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.

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.

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.

A fuzzy rule-based decision support system for Duodopa treatment in Parkinson

A decision support system (DSS) was implemented based on a fuzzy logic inference system (FIS) to provide assistance in dose alteration of Duodopa infusion in patients with advanced Parkinson’s disease, using data from motor state assessments and dosage. Three-tier architecture with an object oriented approach was used. The DSS has a web enabled graphical user interface that presents alerts indicating non optimal dosage and states, new recommendations, namely typical advice with typical dose and statistical measurements. One data set was used for design and tuning of the FIS and another data set was used for evaluating performance compared with actual given dose. Overall goodness-of-fit for the new patients (design data) was 0.65 and for the ongoing patients (evaluation data) 0.98. User evaluation is now ongoing. The system could work as an assistant to clinical staff for Duodopa treatment in advanced Parkinson’s disease.

Teaching Intercultural Communication in Foreign Language Education through Video Conferencing

Since 1980s, Western linguists and specialists on second language acquisition have emphasized the importance of enhancing students’ intercultural communication competence in foreign language education. At the same time, the demand for intercultural communicative competence increased along with the advances of communication technology with its increasingly global reach and the process of globalization itself.In the field of distance language education, these changes have resulted in a shift of focus from the production and distribution of learning materials towards communication and learning as a social process, facilitated by various internet-based platforms. The current focus on learners interacting and communicating synchronously trough videoconferencing is known as the fourth generation of distance language education. Despite the fact that teaching of Chinese as a foreign language (CFL) faces the same or even greater challenges as teaching other languages, the intercultural communication perspective is still quite a new trend in CFL and its implementation and evaluation are still under development. Moreover, the advocates of the new trends in CFL have so far focused almost exclusively on classroom-based courses, neglecting the distance mode of CFL and leaving it as an open field for others to explore. In this under-researched context, Dalarna University (Sweden), where I currently work, started to provide web-based courses of the Chinese language in 2007. Since 2010, the Chinese language courses have been available only in the distance form, using the same teaching materials as the previous campus-based courses. The textbooks used in both settings basically followed the functional nationalism approach. However, in order to catch up with the main trend of foreign-language education, we felt a need to implement the cross-cultural dimension into the distance courses as well. Therefore in 2010, a pilot study has been carried out to explore opportunities and challenges for implementing a cross-cultural perspective into existing courses and evaluating the effectiveness of this implementation based on the feedback of the students and on the experience of the teacher/researcher.

Real time video segmentation for recognising paint marks on bad wooden railway sleepers

Wooden railway sleeper inspections in Sweden are currently performed manually by a human operator; such inspections are based on visual analysis. Machine vision based approach has been done to emulate the visual abilities of human operator to enable automation of the process. Through this process bad sleepers are identified, and a spot is marked on it with specific color (blue in the current case) on the rail so that the maintenance operators are able to identify the spot and replace the sleeper. The motive of this thesis is to help the operators to identify those sleepers which are marked by color (spots), using an “Intelligent Vehicle” which is capable of running on the track. Capturing video while running on the track and segmenting the object of interest (spot) through this vehicle; we can automate this work and minimize the human intuitions. The video acquisition process depends on camera position and source light to obtain fine brightness in acquisition, we have tested 4 different types of combinations (camera position and source light) here to record the video and test the validity of proposed method. A sequence of real time rail frames are extracted from these videos and further processing (depending upon the data acquisition process) is done to identify the spots. After identification of spot each frame is divided in to 9 regions to know the particular region where the spot lies to avoid overlapping with noise, and so on. The proposed method will generate the information regarding in which region the spot lies, based on nine regions in each frame. From the generated results we have made some classification regarding data collection techniques, efficiency, time and speed. In this report, extensive experiments using image sequences from particular camera are reported and the experiments were done using intelligent vehicle as well as test vehicle and the results shows that we have achieved 95% success in identifying the spots when we use video as it is, in other method were we can skip some frames in pre-processing to increase the speed of video but the segmentation results we reduced to 85% and the time was very less compared to previous one. This shows the validity of proposed method in identification of spots lying on wooden railway sleepers where we can compromise between time and efficiency to get the desired result.

Rapport från projektet Multimodala uppgifter och bedömning i matematik : Video och kollaborativa samtal som metod vid lärares bedömning av elevers matematiska kunskaper

This research will discuss some experiences from a four year school research study. It was conducted in cooperation with teachers from four municipalities in Dalarna. The aim of the research was to examine teachers´ professional development when they participated in collaborative discussions based on video recordings and video edited material from specific lessons in their own practice. The study had two foci one was to investigate methods and tools that teachers can use to develop their ability to assess their students while working on multimodal tasks. The other was to examine how video can be used by teachers wanting to obtain knowledge about assessing students. The study is based on several theories about when teachers collaborate to create new knowledge. The first is the design theoretical approach – where visual ethnography and a semiotic approach contribute to problematize the use and mixture of different modes. A basic assumption of the framework here is that meanings are made and communicated in mathematics through a wide range of semiotic modes. By using video as an essential tool in the research the framework theories concerning visual ethnography, video documentation and individuals as reflective practitioners were also needed. The findings can be divided into the following themes: the use of tasks for assessment, collaborative discussion, equipment, ethical dilemmas. Collaborative discussions were evaluated as a meaningful way of sharing knowledge. The use of video recordings in association with these discussions raised important ethical issues. Working with the assessment framework was of great interest to the teachers but it took a lot of time from their ordinary work. In this way the project highlighted more general aspects of school development. The research also concerns teachers´ use of collaborative discussions in assessment work, multimodal tasks in mathematics and video as a research tool in general.