A review of Parkinson’s disease cardinal and dyskinetic motor symptoms assessment methods using sensor systems

This paper is reviewing objective assessments of Parkinson’s disease(PD) motor symptoms, cardinal, and dyskinesia, using sensor systems. It surveys the manifestation of PD symptoms, sensors that were used for their detection, types of signals (measures) as well as their signal processing (data analysis) methods. A summary of this review’s finding is represented in a table including devices (sensors), measures and methods that were used in each reviewed motor symptom assessment study. In the gathered studies among sensors, accelerometers and touch screen devices are the most widely used to detect PD symptoms and among symptoms, bradykinesia and tremor were found to be mostly evaluated. In general, machine learning methods are potentially promising for this. PD is a complex disease that requires continuous monitoring and multidimensional symptom analysis. Combining existing technologies to develop new sensor platforms may assist in assessing the overall symptom profile more accurately to develop useful tools towards supporting better treatment process.

Adolescent boys’ health : managing emotions, masculinities and subjective social status

The health of adolescent boys is complex and surprisingly little is known about how adolescent boys perceive, conceptualise and experience their health. Thus, the overall aim of this thesis was to explore adolescent boys’ perceptions and experiences of health, emotions, masculinity and subjective social status (SSS). This thesis consists of a qualitative, a quantitative and a mixed methods study. The qualitative study aimed to explore how adolescent boys understand the concept of health and what they find important for its achievement. Furthermore, the adolescent boys’ views of masculinity, emotion management and their potential effects on wellbeing were explored. For this purpose, individual interviews were conducted with 33 adolescent boys aged 16-17 years. The quantitative study aimed to investigate the associations between pride, shame and health in adolescence. Data were collected through a cross-sectional postal survey with 705 adolescents. The purpose of the mixed methods study was to investigate associations between SSS in school, socioeconomic status (SES) and self-rated health (SRH), and to explore the concept of SSS in school. Cross-sectional data were combined with interview data in which the meaning of SSS was further explored. Individual interviews with 35 adolescents aged 17-18 years were conducted. In the qualitative study, data were analysed using Grounded Theory. In the quantitative study, statistical analyses (e.g., chi-square test and uni- and multivariable logistic regression analyses) were performed. In the mixed method study, a combination of statistical analyses and thematic network analysis was applied. The results showed that there was a complexity in how the adolescent boys viewed, experienced, dealt with and valued health. On a conceptual level, they perceived health as holistic but when dealing with difficult emotions, they were prone to separate the body from the mind. Thus, the adolescent boys experienced a difference between health as a concept and health as an experience (paper I). Concerning emotional orientation in masculinity, two main categories of masculine conceptions were identified: a gender-normative masculinity and a non-gender-normative masculinity (paper II). Gender-normative masculinity comprised two seemingly opposite emotional masculinity orientations, one towards toughness and the other towards sensitivity, both of which were highly influenced by contextual and situational group norms and demands, despite that their expressions are in contrast to each other. Non-gender-normative masculinity included an orientation towards sincerity, emphasising the personal values of the boys. Emotions were expressed more independently of peer group norms. The findings suggest that different masculinities and the expression of emotions are intricately intertwined and that managing emotions is vital for wellbeing. The present findings also showed that both shame and pride were significantly associated with SRH, and furthermore, that there seems to be a protective effect of experiencing pride for health (paper III). The results also demonstrated that SSS is strongly related to SRH, and high SRH is related to high SSS, and further that the positioning was done in a gendered space (paper IV). Results from all studies suggest that the emotional and relational aspects, as well as perceived SSS, were strongly related to SRH. Positive emotions, trustful relationships and having a sense of belonging were important factors for health and pride was an important emotion protecting health. Physical health, on the other hand, had a more subordinated value, but the body was experienced as an important tool to achieve health. Even though health was mainly perceived in a holistic manner by the boys, there were boys who were prone to dichotomise the health experience into a mind-body dualism when having to deal with difficult emotions. In conclusion, this thesis demonstrates that young, masculine health is largely experienced through emotions and relationships between individuals and their contexts affected by gendered practices. Health is to feel and function well in mind and body and to have trusting relationships. The results support theories on health as a social construction of interconnected processes. Having confidence in self-esteem, access to trustful relationships and the courage to resist traditional masculine norms while still reinforcing and maintaining social status are all conducive to good health. Researchers as well as professionals need to consider the complexity of adolescent boys’ health in which norms, values, relationships and gender form its social determinants. Those working with young boys should encourage them to integrate physical, social and emotional aspects of health into an interconnected and holistic experience.

