Parkinson’s disease (PD), the second most prevalent neurodegenerative condition, lacks a cure, but its symptoms can be managed. Its complex diagnosis and assessment need ongoing monitoring, highlighting the potential use of digital assessment tools for enhancing patient management, even outside the clinical settings. In this vein, this paper proposes a smartphone-based video analysis approach for assessing motor skills, particularly balance and posture, in individuals diagnosed with PD. In particular, the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) ratings for items “3.8” (leg agility), “3.9” (arising from chair),“3.13” (posture) and “3.10” (gait) are estimated by capturing and analysing video from PD patients, while performing a Comprehensive Motor Function Test. Specifically, a 3D pose landmark detection (skeleton extraction) model based on the the MediaPipe Machine Learning Platform is used and different motion features are estimated from the captured videos that may correlate with the MDS-UPDRS assessments provided by clinicians. A machine learning pipeline (evaluating five different ML classifiers) is then proposed to examine the feasibility of using these features for monitoring the balance and posture of PD patients. Experimental results, obtained using a cohort of 17 Greek PD patients, voluntarily participating in this study, demonstrate that certain features have significant correlation with the clinical MDS-UPDRS ratings. These promising results showcase the potentiality of digital assessment to provide objective representation of the PD patient’s motor skills, supporting both PD clinical assessment and self-management. Ongoing work within the AI-PROGNOSIS project will further validate these findings within a larger cohort and from additional countries.

ObjectiveA total of 48% of patients with Parkinson's disease (PD) present symptoms of gastrointestinal dysfunction, particularly constipation. Furthermore, gastrointestinal tract (GIT)-related non-motor symptoms (NMSs) appear at all stages of PD, can be prodromal by many years and have a relevant impact on the quality of life. There is a lack of GIT-focused validated tools specific to PD to assess their occurrence, progress, and response to treatment. The aim of this study was to develop and evaluate a novel, disease- and symptom-specific, self-completed questionnaire, titled Gut Dysmotility Questionnaire (GDQ), for screening and monitoring gastrointestinal dysmotility of the lower GIT in patients with PD.MethodsIn phase 1, a systematic literature review and multidisciplinary expert discussions were conducted. In phase 2, cognitive pretest studies comprising standard pretests, interviews, and evaluation questionnaires were performed in patients with PD (n = 21), age- and sex-matched healthy controls (HC) (n = 30), and neurologists (n = 11). Incorporating these results, a second round of cognitive pretests was performed investigating further patients with PD (n = 10), age- and sex-matched HC (n = 10), and neurologists (n = 5). The questionnaire was adapted resulting in the final GDQ, which underwent cross-cultural adaptation to the English language.ResultsWe report significantly higher GDQ total scores and higher scores in five out of eight domains indicating a higher prevalence of gastrointestinal dysmotility in patients with PD than in HC (p < 0.05). Cognitive pretesting improved the preliminary GDQ so that the final GDQ was rated as relevant (100/100%), comprehensive (100/90%), easy to understand concerning questions and answer options (100/90%), and of appropriate length (80/100%) by neurologists and patients with PD, respectively. The GDQ demonstrated excellent internal consistency (Cronbach‘s alpha value of 0.94). Evidence for good construct validity is given by moderate to high correlations of the GDQ total score and its domains by intercorrelations (rs = 0.67–0.91; p < 0.001) and with validated general NMS measures as well as with specific items that assess gastrointestinal symptoms.InterpretationThe GDQ is a novel, easy, and quick 18-item self-assessment questionnaire to screen for and monitor gastrointestinal dysmotility with a focus on constipation in patients with PD. It has shown high acceptance and efficacy as well as good construct validity in cognitive pretests.

