Abstract
Wearable devices have been showing promising results in a large range of applications: since industry, to entertainment and, in particular, healthcare. In the scope of movement disorders, wearable devices are being widely implemented for motor symptoms objective assessment. Currently, clinicians evaluate patients' motor symptoms resorting to subjective scales and visual perception, such as in Parkinson's Disease. The possibility to make use of wearable devices to quantify this disorder motor symptoms would bring an accurate follow-up on the disease progression, leading to more efficient treatments. Here we present a novel textile embedded low-power wearable device capable to be used in any scenario of movement disorders assessment due to its seamless, comfort and versatility. Regarding our research, it has already improved the setup of a wrist rigidity quantification system for Parkinson's Disease patients: the iHandU system. The wearable comprises a hardware sensing unit integrated in a textile band with an innovative design assuring higher comfort and easiness-to-use in movement disorders assessment. It enables to collect inertial data (9-axis) and has the possibility to integrate two analog sensors. A web platform was developed for data reading, visualization and recording. To ensure inertial data reliability, validation tests for the accelerometer and gyroscope sensors were conducted by comparison with its theoretical behavior, obtaining very good results.

Abstract
The main goal of this systematic review is to synthesize existing evidence on the use of immersive virtual reality (IVR) to train professionals as well as to identify the main gaps and challenges that still remain and need to be addressed by future research. Following a comprehensive search, 66 documents were identified, assessed for relevance, and analysed. The main areas of application of IVR-based training were identified. Moreover, we identified the stimuli provided, the hardware used and information regarding training evaluation. The results showed that the areas in which a greater number of works were published were those related to healthcare and elementary occupations. In hardware, the most commonly used equipment was head mounted displays (HMDs), headphones included in the HMDs and handheld controllers. Moreover, the results indicated that IVR training systems are often evaluated manually, the most common metric being questionnaires applied before and after the experiment, and that IVR training systems have a positive effect in training professionals. We conclude that the literature is insufficient for determining the effect of IVR in the training of professionals. Although some works indicated promising results, there are still relevant themes that must be explored and limitations to overcome before virtual training replaces real-world training.

Abstract
Cathodal transcranial direct current stimulation (c-tDCS) is a non-invasive option for treatment of refractory epilepsy. However, it is still unknown whether this therapy has a positive stabilizing effect on the vegetative function of these patients. Heart Rate Variability (HRV) is considered an efficient tool to monitor the cardiac autonomic system, which has been correlated with the risk of Sudden Unexpected Death in Epilepsy (SUDEP). In this study, changes in HRV are investigated after c-tDCS of six patients (34.50 +/- 11.10 years) with refractory epilepsy, which have been selected at the University Hospital, LMU Munich. Patients were categorized as responders (n=2), non-responders (n=3) and uncategorized (n=1). We analyzed 24 hours of electrophysiological data recorded before and after treatment, and computed HRV metrics (AVNN, SDNN, RMSD, pNN20, pNN50, LH/HF, 0V, 1V, 2LV, 2UV, SD1 and SD2). All patients revealed a change in almost all HRV metrics post stimulation. Grouped all patients, there was a significant (p<0.05) change in RMSSD, pNN50, SD1 and LH/HF. For responders there was an increase in all time domain and nonlinear metrics, which was not seen for non-responders. These results suggest that tDCS exerts significant changes in cardiovascular autonomic system in patients with refractory epilepsy. HRV metrics may also serve as biomarkers of the response to tDCS stimulation. A larger dataset is being gathered for further analysis.

Abstract
Epilepsy is a major neurological disorder affecting approximately 1% of the world population, where seizure semiology is an essential tool for clinical evaluation of seizures. This includes qualitative visual inspection of videos from the seizures in epilepsy monitoring units by epileptologists. In order to support this clinical diagnosis process, promising deep learning-based systems were proposed. However, these indicate that video datasets of epileptic seizures are still rare and limited in size. In order to enable the full potential of AI systems for epileptic seizure diagnosis support and research, a novel collaborative development framework is proposed for a scalable DL-assisted clinical research and diagnosis support of epileptic seizures. The designed cloud-based approach integrates our deployed and tested NeuroKinect data acquisition pipeline into an MLOps framework to scale data set extension and analysis to a multi-clinical utilization. The proposed development framework incorporates an MLOps approach, to ensure convenient collaboration between clinicians and data scientists, providing continuous advantages to both user groups. It addresses methods for efficient utilization of HW, SW and human resources. In the future, the system is going to be expanded with several AI-based tools. Such as DL-based automated 3D motion capture (MoCap), 3D movement analysis support, quantitative seizure semiology analysis tools, video-based MOI and seizure classification. © 2021 IEEE

Abstract
Adaptive Deep Brain Stimulation has recently emerged to tackle conventional DBS limitations by measuring disease fluctuations and to adapt stimulation parameter accordingly. In early 2020, Medtronic launched in the European Union the first certified DBS neurostimulator capable of simultaneously stimulate and read signals from the deep brain structures, the PerceptTMPC. In epilepsy, the most common target brain structure is the Anterior Nucleus of Thalamus and the Local Field Potentials analysis requires prior synchronization of data recorded from the Percept PC with video-Electroencephalography (vEEG) equipment. Fine-grained synchronization (sub-second resolution) is mandatory for multimodal neurodata analysis and may be achieved by aligning artefacts perceived in both systems. In this work we study two methods aiming for neurodata streams clock synchronization: one based on DBS stimulation artefacts and another on tapping maneuver artefacts. For this purpose, we studied the data collected from the first epileptic patient that underwent 1-week vEEG-PerceptTMPC monitoring at a Hospital monitoring unit. We found that tapping maneuver-based methodology allowed a more accurate synchronization in relation to the stimulation artefact-based method (0.56s vs. 2.07s absolute average uncertainty). This method was also more complete one since tapping timestamps can be determined by video timeframes and do not require a prior identification of artefacts in EEG data by clinicians.

Abstract
Hereditary Transthyretin Amyloidosis (vATTR-V30M) is a rare and highly incapacitating sensorimotor neuropathy caused by an inherited mutation (Val30Met), which typically affects gait, among other symptoms. In this context, we investigated the possibility of using machine learning (ML) techniques to build a model(s) that can be used to support the detection of the Val30Met mutation (possibility of developing the disease), as well as symptom onset detection for the disease, given the gait characteristics of a person. These characteristics correspond to 24 gait parameters computed from 3-D body data, provided by a Kinect v2 camera, acquired from a person while walking towards the camera. To build the model(s), different ML algorithms were explored: k-nearest neighbors, decision tree, random forest, support vector machines (SVM), and multilayer perceptron. For a dataset corresponding to 66 subjects (25 healthy controls, 14 asymptomatic mutation carriers, and 27 patients) and several gait cycles per subject, we were able to obtain a model that distinguishes between controls and vATTR-V30M mutation carriers (with or without symptoms) with a mean accuracy of 92% (SVM). We also obtained a model that distinguishes between asymptomatic and symptomatic carriers with a mean accuracy of 98% (SVM). These results are very relevant, since this is the first study that proposes a ML approach to support vATTR-V30M patient assessment based on gait, being a promising foundation for the development of a computer-aided diagnosis tool to help clinicians in the identification and follow-up of this disease. Furthermore, the proposed method may also be used for other neuropathies.