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.

A pharmacokinetic-pharmacodynamic model for duodenal levodopa infusion

Objective Levodopa in presence of decarboxylase inhibitors is following two-compartment kinetics and its effect is typically modelled using sigmoid Emax models. Pharmacokinetic modelling of the absorption phase of oral distributions is problematic because of irregular gastric emptying. The purpose of this work was to identify and estimate a population pharmacokinetic- pharmacodynamic model for duodenal infusion of levodopa/carbidopa (Duodopa®) that can be used for in numero simulation of treatment strategies. Methods The modelling involved pooling data from two studies and fixing some parameters to values found in literature (Chan et al. J Pharmacokinet Pharmacodyn. 2005 Aug;32(3-4):307-31). The first study involved 12 patients on 3 occasions and is described in Nyholm et al. Clinical Neuropharmacology 2003:26:156-63. The second study, PEDAL, involved 3 patients on 2 occasions. A bolus dose (normal morning dose plus 50%) was given after a washout during night. Plasma samples and motor ratings (clinical assessment of motor function from video recordings on a treatment response scale between -3 and 3, where -3 represents severe parkinsonism and 3 represents severe dyskinesia.) were repeatedly collected until the clinical effect was back at baseline. At this point, the usual infusion rate was started and sampling continued for another two hours. Different structural absorption models and effect models were evaluated using the value of the objective function in the NONMEM package. Population mean parameter values, standard error of estimates (SE) and if possible, interindividual/interoccasion variability (IIV/IOV) were estimated. Results Our results indicate that Duodopa absorption can be modelled with an absorption compartment with an added bioavailability fraction and a lag time. The most successful effect model was of sigmoid Emax type with a steep Hill coefficient and an effect compartment delay. Estimated parameter values are presented in the table. Conclusions The absorption and effect models were reasonably successful in fitting observed data and can be used in simulation experiments.

Attraktivt arbete ur enhetschefers perspektiv : En kvalitativ studie genomförd i Falu kommun

Sedan 1990- talet har de offentliga organisationerna genomgått flera stora förändringar som medfört att enhetschefer inom den kommunala verksamheten fått ökat ansvar och befogenheter, vilket gör enhetschefsrollen mer komplext. Rapporter visar på svårigheter att rekrytera och behålla chefer, därför är viktigt att undersöka hur enhetschefens arbete ska kunna utvecklas till ett mer attraktivt arbete. Ett attraktivt arbete skapas genom att ge medarbetarna bra förutsättningar inom arbetsinnehåll, arbetstillfredsställelse, samt arbetsförhållanden. I chefsyrket ingår tre roller varav en är medarbetarrollen som enligt teorin inte är speciellt prioriterad eller synliggjord. Studiens syfte är att identifiera vad som gör enhetschefyrket till ett attraktivt arbete samt hur det kan utvecklas. I studien har en kvalitativ forskningsmetod används för att uppnå en djupare förståelse kring enhetschefernas arbetssituation. Detta har bidragit till att ett hermeneutiska synsätt valts, då vi strävar efter att få en ökad förståelse. Studien genomfördes på Falu kommun, där sex enhetschefer inom omvårdnadsförvaltningen intervjuades. Resultatet visar att enhetscheferna är tillfredsställda med sin arbetssituation och upplever arbetet som attraktivt. Faktorer som varierande, handlingsfrihet, problemlösning, sociala kontakter och delar av erkänsla hör till attraktivitetsfaktorer i yrket. Enhetscheferna beskriver tre utvecklingsområden för att öka attraktiviteten. Det första innefattar ledarskapet i organisationen där brister förekommer inom kommunikation och planering. Andra utvecklingsområdet handlar om arbetstakten genom att individanpassa arbetsgruppernas storlek samt tid för reflektion och återhämtning. Tredje utvecklingsområdet enhetscheferna belyser är bristen på erkänsla från organisationen. Utöver enhetschefernas brister har vi identifierat ytterligare ett utvecklingsområde för att öka attraktiviteten. Under intervjuerna har det framkommit att det råder stor avsaknad av kollegor att "bolla idéer" med vilket i sin tur medför att arbetet upplevs som ensamt. För att motverka ensamheten är chefsgruppshandledning ett alternativ, där reflektion och feedback ingår. Forskningen visar att detta även leder till ökad självkänsla, engagemang.

