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In this report we present a dataset that combines multimodal biosignals and eye tracking information gathered under a human-computer interaction framework. The dataset was developed in the vein of the MAMEM project that aims to endow people with motor disabilities with the ability to edit and author multimedia content through mental commands and gaze activity. The dataset includes EEG, eye-tracking, and physiological (GSR and Heart rate) signals along with demographic, clinical and behavioral data collected from 36 individuals (18 able-bodied and 18 motor-impaired). Data were collected during the interaction with specifically designed interface for web browsing and multimedia content manipulation and during imaginary movement tasks. Alongside these data we also include evaluation reports both from the subjects and the experimenters as far as the experimental procedure and collected dataset are concerned. We believe that the presented dataset will contribute towards the development and evaluation of modern human-computer interaction systems that would foster the integration of people with severe motor impairments back into society.

The mean total score of PD paents on the CCSL scale was 22.7±6.9 Single items that scored high were relevant to interpersonal interacon, educaon, work and employment. The DICOS scale yielded a mean total score of 24.7± 10.0.Single items that had a significant impact on the whole score were speed of computer operaon and accuracy of performance. Caregivers’ mean scores on the CCSL and DICOS scales were similar to those of the paents (p=0.324).

Thirty five PD pts with a long experience in computer operation were included in the study. Their mean age was 59.5 (SD 8.27) years. Most of them were in Hoehn and Yahr stage II. PD pts uses, habits, and difficulties with the computer were explored by means of a structured interview which provided information in the form of yes/no answers to questions relevant to a wide range of usual computer uses and applications as well as difficutlties in performing various tasks relevant to computer operation. Two quantitative scales one referring to the contribution of the computer in social life, every day activities, emotional well-being (total score: 9=not important/45-very important) and the other exploring the disease impact on various aspects of computer operation (total score: 11=no effect/55 maximum effect) were also employed.

Three groups of neurological patients were studied:a)25 patients with Parkinson's disease (PD),b)23 patients with spinal cord injury (SCI) and c)19 with neuromuscular disorders ((NMD).All patients were assessed by means of two scales, one referring to the contribution of the computer in social life,everyday activities,emotional well-being (CCLS) [total score:9=not important/45=very important] and the other exploring the disease impact on various aspects of computer operation (DICOS) [total score:11=no effect/55=maximum effect]. Reliability of both scales was excellent (Cronbach's alpha was 0.87 for CCLS and 0.93 for DICOS).Between groups comparisons showed that NMD patients regarded conputer use as most important and SCI patients had the major difficulty.Mean total scores (SD) were as follows for a)CCLS:PD patients=23.28(7.22);SCI patients=20.78(9.72);NMD patients=32.84(5.12) [p=0.000] and b)DICOS:PD patients=25.9(9.9);SCI patients=31.22(15.0);NMD patients=20.53(5.15)[p=0.017]. Our preliminary results show that patients with motor disabilities regard computer use as an important aspect of their life and their disability has a significant effect in their ability to operate it satisfactorily.This information is important for the development of innovating technology helping patients to overcome their specific disabilities.

Health professionals with experience in the field of Parkinson Disease, neuromuscular conditions and tetraplegia following spinal cord injury, from three medical centers, from two countries, participated. We performed a literature survey, focusing on the characteristics of the study population, their computer and internet use habits, existing solutions, and specific challenges related to EEGs and EMs - based –computerassistive devices. We conducted three focus groups, with six health professionals per group. We also performed a qualitative analysis of the focus groups transcripts. The clinical requirements that resulted at the end of this phase have been then summarized, prioritized and coded with numbers from 1 (minimal) to 7 (maximal importance) by the health professionals from each sit

Objective: To provide clinically-corroborated evidence of the Parkinson’s disease (PD) diagnostic potential of machine learning-based approaches for motor symptoms severity inference via multimodal data, passively captured during the natural use of smartphones. Background: PD symptoms can be mild in the early stages and they usually go unnoticed, leaving the disease undiagnosed for years [1]. Subtle motor manifestations may start five to six years prior to PD clinical diagnosis and thereafter progress quickly [2]. Motor impairment affects daily activities and can severely impact patients’ quality over the course of the disease. Information derived from mobile electronic sensors can provide, via algorithmic transformation, objective and dense information of an individual’s motor status, allowing for frequent relevant symptoms early screening and subsequent monitoring. Methodology: We analyzed longitudinal recordings of tri-axial accelerometer, voice and keystroke timing data, captured passively from 70 PD patients and healthy controls (HC), in their daily life via the iPrognosis Android smartphone application, for relevant motor symptoms severity inference. The proposed methods for motor symptoms inference show promising PD diagnostic performance in our relatively small clinically-evaluated cohorts. Our results highlight the potential of evolving these methods into an objective PD screening/monitoring tool that could support clinical diagnosis, drug response assessment and decision-making. Passive capturing of the required input data further fosters evaluation of individuals’ natural behavior, as well as long-term adherence

Parkinson's Disease (PD) is a neurodegenerative disorder with early non-motor/motor symptoms that may evade clinical detection for years after the disease onset due to their mildness and slow progression. Digital health tools that process densely sampled data streams from the daily human-mobile interaction can objectify the monitoring of behavioral patterns that change due to the appearance of early PD-related signs. In this context, touchscreens can capture micro-movements of fingers during natural typing; an unsupervised activity of high frequency that can reveal insights for users’ fine-motor handling and identify motor impairment. Subjects' typing dynamics related to their fine-motor skills decline, unobtrusively captured from a mobile touchscreen, were recently explored in-the-clinic assessment to classify early PD patients and controls. In this study, estimation of individual fine motor impairment severity scores is employed to interpret the footprint of specific underlying symptoms (such as brady-/hypokinesia (B/H-K) and rigidity (R)) to keystroke dynamics that cause group-wise variations. Regression models are employed for each fine-motor symptom, by exploiting features from keystroke dynamics sequences from in-the-clinic data captured from 18 early PD patients and 15 healthy controls. Results show that R and B/H-K UPDRS Part III single items scores can be predicted with an accuracy of 78\% and 70\% respectively. The generalization power of these trained regressors derived from in-the-clinic data was further tested in a PD screening problem using data harvested in-the-wild for a longitudinal period of time ($mean\pm std: 7\pm 14$ weeks) via a dedicated smartphone application for unobtrusive sensing of their routine smartphone typing. From a pool of 210 active users, data from 13 self-reported PD patients and 35 controls were selected based on demographics matching with the ones in-the-clinic setting. The results have shown that the estimated index achieve {0.84(R),0.80(B/H-K) ROC AUC, respectively, with {sensitivity/specificity: 0.77/0.8(R), 0.92/0.63(B/H-K), on classifying PD and controls in-the-wild setting. Apparently, the proposed approach constitutes a step forward to unobtrusive remote screening and detection of specific early PD signs from mobile-based human-computer interaction, introduces an interpretable methodology for the medical community and contributes to the continuous improvement of deployed tools and technologies in-the-wild.