis aims to study the GPS tracking form the methodological, technical and practical aspects. It first evaluates the reliability of GPS based traffic data based on data from an experiment containing three different traffic modes (car, bike and bus) traveling along the road network. It then outline the general procedure for processing GPS tracking data and discuss related issues that are uncovered by using real-world GPS tracking data of 316 cars. Thirdly, it investigates the influence of road network density in finding optimal location for enhancing travel efficiency and decreasing travel cost. The results show that the geographical positioning is reliable. Velocity is slightly underestimated, whereas altitude measurements are unreliable.Post processing techniques with auxiliary information is found necessary and important when solving the inaccuracy of GPS data. The densities of the road network influence the finding of optimal locations. The influence will stabilize at a certain level and do not deteriorate when the node density is higher.

Fingerprint Segmentation

In this thesis, a new algorithm has been proposed to segment the foreground of the fingerprint from the image under consideration. The algorithm uses three features, mean, variance and coherence. Based on these features, a rule system is built to help the algorithm to efficiently segment the image. In addition, the proposed algorithm combine split and merge with modified Otsu. Both enhancements techniques such as Gaussian filter and histogram equalization are applied to enhance and improve the quality of the image. Finally, a post processing technique is implemented to counter the undesirable effect in the segmented image. Fingerprint recognition system is one of the oldest recognition systems in biometrics techniques. Everyone have a unique and unchangeable fingerprint. Based on this uniqueness and distinctness, fingerprint identification has been used in many applications for a long period. A fingerprint image is a pattern which consists of two regions, foreground and background. The foreground contains all important information needed in the automatic fingerprint recognition systems. However, the background is a noisy region that contributes to the extraction of false minutiae in the system. To avoid the extraction of false minutiae, there are many steps which should be followed such as preprocessing and enhancement. One of these steps is the transformation of the fingerprint image from gray-scale image to black and white image. This transformation is called segmentation or binarization. The aim for fingerprint segmentation is to separate the foreground from the background. Due to the nature of fingerprint image, the segmentation becomes an important and challenging task. The proposed algorithm is applied on FVC2000 database. Manual examinations from human experts show that the proposed algorithm provides an efficient segmentation results. These improved results are demonstrating in diverse experiments.

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.

Kvinnors erfarenhet av postoperativ smärta och smärtlindring efter kejsarsnitt

Background: Many women experience high levels of pain after caesarean birth. Adequate postoperative pain treatment is important for the mother to be able to breastfeed, take care of the infant and experience a positive birth. Objective: The overall aim is to study women’s experience of postoperative pain and pain relief after caesarean birth. Method: A quantitative retrospective survey. Data were collected thru a questionnaire from Centralsjukhuset in Karlstad and Falu Larsarett in Sweden. Ninety-eight women participated in the study. Data was analysed with descriptive and comparative statistic. Result: Eighty percent of the women rated the pain with VAS = 4 during the first 24 hours post operative. Those who had to undergo acute caesarean birth rated significant higher levels of pain compared with those who had undergone planned caesarean birth. Despite high level of pain the women were satisfied with the pain relief they received. Both the ability to breastfeed and take care of the infant were affected by pain the first 24 hours post operative. Those who had undergone emergency caesarean birth experienced in greater extend the birth in a negative way. Conclusion: Postoperative pain affects the women’s ability to breastfeed and takes care of here infant. Adequate pain management is therefore important. The women who had to undergo emergency caesarean birth have a more negative birth experience. Midwifes have an important role to inform and support the women in processing here experience.

Barriers and facilitators in the provision of post-abortion care at district level in central Uganda : a qualitative study focusing on task sharing between physicians and midwives

Background: Abortion is restricted in Uganda, and poor access to contraceptive methods result in unwanted pregnancies. This leaves women no other choice than unsafe abortion, thus placing a great burden on the Ugandan health system and making unsafe abortion one of the major contributors to maternal mortality and morbidity in Uganda. The existing sexual and reproductive health policy in Uganda supports the sharing of tasks in post-abortion care. This task sharing is taking place as a pragmatic response to the increased workload. This study aims to explore physicians' and midwives' perception of post-abortion care with regard to professional competences, methods, contraceptive counselling and task shifting/sharing in post-abortion care. Methods: In-depth interviews (n = 27) with health care providers of post-abortion care were conducted in seven health facilities in the Central Region of Uganda. The data were organized using thematic analysis with an inductive approach. Results: Post-abortion care was perceived as necessary, albeit controversial and sometimes difficult to provide. Together with poor conditions post-abortion care provoked frustration especially among midwives. Task sharing was generally taking place and midwives were identified as the main providers, although they would rarely have the proper training in post-abortion care. Additionally, midwives were sometimes forced to provide services outside their defined task area, due to the absence of doctors. Different uterine evacuation skills were recognized although few providers knew of misoprostol as a method for post-abortion care. An overall need for further training in post-abortion care was identified. Conclusions: Task sharing is taking place, but providers lack the relevant skills for the provision of quality care. For post-abortion care to improve, task sharing needs to be scaled up and in-service training for both doctors and midwives needs to be provided. Post-abortion care should further be included in the educational curricula of nurses and midwives. Scaled-up task sharing in post-abortion care, along with misoprostol use for uterine evacuation would provide a systematic approach to improving the quality of care and accessibility of services, with the aim of reducing abortion-related mortality and morbidity in Uganda.