Background and ObjectivesAutoantibodies against α3-subunit–containing nicotinic acetylcholine receptors (α3-nAChRs), usually measured by radioimmunoprecipitation assay (RIPA), are detected in patients with autoimmune autonomic ganglionopathy (AAG). However, low α3-nAChR antibody levels are frequently detected in other neurologic diseases with questionable significance. Our objective was to develop a method for the selective detection of the potentially pathogenic α3-nAChR antibodies, seemingly present only in patients with AAG.MethodsThe study involved sera from 55 patients from Greece, suspected for autonomic failure, and 13 patients from Italy diagnosed with autonomic failure, positive for α3-nAChR antibodies by RIPA. In addition, sera from 52 patients with Ca2+ channel or Hu antibodies and from 2,628 controls with various neuroimmune diseases were included. A sensitive live cell-based assay (CBA) with α3-nAChR–transfected cells was developed to detect antibodies against the cell-exposed α3-nAChR domain.ResultsTwenty-five patients were found α3-nAChR antibody positive by RIPA. Fifteen of 25 patients were also CBA positive. Of interest, all 15 CBA-positive patients had AAG, whereas all 10 CBA-negative patients had other neurologic diseases. RIPA antibody levels of the CBA-negative sera were low, although our CBA could detect dilutions of AAG sera corresponding to equally low RIPA antibody levels. No serum bound to control-transfected cells, and none of the 2,628 controls was α3-CBA positive.DiscussionThis study showed that in contrast to the established RIPA for α3-nAChR antibodies, which at low levels is of moderate disease specificity, our CBA seems AAG specific, while at least equally sensitive with the RIPA. This study provides Class II evidence that α3-nAChR CBA is a specific assay for AAG.Classification of EvidenceThis study provides Class II evidence that an α3-nAChR cell-based assay is a more specific assay for AAG than the standard RIPA.

OBJECTIVE:Parkinson's Disease (PD) is a progressive neurodegenerative disorder, manifesting with subtle early signs, which, often hinder timely and early diagnosis and treatment. The development of accessible, technology-based methods for longitudinal PD symptoms tracking in daily living, offers the potential for transforming disease assessment and accelerating diagnosis.METHODS:A privacy-aware method for classifying patients and healthy controls (HC), on the grounds of speech impairment present in PD, is proposed. Voice features from running speech signals were extracted from passively-captured recordings over voice calls. Language-aware training of multiple- and single-instance learning classifiers was employed to fuse and predict on voice features and demographic data from a multilingual cohort of 498 subjects (392/106 self-reported HC/PD patients).RESULTS:By means of leave-one-subject-out cross-validation, the best-performing models yielded 0.69/0.68/0.63/0.83 area under the Receiver Operating Characteristic curve (AUC) for the binary classification of PD patient vs. HC in sub-cohorts of English/Greek/German/Portuguese-speaking subjects, respectively. Out-of sample testing of the best performing models was conducted in an additional dataset, generated by 63 clinically-assessed subjects (24/39 HC/early PD patients). Testing has resulted in 0.84/0.93/0.83 AUC for the English/Greek/German-speaking sub-cohorts, respectively.CONCLUSIONS:The proposed approach outperforms other methods proposed for language-aware PD detection considering the ecological validity of the voice data.SIGNIFICANCE:This paper introduces for the first time a high-frequency, privacy-aware and unobtrusive PD screening tool based on analysis of voice samples captured during routine phone calls.

Human-Computer Interaction (HCI) and games set a new domain in understanding people's motivations in gaming, behavioral implications of game play, game adaptation to player preferences and needs for increased engaging experiences in the context of HCI serious games (HCI-SGs). When the latter relate with people's health status, they can become a part of their daily life as assistive health status monitoring/enhancement systems. Co-designing HCI-SGs can be seen as a combination of art and science that involves a meticulous collaborative process. The design elements in assistive HCI-SGs for Parkinson's Disease (PD) patients, in particular, are explored in the present work. Within this context, the Game-Based Learning (GBL) design framework is adopted here and its main game-design parameters are explored for the Exergames, Dietarygames, Emotional games, Handwriting games, and Voice games design, drawn from the PD-related i-PROGNOSIS Personalized Game Suite (PGS) (www.i-prognosis.eu) holistic approach. Two main data sources were involved in the study. In particular, the first one includes qualitative data from semi-structured interviews, involving 10 PD patients and four clinicians in the co-creation process of the game design, whereas the second one relates with data from an online questionnaire addressed by 104 participants spanning the whole related spectrum, i.e., PD patients, physicians, software/game developers. Linear regression analysis was employed to identify an adapted GBL framework with the most significant game-design parameters, which efficiently predict the transferability of the PGS beneficial effect to real-life, addressing functional PD symptoms. The findings of this work can assist HCI-SG designers for designing PD-related HCI-SGs, as the most significant game-design factors were identified, in terms of adding value to the role of HCI-SGs in increasing PD patients' quality of life, optimizing the interaction with personalized HCI-SGs and, hence, fostering a collaborative human-computer symbiosis.

