Prabitha Urwyler

Visual complaints and visual hallucinations in Parkinson's disease

BACKGROUND Visual symptoms are common in Parkinsons disease (PD) and are frequently under-diagnosed. The detection of visual symptoms is important for differential diagnosis and patient management. AIM To establish the prevalence of recurrent visual complaints (RVC) and recurrent visual hallucinations (RVH) and to investigate their interaction in PD patients and controls. METHODS This cross-sectional study included 88 PD patients and 90 controls. RVC and RVH were assessed with a visual symptom questionnaire and the North-East-Visual-Hallucinations-Interview (NEVHI). RESULTS Double vision (PD vs. CONTROLS 18.2% vs. 1.3%; p < 0.001), misjudging objects when walking (PD vs. CONTROLS 12.5% vs. 1.3%; p < 0.01), words moving whilst reading (PD vs. CONTROLS 17.0% vs. 1.3%; p < 0.001) and freezing in narrow spaces (PD vs. CONTROLS 30.7% vs. 0%; p < 0.001) were almost exclusively found in PD patients. The same was true for recurrent complex visual hallucinations and illusions (PD vs. CONTROLS both 17.0% vs. 0%; p < 0.001). Multiple RVC (43.2% vs. 15.8%) and multiple RVH (29.5% vs. 5.6%) were also more common in PD patients (both p < 0.001). RVC did not predict recurrent complex visual hallucinations; but double vision (p = 0.018, R(2) = 0.302) and misjudging objects (p = 0.002, R(2) = 0.302) predicted passage hallucinations. Misjudging objects also predicted the feeling of presence (p = 0.010, R(2) = 0.321). CONCLUSIONS Multiple and recurrent visual symptoms are common in PD. RVC emerged as risk factors predictive of the minor forms of hallucinations, but not recurrent complex visual hallucinations.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The “health state” of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

Nanoimprint lithography process chains for the fabrication of micro- and nanodevices

Abstract. The nanoimprint lithography (NIL) process with its key elements molding and thin film pattern transfer refers to the established process chain of resist-based patterning of hard substrates. Typical processes for mass fabrication are either wafer-scale imprint or continuous roll-to-roll processes. In contrast to this, similar process chains were established for polymeric microelements fabricated by injection molding, particularly when surface topographies need to be integrated into monolithic polymer elements. NIL needs to be embedded into the framework of general replication technologies, with sizes ranging from nanoscopic details to macroscopic entities. This contribution presents elements of a generalized replication process chain involving NIL and demonstrates its wide application by presenting nontypical NIL products, such as an injection-molded microcantilever. Additionally, a hybrid approach combining NIL and injection molding in a single tool is presented. Its aim is to introduce a toolbox approach for nanoreplication into NIL-based processing and to facilitate the choice of suitable processes for micro- and nanodevices. By proposing a standardized process flow as described in the NaPANIL library of processes, the use of establish process sequences for new applications is facilitated.

Effects of age and eccentricity on visual target detection

The aim of this study was to examine the effects of aging and target eccentricity on a visual search task comprising 30 images of everyday life projected into a hemisphere, realizing a ±90° visual field. The task performed binocularly allowed participants to freely move their eyes to scan images for an appearing target or distractor stimulus (presented at 10°; 30°, and 50° eccentricity). The distractor stimulus required no response, while the target stimulus required acknowledgment by pressing the response button. One hundred and seventeen healthy subjects (mean age = 49.63 years, SD = 17.40 years, age range 20–78 years) were studied. The results show that target detection performance decreases with age as well as with increasing eccentricity, especially for older subjects. Reaction time also increases with age and eccentricity, but in contrast to target detection, there is no interaction between age and eccentricity. Eye movement analysis showed that younger subjects exhibited a passive search strategy while older subjects exhibited an active search strategy probably as a compensation for their reduced peripheral detection performance.

Micro- and nanostructured polymer substrates for biomedical applications

Polymer implants are interesting alternatives to the contemporary load-bearing implants made from metals. Polyetheretherketone (PEEK), a well-established biomaterial for example, is not only iso-elastic to bone but also permits investigating the surrounding soft tissues using magnetic resonance imaging or computed tomography, which is particularly important for cancer patients. The commercially available PEEK bone implants, however, require costly coatings, which restricts their usage. As an alternative to coatings, plasma activation can be applied. The present paper shows the plasma-induced preparation of nanostructures on polymer films and on injection-molded micro-cantilever arrays and the associated chemical modifications of the surface. In vitro cell experiments indicate the suitability of the activation process. In addition, we show that microstructures such as micro-grooves 1 μm deep and 20 μm wide cause cell alignment. The combination of micro-injection molding, simultaneous microstructuring using inserts/bioreplica and plasma treatments permits the preparation of polymer implants with nature-analogue, anisotropic micro- and nanostructures.