MACHINE VISION FOR AUTOMATICVISUAL INSPECTION OF WOODENRAILWAY SLEEPERS USING UNSUPERVISED NEURAL NETWORKS

The motivation for this thesis work is the need for improving reliability of equipment and quality of service to railway passengers as well as a requirement for cost-effective and efficient condition maintenance management for rail transportation. This thesis work develops a fusion of various machine vision analysis methods to achieve high performance in automation of wooden rail track inspection.The condition monitoring in rail transport is done manually by a human operator where people rely on inference systems and assumptions to develop conclusions. The use of conditional monitoring allows maintenance to be scheduled, or other actions to be taken to avoid the consequences of failure, before the failure occurs. Manual or automated condition monitoring of materials in fields of public transportation like railway, aerial navigation, traffic safety, etc, where safety is of prior importance needs non-destructive testing (NDT).In general, wooden railway sleeper inspection is done manually by a human operator, by moving along the rail sleeper and gathering information by visual and sound analysis for examining the presence of cracks. Human inspectors working on lines visually inspect wooden rails to judge the quality of rail sleeper. In this project work the machine vision system is developed based on the manual visual analysis system, which uses digital cameras and image processing software to perform similar manual inspections. As the manual inspection requires much effort and is expected to be error prone sometimes and also appears difficult to discriminate even for a human operator by the frequent changes in inspected material. The machine vision system developed classifies the condition of material by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features.A pattern recognition approach is developed based on the methodological knowledge from manual procedure. The pattern recognition approach for this thesis work was developed and achieved by a non destructive testing method to identify the flaws in manually done condition monitoring of sleepers.In this method, a test vehicle is designed to capture sleeper images similar to visual inspection by human operator and the raw data for pattern recognition approach is provided from the captured images of the wooden sleepers. The data from the NDT method were further processed and appropriate features were extracted.The collection of data by the NDT method is to achieve high accuracy in reliable classification results. A key idea is to use the non supervised classifier based on the features extracted from the method to discriminate the condition of wooden sleepers in to either good or bad. Self organising map is used as classifier for the wooden sleeper classification.In order to achieve greater integration, the data collected by the machine vision system was made to interface with one another by a strategy called fusion. Data fusion was looked in at two different levels namely sensor-level fusion, feature- level fusion. As the goal was to reduce the accuracy of the human error on the rail sleeper classification as good or bad the results obtained by the feature-level fusion compared to that of the results of actual classification were satisfactory.

Computerized identification of motor complications in Parkinson's disease

Objective: To investigate whether spirography-based objective measures are able to effectively characterize the severity of unwanted symptom states (Off and dyskinesia) and discriminate them from motor state of healthy elderly subjects. Background: Sixty-five patients with advanced Parkinson’s disease (PD) and 10 healthy elderly (HE) subjects performed repeated assessments of spirography, using a touch screen telemetry device in their home environments. On inclusion, the patients were either treated with levodopa-carbidopa intestinal gel or were candidates for switching to this treatment. On each test occasion, the subjects were asked trace a pre-drawn Archimedes spiral shown on the screen, using an ergonomic pen stylus. The test was repeated three times and was performed using dominant hand. A clinician used a web interface which animated the spiral drawings, allowing him to observe different kinematic features, like accelerations and spatial changes, during the drawing process and to rate different motor impairments. Initially, the motor impairments of drawing speed, irregularity and hesitation were rated on a 0 (normal) to 4 (extremely severe) scales followed by marking the momentary motor state of the patient into 2 categories that is Off and Dyskinesia. A sample of spirals drawn by HE subjects was randomly selected and used in subsequent analysis. Methods: The raw spiral data, consisting of stylus position and timestamp, were processed using time series analysis techniques like discrete wavelet transform, approximate entropy and dynamic time warping in order to extract 13 quantitative measures for representing meaningful motor impairment information. A principal component analysis (PCA) was used to reduce the dimensions of the quantitative measures into 4 principal components (PC). In order to classify the motor states into 3 categories that is Off, HE and dyskinesia, a logistic regression model was used as a classifier to map the 4 PCs to the corresponding clinically assigned motor state categories. A stratified 10-fold cross-validation (also known as rotation estimation) was applied to assess the generalization ability of the logistic regression classifier to future independent data sets. To investigate mean differences of the 4 PCs across the three categories, a one-way ANOVA test followed by Tukey multiple comparisons was used. Results: The agreements between computed and clinician ratings were very good with a weighted area under the receiver operating characteristic curve (AUC) coefficient of 0.91. The mean PC scores were different across the three motor state categories, only at different levels. The first 2 PCs were good at discriminating between the motor states whereas the PC3 was good at discriminating between HE subjects and PD patients. The mean scores of PC4 showed a trend across the three states but without significant differences. The Spearman’s rank correlations between the first 2 PCs and clinically assessed motor impairments were as follows: drawing speed (PC1, 0.34; PC2, 0.83), irregularity (PC1, 0.17; PC2, 0.17), and hesitation (PC1, 0.27; PC2, 0.77). Conclusions: These findings suggest that spirography-based objective measures are valid measures of spatial- and time-dependent deficits and can be used to distinguish drug-related motor dysfunctions between Off and dyskinesia in PD. These measures can be potentially useful during clinical evaluation of individualized drug-related complications such as over- and under-medications thus maximizing the amount of time the patients spend in the On state.