Oral Levosimendan Increases Cerebral Blood Flow Velocities in Patients with a History of Stroke or Transient Ischemic Attack : A Pilot Safety Study

Peer reviewe

Using biochar to purify runoff in road verges of urbanised watersheds: A large-scale field lysimeter study

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Feasibility and patient acceptability of a commercially available wearable and a smart phone application in identification of motor states in parkinson’s disease

Abstract In the quantification of symptoms of Parkinson’s disease (PD), healthcare professional assessments, patient reported outcomes (PRO), and medical device grade wearables are currently used. Recently, also commercially available smartphones and wearable devices have been actively researched in the detection of PD symptoms. The continuous, longitudinal, and automated detection of motor and especially non-motor symptoms with these devices is still a challenge that requires more research. The data collected from everyday life can be noisy and frequently contains artefacts, and novel detection methods and algorithms are therefore needed. 42 PD patients and 23 control subjects were monitored with Garmin Vivosmart 4 wearable device and asked to fill a symptom and medication diary with a mobile application, at home, for about four weeks. Subsequent analyses are based on continuous accelerometer data from the device. Accelerometer data from the Levodopa Response Study (MJFFd) were reanalyzed, with symptoms quantified with linear spectral models trained on expert evaluations present in the data. Variational autoencoders (VAE) were trained on both our study accelerometer data and on MJFFd to detect movement states (e.g., walking, standing). A total of 7590 self-reported symptoms were recorded during the study. 88.9% (32/36) of PD patients, 80.0% (4/5) of DBS PD patients and 95.5% (21/22) of control subjects reported that using the wearable device was very easy or easy. Recording a symptom at the time of the event was assessed as very easy or easy by 70.1% (29/41) of subjects with PD. Aggregated spectrograms of the collected accelerometer data show relative attenuation of low (<5Hz) frequencies in patients. Similar spectral patterns also separate symptom periods from immediately adjacent non-symptomatic periods. Discriminative power of linear models to separate symptoms from adjacent periods is weak, but aggregates show partial separability of patients vs. controls. The analysis reveals differential symptom detectability across movement tasks, motivating the third part of the study. VAEs trained on either dataset produced embedding from which movement states in MJFFd could be predicted. A VAE model was able to detect the movement states. Thus, a pre-detection of these states with a VAE from accelerometer data with good S/N ratio, and subsequent quantification of PD symptoms is a feasible strategy. The usability of the data collection method is important to enable the collection of self-reported symptom data by PD patients. Finally, the usability of the data collection method is important to enable the collection of self-reported symptom data by PD patients

Virtual environment application that complements the treatment of dyslexia (VEATD) in children

The educational disorders that children present at an early age can cause them to not fully develop throughout their lives. In this research work a 3D virtual system that allows the child who has been diagnosed with dyslexia to complement the exercises performed in a conventional therapy is described. To achieve this an application was developed, the app consists of two games (each with three levels of difficulty), and that are part of the rehabilitation program. In each of these games virtual objects are combined with auditory messages to provide the user with an immersive experience, and to train more than one sense at a time. In the first game task, the activity asks the children to correctly locate the syllables that compose a word and for the second activity the children will listen to a word, after the games asks the children to select the correct word. This tool has been tested by a group of children (eight), with ages ranging from 8 to 12 years old, whose development can be supervised at home by their parents, since it is an intuitive and easy to use interface. The results obtained are stored in a database and in this way the medical specialist can monitor the progress of the child throughout his treatment. For the validation of this proposal the SUS usability test was used. © Springer Nature Switzerland AG 2020