Recognition of activities of daily living in healthy subjects using two ad-hoc classifiers

BackgroundActivities of daily living (ADL) are important for quality of life. They are indicators of cognitive health status and their assessment is a measure of independence in everyday living. ADL are difficult to reliably assess using questionnaires due to self-reporting biases. Various sensor-based (wearable, in-home, intrusive) systems have been proposed to successfully recognize and quantify ADL without relying on self-reporting. New classifiers required to classify sensor data are on the rise. We propose two ad-hoc classifiers that are based only on non-intrusive sensor data.MethodsA wireless sensor system with ten sensor boxes was installed in the home of ten healthy subjects to collect ambient data over a duration of 20 consecutive days. A handheld protocol device and a paper logbook were also provided to the subjects. Eight ADL were selected for recognition. We developed two ad-hoc ADL classifiers, namely the rule based forward chaining inference engine (RBI) classifier and the circadian activity rhythm (CAR) classifier. The RBI classifier finds facts in data and matches them against the rules. The CAR classifier works within a framework to automatically rate routine activities to detect regular repeating patterns of behavior. For comparison, two state-of-the-art [Naïves Bayes (NB), Random Forest (RF)] classifiers have also been used. All classifiers were validated with the collected data sets for classification and recognition of the eight specific ADL.ResultsOut of a total of 1,373 ADL, the RBI classifier correctly determined 1,264, while missing 109 and the CAR determined 1,305 while missing 68 ADL. The RBI and CAR classifier recognized activities with an average sensitivity of 91.27 and 94.36%, respectively, outperforming both RF and NB.ConclusionsThe performance of the classifiers varied significantly and shows that the classifier plays an important role in ADL recognition. Both RBI and CAR classifier performed better than existing state-of-the-art (NB, RF) on all ADL. Of the two ad-hoc classifiers, the CAR classifier was more accurate and is likely to be better suited than the RBI for distinguishing and recognizing complex ADL.

Age-dependent visual exploration during simulated day- and night driving on a motorway: a cross-sectional study.

BackgroundCentral and peripheral vision is needed for object detection. Previous research has shown that visual target detection is affected by age. In addition, light conditions also influence visual exploration. The aim of the study was to investigate the effects of age and different light conditions on visual exploration behavior and on driving performance during simulated driving.MethodsA fixed-base simulator with 180 degree field of view was used to simulate a motorway route under daylight and night conditions to test 29 young subjects (25–40 years) and 27 older subjects (65–78 years). Drivers’ eye fixations were analyzed and assigned to regions of interests (ROI) such as street, road signs, car ahead, environment, rear view mirror, side mirror left, side mirror right, incoming car, parked car, road repair. In addition, lane-keeping and driving speed were analyzed as a measure of driving performance.ResultsOlder drivers had longer fixations on the task relevant ROI, but had a lower frequency of checking mirrors when compared to younger drivers. In both age groups, night driving led to a less fixations on the mirror. At the performance level, older drivers showed more variation in driving speed and lane-keeping behavior, which was especially prominent at night. In younger drivers, night driving had no impact on driving speed or lane-keeping behavior.ConclusionsOlder drivers’ visual exploration behavior are more fixed on the task relevant ROI, especially at night, when driving performance becomes more heterogeneous than in younger drivers.

Surface-patterned micromechanical elements by polymer injection molding with hybrid molds

Hybrid molds enable the fabrication of polymeric parts with features of different length scales by injection molding. The resulting polymer microelements combine optical or biological functionalities with designed mechanical properties. Two applications are chosen for illustration of this concept: As a first example, microelements for optical communication via fiber-to-fiber coupling are manufactured by combining two molds to a small mold insert. Both molds are fabricated using lithography and electroplating. As a second example, microcantilevers (μCs) for chemical sensing are surface patterned using a modular mold composed of a laser-machined cavity defining the geometry of the μCs, and an opposite flat tool side which is covered by a patterned polymer foil. Injection molding results in an array of 35 μm-thick μCs with microscale surface topographies. In both cases, when the mold is assembled and closed, reliefs are transferred onto one surface of the molded element whose outlines are defined by the micromold cavity. The main advantage of these hybrid methods lies in the simple integration of optical surface structures and gratings onto the surface of microcomponents with different sizes and orientations. This allows for independent development of functional properties and combinations thereof.

Cognitive impairment categorized in community-dwelling older adults with and without dementia using in-home sensors that recognise activities of daily living

Cognitive impairment due to dementia decreases functionality in Activities of Daily Living (ADL). Its assessment is useful to identify care needs, risks and monitor disease progression. This study investigates differences in ADL pattern-performance between dementia patients and healthy controls using unobtrusive sensors. Around 9,600 person-hours of activity data were collected from the home of ten dementia patients and ten healthy controls using a wireless-unobtrusive sensors and analysed to detect ADL. Recognised ADL were visualized using activity maps, the heterogeneity and accuracy to discriminate patients from healthy were analysed. Activity maps of dementia patients reveal unorganised behaviour patterns and heterogeneity differed significantly between the healthy and diseased. The discriminating accuracy increases with observation duration (0.95 for 20 days). Unobtrusive sensors quantify ADL-relevant behaviour, useful to uncover the effect of cognitive impairment, to quantify ADL-relevant changes in the course of dementia and to measure outcomes of anti-dementia treatments.

Tailoring surface nanostructures on polyaryletherketones for load-bearing implants

Abstract High-performance thermoplastics including polyetheretherketone (PEEK) are key biomaterials for load-bearing implants. Plasma treatment of implants surfaces has been shown to chemically activate its surface, which is a prerequisite to achieve proper cell attachment. Oxygen plasma treatment of PEEK films results in very reproducible surface nanostructures and has been reported in the literature. Our goal is to apply the plasma treatment to another promising polymer, polyetherketoneketone (PEKK), and compare its characteristics to the ones of PEEK. Oxygen plasma treatments of plasma powers between 25 and 150 W were applied on 60 μm-thick PEKK and 100 μm-thick PEEK films. Analysis of the nanostructures by atomic force microscopy showed that the roughness increased and island density decreased with plasma power for both PEKK and PEEK films correlating with contact angle values without affecting bulk properties of the used films. Thermal analysis of the plasma-treated films shows that the plasma treatment does not change the bulk properties of the PEKK and PEEK films.