Hand Gesture Detection & Recognition System

The project introduces an application using computer vision for Hand gesture recognition. A camera records a live video stream, from which a snapshot is taken with the help of interface. The system is trained for each type of count hand gestures (one, two, three, four, and five) at least once. After that a test gesture is given to it and the system tries to recognize it.A research was carried out on a number of algorithms that could best differentiate a hand gesture. It was found that the diagonal sum algorithm gave the highest accuracy rate. In the preprocessing phase, a self-developed algorithm removes the background of each training gesture. After that the image is converted into a binary image and the sums of all diagonal elements of the picture are taken. This sum helps us in differentiating and classifying different hand gestures.Previous systems have used data gloves or markers for input in the system. I have no such constraints for using the system. The user can give hand gestures in view of the camera naturally. A completely robust hand gesture recognition system is still under heavy research and development; the implemented system serves as an extendible foundation for future work.

Strategies for the use of PIMEX and other video exposure monitoring methods. : Final report from sub-project “implementation strategies” within the HERIVIS project

Video exposure monitoring (VEM) is a group of methods used for occupational hygiene studies. The method is based on a combined use of video recordings with measurements taken with real-time monitoring instruments. A commonly used name for VEM is PIMEX. Since PIMEX initially was invented in the mid 1980’s have the method been implemented and developed in a number of countries. With the aim to give an updated picture of how VEM methods are used and to investigate needs for further development have a number of workshops been organised in Finland, UK, the Netherlands, Germany and Austria. Field studies have also been made with the aim to study to what extent the PIMEX method can improve workers motivation to actively take part in actions aimed at workplace improvements.The results from the workshops illustrates clearly that there is an impressive amount of experiences and ideas for the use of VEM within the network of the groups participating in the workshops. The sharing of these experiences between the groups, as well as dissemination of it to wider groups is, however, limited. The field studies made together with a number of welders indicate that their motivation to take part in workplace improvements is improved after the PIMEX intervention. The results are however not totally conclusive and further studies focusing on motivation are called for.It is recommended that strategies for VEM, for interventions in single workplaces, as well as for exposure categorisation and production of training material are further developed. It is also recommended to conduct a research project with the intention of evaluating the effects of the use of VEM as well as to disseminate knowledge about the potential of VEM to occupational hygiene experts and others who may benefit from its use.

Motion Cues Analysis for Parkinson Gait Recognition

Background: Previous assessment methods for PG recognition used sensor mechanisms for PG that may cause discomfort. In order to avoid stress of applying wearable sensors, computer vision (CV) based diagnostic systems for PG recognition have been proposed. Main constraints in these methods are the laboratory setup procedures: Novel colored dresses for the patients were specifically designed to segment the test body from a specific colored background. Objective: To develop an image processing tool for home-assessment of Parkinson Gait(PG) by analyzing motion cues extracted during the gait cycles. Methods: The system is based on the idea that a normal body attains equilibrium during the gait by aligning the body posture with the axis of gravity. Due to the rigidity in muscular tone, persons with PD fail to align their bodies with the axis of gravity. The leaned posture of PD patients appears to fall forward. Whereas a normal posture exhibits a constant erect posture throughout the gait. Patients with PD walk with shortened stride angle (less than 15 degrees on average) with high variability in the stride frequency. Whereas a normal gait exhibits a constant stride frequency with an average stride angle of 45 degrees. In order to analyze PG, levodopa-responsive patients and normal controls were videotaped with several gait cycles. First, the test body is segmented in each frame of the gait video based on the pixel contrast from the background to form a silhouette. Next, the center of gravity of this silhouette is calculated. This silhouette is further skeletonized from the video frames to extract the motion cues. Two motion cues were stride frequency based on the cyclic leg motion and the lean frequency based on the angle between the leaned torso tangent and the axis of gravity. The differences in the peaks in stride and lean frequencies between PG and normal gait are calculated using Cosine Similarity measurements. Results: High cosine dissimilarity was observed in the stride and lean frequencies between PG and normal gait. High variations are found in the stride intervals of PG whereas constant stride intervals are found in the normal gait. Conclusions: We propose an algorithm as a source to eliminate laboratory constraints and discomfort during PG analysis. Installing this tool in a home computer with a webcam allows assessment of gait in the home environment.