Tuesday, April 28April 14, 2020Free AccessEvaluation of Gastric Motility of Parkinson’s Disease Patients Based on a Novel Wearable Device and Time-Frequency Analysis (1294)Vasileios Charisis, Stelios Hadjidimitriou, Dimitrios Iakovakis, Hugo Placido da Silva, Sevasti Bostantjopoulou-Kambouroglou, Zoe Katsarou, and Leontios HadjileontiadisAuthors Info & AffiliationsApril 14, 2020 issue94 (15_supplement)https://doi.org/10.1212/WNL.94.15_supplement.1294 Letters to the Editor

Being the second most common neurodegenerative disease, Parkinson's disease (PD) can be symptomatically treated, although, unfortunately, it cannot be cured yet. Moreover, diagnosing and assessing PD patients is a complex process, requiring continuous monitoring. In this vein, the design, development, and validation of innovative assessment tools may be helpful in the management of patients with PD, in particular. Based on intelligent ICT interventions, the i-PROGNOSIS project intends to mitigate PD's specific symptoms, such as neurological movement disorders of gait, balance, coordination, and posture, already characterized in the early phase of the disease. From this perspective, an innovative iPrognosis motor assessment tool is presented here, taking into consideration the Unified Parkinson Disease Rating Scale (UPDRS) Part III motor skills testing items, for evaluating the motor skills status. The efficiency of the proposed Assessment Tests to reflect the motor skills status, similarly to the UPDRS Part III items, was validated via 27 participants (18 males; mean age = 62 years, SD = 10.36 years; range, 43–79 years) with early (n = 10) and moderate (n = 17) PD who performed the Assessment Tests. Features from the latter were then correlated with the corresponding clinically assessed UPDRS Part III items, and statistically significant negative correlations (range, −0.364 to −0.802) were identified between the median values of the Assessment Tests and the UPDRS Part III items. In this vein, the iPrognosis Assessment Tests were integrated within the personalized interventions of the i-PROGNOSIS project, providing alternative means of assessing their effect on the PD patient's motor skills enhancement. The promising results presented here elaborate on the concept of using ICT-based assessment means to achieve comparable outcomes with the clinical standards in motor skills assessment.

Parkinson's Disease (PD) is the second most common neurodegenerative disorder with the non-motor symptoms preceding the motor impairment that is needed for clinical diagnosis. In the current study, an angle-based analysis that processes activity data during sleep from a smartwatch for quantification of sleep quality, when applied on controls and PD patients, is proposed. Initially, changes in their arm angle due to activity are captured from the smartwatch triaxial accelerometry data and used for the estimation of the corresponding binary state (awake/sleep). Then, sleep metrics (i.e., sleep efficiency index, total sleep time, sleep fragmentation index, sleep onset latency, and wake after sleep onset) are computed and used for the discrimination between controls and PD patients. A process of validation of the proposed approach when compared with the PSG-based ground truth in an in-the-clinic setting, resulted in comparable state estimation. Moreover, data from 15 early PD patients and 11 healthy controls were used as a test set, including 1,376 valid sleep recordings in-the-wild setting. The univariate analysis of the extracted sleep metrics achieved up to 0.77 AUC in early PD patients vs. healthy controls classification and exhibited a statistically significant correlation (up to 0.46) with the clinical PD Sleep Scale 2 counterpart Items. The findings of the proposed method show the potentiality to capture non-motor behavior from users' nocturnal activity to detect PD in the early stage.

The primary manifestations of Parkinson Disease (PD) concern abnormalities of movement associated with the constant deterioration of motor skills. Such motor impairment affects patients’ movement accuracy and coordination, disrupting their daily life. Taking into account recent studies stating that computer-based physical therapy games can be used as a PD rehabilitation option, we propose a novel Exergame, the iPrognosis Warming up Game (http://www.i-prognosis.eu/), as a user-friendly tool that could both serve as a computer-based physical therapy game, as well as a means of accurately and automatically identifying the severity of PD motor symptoms. To this regard, we propose a novel deep learning methodology for motor impairment stage prediction that relies solely on human body motion data extracted from the recorded game sessions. Experimental results using a dataset of both early and advanced PD patients reveal a good classification performance of the proposed methodology, predicting the motor impairment stage of PD patients and paving the way for additional research in the field.

