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  • Public defence: 2024-03-28 13:00 room Clas Ohlson
    Saeed, Nausheen
    Dalarna University, School of Information and Engineering, Microdata Analysis.
    Objective Assessment of Loose Gravel Condition using Machine Learning with Audio-visual Observation2024Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    A well-maintained road network is essential for sustainable economic development, providing vital transportation routes for goods and services while connecting communities. Sweden's public road network includes a significant portion of gravel roads, particularly cost-effective for less populated areas with lower traffic volumes. However, gravel roads deteriorate quickly, leading to accidents, environmental pollution, and vehicle tire wear when not adequately maintained. The Swedish Road Administration Authority (Trafikverket) assesses gravel road conditions using subjective methods, analysing images taken during snow-free periods. Due to cost constraints, this labour-intensive process is prone to errors and lacks advanced techniques like road profilometers.

    This thesis explores the field of assessing gravel road conditions. It commences with a comprehensive review of manual gravel road assessment methods employed globally and existing data-driven smart methods. Subsequently, it harnesses machine hearing and machine vision techniques, primarily focusing on enhancing road condition classification by integrating sound and image data.

    The research examines sound data collected from gravel roads, exploring machine learning algorithms for loose gravel conditions classification with potential road maintenance and monitoring implications. Another crucial aspect involves applying machine vision to categorise image data from gravel roads. The study introduces an innovative approach using publicly available resources like Google Street View for image data collection, demonstrating machine vision's adaptability in assessing road conditions.

    The research also compares machine learning methods with manual human classification, specifically regarding sound data. Automated approaches consistently outperform manual methods, providing more reliable results. Furthermore, the thesis investigates combining audio and image data to classify road conditions, particularly loose gravel scenarios. Early feature fusion using pre-trained models significantly improves classifier accuracy.

    The research proposes using cost-effective devices like mobile phones with AI applications attached to car windshields to collect audio and visual data on gravel road conditions. This approach can provide more accurate and efficient data collection, resulting in real-time mapping of road conditions over considerable distances. Such information can benefit drivers, travellers, and road maintenance agencies by identifying problematic areas with loose gravel, enabling targeted and efficient maintenance efforts, and minimising disruptions to traffic flow during maintenance operations.

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  • Public defence: 2024-04-19 09:00 lecture hall 105 (Fö 5)
    Falk Johansson, Marcus
    Dalarna University, School of Health and Welfare, Care Sciences. Dalarna University, School of Health and Welfare, Social Work.
    For better and for worse, till death do us part: Support needs of persons caring for a co-habitant spouse or partner with dementia2024Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Background: Caring for a partner with dementia is typically stressful and challenging. Such carers can become overwhelmed by their responsibilities, neglecting their personal needs as well as their need for support as a carer. Receipt of support is low among spouse carers, while the support received may not be appropriate for their needs. More research is required to develop effective support for this important group of carers. 

    Overall aim: To explore the life- and caring situation of spouses caring for a partner with dementia and to increase the understanding of their needs and experiences of support.  

    Methods: This thesis consists of four papers (I-IV): I, Analysis of data on informal carers of persons with dementia (n=330) from a cross-sectional survey of a stratified random probability sample of adults in Sweden (N=30 009); II and III, a cross-sectional survey of a convenience sample of people aged 65 years or older caring for a partner with dementia (N=175). Hierarchal regression models explored positive and negative aspects of caring (II), and principal component analysis examined carers’ perceptions of support (III); IV, a thematic analysis of semi-structured telephone interviews with 24 spouse carers, exploring their caring experiences. 

    Results: Compared to other carers, spouses of persons with dementia received less support from family or local authorities, while experiencing more negative impact from caring (I). Negative impact from, and positive value of, caring among spouses, were associated with different aspects of their situation (II). Support was perceived as important, yet spouses may not perceive support to themselves as more important than support to their partner (III). Spouse carers experienced a loss of self and felt confined in their situation, finding it hard to distinguish between their needs and those of their partner (IV). 

    Conclusion: Compared to other carers, spouses are more exposed to the negative aspects of caring, while being less supported. Support to spouse carers should focus on strengthening the positive aspects of caring to mitigate the negative aspects. As a spouse’s needs are conditioned by their partner’s, support should focus on spouses’ personal needs and their partners’ care needs.

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