After severe injury or neurodegenerative disorders patients often experience long-term functional deficits, resulting in a reduction o performance in activities of daily living (ADL). Given their direct relevance to everyday functioning and quality of life, neurorehabilitative programs using simulated ADL's have seen increased interest recently. One of the core elements in simulated ADL's is the interface between the user and the virtual environment, which has a high impact on the therapeutic outcome. The aim of this study was to nalyze the feasibility of a simple virtual ADL (tea preparation task) using two different input devices. The tea preparation task setup consisted of a computer rendering the virtual environment, a head-mounted display (HMD) to visually present the ADL, and two input devices (mouse and handheld controller) to guide virtual hands in the virtual environment. A total of 24 healthyyoung adults performed the tea preparation task after which workload, usability, immersion and presence was rated. The handheld controller was rated significantly lower workload and higher usability than the mouse input device. Also, the sense of being there (immersion) and spatial presence ratings for the task and setup were close to the maximum score of 5. Thus, the handheld controller outperformed the mouse, suggesting that user interaction in the virtual environment with the handheld controller is similar to the real world and intuitive to use. Overall, the simulated ADL implemented with VR technology is feasible for diagnostic and rehabilitative purposes in patients experiencing long-term functional deficits.
Aphasia is the loss or impairment of language functions and affects everyday social life. The disorder leads to the inability to understand and be understood in both written and verbal communication and affects the linguistic modalities of auditory comprehension, verbal expression, reading, and writing. Due to heterogeneity of the impairment, therapy must be adapted individually and dynamically to patient needs. An important factor for successful aphasia therapy is dose and intensity of therapy. Tablet computer-based apps are a promising treatment method that allows patients to train independently at home, is well accepted, and is known to be beneficial for patients. In addition, it has been shown to ease the burden of therapists.The aim of this project was to develop an adaptive multimodal system that enables aphasic patients to train at home using language-related tasks autonomously, allows therapists to remotely assign individualized tasks in an easy and time-efficient manner, and tracks the patient's progress as well as creation of new individual exercises.The system consists of two main parts: (1) the patient's interface, which allows the patient to exercise, and (2) the therapist's interface, which allows the therapist to assign new exercises to the patient and supervise the patient's progress. The pool of exercises is based on a hierarchical language structure. Using questionnaires, therapists and patients evaluated the system in terms of usability (ie, System Usability Scale) and motivation (ie, adapted Intrinsic Motivation Inventory).A total of 11 speech and language therapists (age: mean 28, SD 7 years) and 15 patients (age: mean 53, SD 10 years) diagnosed with aphasia participated in this study. Patients rated the Bern Aphasia App in terms of usability (scale 0-100) as excellent (score >70; Z=-1.90; P=.03) and therapists rated the app as good (score >85; Z=-1.75; P=.04). Furthermore, patients enjoyed (scale 0-6) solving the exercises (score>3; mean 3.5, SD 0.40; Z=-1.66; P=.049).Based on the questionnaire scores, the system is well accepted and simple to use for patients and therapists. Furthermore, the new tablet computer-based app and the hierarchical language exercise structure allow patients with different types of aphasia to train with different doses and intensities independently at home. Thus, the novel system has potential for treatment of patients with aphasia as a supplement to face-to-face therapy.
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.
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.
Hallucinations can occur in different sensory modalities within an individual, both simultaneously and serially in time. Historically, they have typically been studied in clinical populations as phenomena occurring in a single sensory modality. Yet, hallucinatory experiences that occur in two or more sensory systems - multimodal hallucinations (MMHs) - are more prevalent than previously thought, and may have greater adverse impact than unimodal ones, but they remain relatively under-researched. Why people experience multi- modal hallucinations, what the implications for the person are, and how this could impact how such experiences are treated when distressing remain under-studied questions. Here, we review the available literature on MMHs and discuss some key concepts in the field, namely a) the definition and categorisation of both serial and simultaneous MMHs, b) which assessment tools are available and how they can be improved, and c) the explanatory power that current hallucination theories might have for MMHs. Overall, we suggest that current models need to be updated or developed in order to account for MMHs and to inform research into the underlying processes of such hallucinatory phenomena. We make recommendations for future research and clinical practice, highlighting the potential clinical impact of MMHs, the need for better assessment tools that can reliably measure MMHs and distinguish them from other related phenomena (such as delirium), as well as the need for service-user involvement in the validation of classification systems of MMHs.
Activities 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.A 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.Out 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.The 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.
There is currently a need for engaging, user-friendly and repeatable tasks for assessment of cognitive and motor function in aging and neurodegenerative diseases. This study evaluated the feasibility of a maze-like Numberlink puzzle game in assessing differences in game-based measures of cognition and motor function due to age and neurodegenerative diseases. Fifty-five participants, including young (18 – 31 years, n=18), older (64 – 79 years, n=14) and oldest adults (86 – 98 years, n=14), and patients with Parkinson's (59 – 76 years, n=4) and Huntington's disease (35 – 66 years, n=5) played different difficulty levels of the Numberlink puzzle game and completed usability questionnaires and tests for psychomotor, attentional, visuospatial, and constructional and executive function. Analyses of Numberlink game-based cognitive (solving time and errors) and motor (mean velocity and movement direction changes) performance metrics revealed statistically significant differences between age groups and between patients with Huntington's disease and older adults. However, patients with Parkinson's disease did not differ from older adults. Correlational analyses showed significant associations between game-based performance and movement metrics and performance on neuropsychological tests for psychomotor, attentional, visuospatial, and constructional and executive function. Furthermore, varying characteristics of the Numberlink puzzle game succeeded in creating graded difficulty levels. Findings from this study support recent suggestions that data from a maze-like puzzle game provides potential 'digital biomarkers' to assess changes in psychomotor, visuoconstructional and executive function related to aging and neurodegeneration. Especially, game-based movement measures from the maze-like puzzle Numberlink games are promising as a tool to monitor the progression of motor impairment in neurodegenerative diseases. Further studies are needed to more comprehensively establish the cognitive validity and test-retest reliability of using Numberlink puzzles as a valid cognitive assessment tool.
Isometric strength measures and timed up and go (TUG) tests are both recognized as valuable tools for fall prediction in older adults. However, results from direct comparison of these two tests are lacking. We aimed to assess the potential of isometric strength measures and the different modalities of the TUG test to detect individuals at risk of falling.This is a prospective cohort study including 24 community-dwelling older adults (≥65 years, 19 females, 88±7 years). Participants performed three variations of the TUG test (standard, counting and holding a full cup) and three isometric strength tests (handgrip, knee extension and hip flexion) at several time points (at baseline and every ~6 weeks) during a one-year follow-up. The association between these tests and the incidence of falls during the follow-up was assessed.Twelve participants out of 24 participants experienced falls during the follow-up. Fallers showed a significantly lower handgrip strength (-5.7 kg, 95% confidence interval: -10.4 to -1.1, p=0.019) and knee extension strength (-4.9 kg, -9.6 to -0.2, p=0.042) at follow-up, while no significant differences were found for any TUG variation.Handgrip and knee extension strength measures - particularly when assessed regularly over time - have the potential to serve as a simple and easy tool for detecting individuals at risk of falling as compared to functional mobility measures (ie, TUG test).
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