Individuals with motor disabilities are marginalized and unable to keep up with the rest of the society in a digitized world with little opportunity for social inclusion. Specially designed electronic devices are required so as to enable patients to overcome their handicap and bypass the loss of their hand motor dexterity, which constitutes computer use impossible. The MAMEM's ultimate goal is to deliver technology in order to enable people with motor disabilities to operate the computer using interface channels that can be controlled through eye-movements and mental commands. Three groups of 10 patients with motor disabilities each were recruited to try the MAMEM platform at their home: patients diagnosed with high spinal cord injuries, patients with Parkinson's disease and patients with neuromuscular diseases. Patients had the MAMEM platform - including a built-in monitoring mechanism - at home for 1 month. Some of the participants used the platform extensively participating in social networks, while others did not use it that much. In general, patients with motor disabilities perceived the platform as a useful and satisfactory assistive device that enabled computer use and digital social activities.

Parkinson's disease (PD) is a chronic and progressive neurodegenerative disease that affects ~7 million people worldwide, without any cure to date; however, it can be symptomatically treated. In this vein, innovative technologies can be used for the objective assessment of clinical symptoms and to provide supportive therapies at home. The present work explores the processes and the outcomes of the i-PROGNOSIS (www.i-prognosis.eu) intervention deployment in three PD clinical centres (Greece, UK, and Germany). For that purpose, 36 PD patients were recruited to voluntarily participate in the i-PROGNOSIS feasibility study, spread across the three different countries. The PD patients interacted with the i-PROGNOSIS system for up-to-three months, mainly within the clinical environment, using the provided iPrognosis Games in dedicated gaming stations that were setup in the corresponding clinical centres. Overall, the results show that the iPrognosis Games were positively evaluated by medical experts. Moreover, based on the collected feedback, the iPrognosis Games have achieved their main goals of providing an innovative, objective and usable system for the monitoring of early PD (motor and non-motor) symptomatology, by providing tools for complementing existing clinical interventions for the improvement of PD patients' quality of life.

Fine-motor impairment (FMI) is progressively expressed in early Parkinson’s Disease (PD) patients and is now known to be evident in the immediate prodromal stage of the condition. The clinical techniques for detecting FMI may not be robust enough and here, we show that the subtle FMI of early PD patients can be effectively estimated from the analysis of natural smartphone touchscreen typing via deep learning networks, trained in stages of initialization and fine-tuning. In a validation dataset of 36,000 typing sessions from 39 subjects (17 healthy/22 PD patients with medically validated UPDRS Part III single-item scores), the proposed approach achieved values of area under the receiver operating characteristic curve (AUC) of 0.89 (95

Hypomimia, i.e. reduction in the expressiveness of the face, is a cardinal sign of the PD, often present at its early stages. Within the EU-funded i-Prognosis project (http://www.i-prognosis.eu), early and unobtrusive Parkinson's disease detection tests are developed, based on the interaction of users with everyday technological devices. The selfie analysis module translates facial expression features into an index reflecting the severity of PD hypomimia symptoms that affect the variability of patients' facial expressions. Monitoring of such an index over time holds the promise to detect the onset of hypomimia symptoms in an unobtrusive way. Our approach proposes a methodology for detecting and quantifying the progressive decrease of variability of facial expressions in early PD patients by analysing patterns emerging from photos (selfies) during daily life. Promising results are presented from both a) a small development set of 36 users (both PD patients and healthy controls) and b) a large set of selfie photos obtained from 1292 users that were analysed by the iPrognosis cloud analysis module.

Parkinson's disease (PD) is a progressive neurological disorder and the second most common age-related neurodegenerative disease after Alzheimer's disease. The primary symptoms of the disease are associated with the loss of motor skills affecting patients' movement and coordination and disrupting their daily life. Unfortunately, such motor symptoms cannot be fully relieved by therapeutic options. On the other hand, studies have shown that regular training and exercising can prove neuroprotective in PD patients helping them maintain independent longer. Based on recent studies stating that computer-based physical therapy games can be used as an option for facilitating PD rehabilitation exercise programs, we present the development of a body motion based videogame, using the Kinect sensor, targeted for PD patients. We tested twelve patients with advanced forms of PD motor symptoms (UPDRS motor score>20) and six initial stage PD patients (UPDRS motor score<20). All participants underwent an (UPDRS) motor skills pretest and afterwards performed three training sessions. In this paper, we will present part of our research aiming to analyze the movement patterns of PD patients in order to detect statistical significant differences between groups of different impairment level based on their UPDRS motor score and their performance. Consequently, we adopt a deep learning approach by analyzing the recorded human skeleton sequences for predicting the players' level of motor skills decline. Such methods and data can serve as preliminary evidence for further larger and controlled studies to propose such an exergame that can independently detect and adapt its difficulty level to better match players' ability providing a more targeted and personalized rehabilitation option.

