Intelligibility of French historical towns : assessing the impact of 19th century urban interventions

During the 19th century, French cities underwent great transformations in response to insalubrity. Many new streets were created and changed the overall intelligibility of the street network with the creation of boulevards where the defensive town wall used to stand, as well as large avenues to provide better accessibility to the centre of towns. This study aims to assess the impact of these transformations on the urban tissue by measuring the intelligibility of the urban form before and after the transformations. It compares the Napoleonic cadastral maps of Agen, Amiens, Avignon, Dijon, Clermont and Tourcoing with the current cadastre. Intelligibility is measured by the ease of navigation towards the centre of an urban environment and the ease of traversing it as one traces a path between two given points on the map. The choice of paths is examined, highlighting the role of the new streets. This research brings together the cognitive mechanisms that underpin the exploration and decision-making process when navigating urban maps with their syntactic and morphological properties. It examines the motor aspect of decision making during the navigation process. Motor reactions are recorded using technology developed for the quantification of neuromotor impairments. This interdisciplinary approach provides a means to measure and better understand the intelligibility of urban environments

Distinguishing Parkinson's disease from other syndromes causing tremor using automatic analysis of writing and drawing tasks

An easily performed and objective test of patients fine motor skills would be valuable in the diagnosis of Parkinson's disease (PD). In this study we present a set of automatic methods for quantifying the motor symptoms of PD and show that these automatically extracted features can be used to distinguish PD from other movement disorders causing tremor, namely essential tremor (ET), functional tremor (FT) and enhanced physiological tremor (EPT). The classification accuracies (mean of sensitivity and specificity) for separating PD from the other tremor syndromes were 82.0 % for ET, 69.8 % for FT and 72.2 % for EPT

Classification of handwriting kinematics in automated diagnosis and monitoring of Parkinson's disease

Parkinson's disease is one of the most prevalent neurodegenerative conditions. Currently, there is no standard clinical tool available to diagnose PD. One of the research priorities is to come up with biomarkers which will improve the diagnostic process and can be used for the clinical test. At present, the only way to assess this disease is by visually observing the symptoms of the patient which is performed only by expert neurologists. As of now, there is no treatment to prevent the progression of PD. However, there is an elemental drug `Levodopa' (L-dopa) available to control the disease by increasing dopamine cells in the brain. It is important to detect PD and start treatment in the early stages as it helps to control the symptoms and significantly delays the development of motor complications. In this study fine motor symptoms handwriting has been studied. As a first objective I have conducted the experiments on the significant number of patients and age-matched control (112 Participants:56 PD and 56 controls), and thus completed the task of data collection. The system developed extracts the dynamic features of the handwriting/drawing, reports the possible strength of dynamic features providing a basis for automated analysis. The advantage of this approach is that patients are not required to follow complex commands, and the analysis can be fully automized. I anticipate that following appropriate clinical tests already planned, the system will be able to detect early disease symptoms remotely outside hospitals or clinics. It could also be used for self-evaluation by patients with neuromuscular and motor neuron disorders. This device can be used without compromising on the comfort level of Patients who may still prefer writing with an ink pen on plain paper. This study proposes a new feature `Composite Index of Speed and Pen-pressure' (CISP) to distinguish between different stages of Parkinson's disease. The experiment also demonstrated a method which can be used with guided spiral drawing to improve classification results to predict Parkinson's disease. Further, I recommend using a panel of writing tasks which might prove to be an effective biomarker for cell loss in the substantia nigra and the associated dopamine deficiency. Thus, models developed can be used in designing an automated application for predicting and monitoring Parkinson's diseas