Parkinson's Disease (PD) is the second most common neurodegenerative disorder worldwide, causing both motor and non-motor symptoms. In the early stages, symptoms are mild and patients may ignore their existence. As a result, they do not undergo any related clinical examination; hence delaying their PD diagnosis. In an effort to remedy such delay, analysis of data passively captured from user's interaction with consumer technologies has been recently explored towards remote screening of early PD motor signs. In the current study, a smartphone-based method analyzing subjects' finger interaction with the smartphone screen is developed for the quantification of fine-motor skills decline in early PD using Convolutional Neural Networks. Experimental results from the analysis of keystroke typing in-the-clinic data from 18 early PD patients and 15 healthy controls have shown a classification performance of 0.89 Area Under the Curve (AUC) with 0.79/0.79 sensitivity/specificity, respectively. Evaluation of the generalization ability of the proposed approach was made by its application on typing data arising from a separate self-reported cohort of 27 PD patients' and 84 healthy controls' daily usage with their personal smartphones (data in-the-wild), achieving 0.79 AUC with 0.74/0.78 sensitivity/specificity, respectively. The results show the potentiality of the proposed approach to process keystroke dynamics arising from users' natural typing activity to detect PD, which contributes to the development of digital tools for remote pathological symptom screening.

Hypomimia, i.e. reduction in the expressiveness of the face, is a cardinal sign of the PD, often present at its early stages. Within the EU-funded i-Prognosis project (http://www.i-prognosis.eu), early and unobtrusive Parkinson's disease detection tests are developed, based on the interaction of users with everyday technological devices. The selfie analysis module translates facial expression features into an index reflecting the severity of PD hypomimia symptoms that affect the variability of patients' facial expressions. Monitoring of such an index over time holds the promise to detect the onset of hypomimia symptoms in an unobtrusive way. Our approach proposes a methodology for detecting and quantifying the progressive decrease of variability of facial expressions in early PD patients by analysing patterns emerging from photos (selfies) during daily life. Promising results are presented from both a) a small development set of 36 users (both PD patients and healthy controls) and b) a large set of selfie photos obtained from 1292 users that were analysed by the iPrognosis cloud analysis module.

People with spinal cord injuries (SCI), and particularly with high level lesions, can potentially lose the ability to effectively operate computers. The Multimedia Authoring and Management using your Eyes and Mind (MAMEM) project aims to design and produce a novel assistive device to support computer use by individuals with SCI and other disabilities. The solution harnesses eye tracking and brain waves, as measured by encephalography (EEG), to manipulate common computer functions. This paper describes the first step in the project, during which we defined clinically related requirements of the assistive device. These definitions were based on data from three sources: (1) a narrative review; (2) a focus group of SCI rehabilitation professionals; and (3) structured questionnaires administrated to potential computer users with SCI, addressing computer-use habits, barriers, and needs. We describe both the collection of data from each source and the clinically related requirements extracted. The novel three-source requirement assessment method is discussed, and the advantages and disadvantages of each data source are reported. In conclusion, we suggest that this approach makes it possible to organize, discuss, and prioritize the requirements, and to create a work program while planning the device. This increases our level of certainty that the efficacy and adequacy of the assistive device will be maximized, in terms of the clinical needs of users.

May 9, 2019April 9, 2019Free AccessParkinsonian patients experiences operating the computer with their eyes: the MAMEM project. (P5.8-043)SEVASTI BOSTANTJOPOULOU-KAMPOUROGLOU, ZOE KATSAROU, MEIR PLOTNIK, GABI ZEILIG, IOANNIS DAGLIS, GEORGE LIAROS, FOTIOS KALAGANIS, KONSTANTINOS GEORGIADIS, YIANNIS KOMPATSIARIS, and SPIROS NIKOLOPOULOSAuthors Info & AffiliationsApril 9, 2019 issue92 (15_supplement)https://doi.org/10.1212/WNL.92.15_supplement.P5.8-043 Letters to the Editor