Computing and Information Systems - Research Publications
Now showing items 1-12 of 1935
Gamified Motor Training With Tangible Robots in Older Adults: A Feasibility Study and Comparison With the Young
(FRONTIERS MEDIA SA, 2020-04-03)
Background: An increasing lifespan and the resulting change in our expectations of later life stages are dependent on a good health state. This emphasizes the importance of the development of strategies to further strengthen healthy aging. One important aspect of good health in later life stages is sustained skilled motor function. Objective: Here, we tested the effectiveness of robotic upper limb motor training in a game-like scenario assessing game-based learning and its transfer potential. Methods: Thirty-six healthy participants (n = 18 elderly participants, n = 18 young controls) trained with a Pacman-like game using a hand-held Cellulo robot on 2 consecutive days. The game-related movements were conducted on a printed map displaying a maze and targets that had to be collected. Gradually, the task difficulty was adjusted between games by modifying or adding different game elements (e.g., speed and number of chasing ghosts, additional rules, and haptic feedback). Transfer was assessed by scoring simple robot manipulation on two different trajectories. Results: Elderly participants were able to improve their game performance over time [t (874) = 2.97, p < 0.01]. The applied game elements had similar effects on both age groups. Importantly, the game-based learning was transferable to simple robot manipulation that resembles activities of daily life. Only minor age-related differences were present (smaller overall learning gain and different effects of the wall-crash penalty rule in the elderly group). Conclusions: Gamified motor training with the Cellulo system has the potential to translate into an efficient and relatively low-cost robotic motor training tool for promoting upper limb function to promote healthy aging.
Iterative Design and Evaluation of a Tangible Robot-Assisted Handwriting Activity for Special Education
(FRONTIERS MEDIA SA, 2020-03-06)
In this article we investigate the role of interactive haptic-enabled tangible robots in supporting the learning of cursive letter writing for children with attention and visuomotor coordination issues. We focus on the two principal aspects of handwriting that are linked to these issues: Visual perception and visuomotor coordination. These aspects, respectively, enhance two features of letter representation in the learner's mind in particular, namely the shape (grapheme) and the dynamics (ductus) of the letter, which constitute the central learning goals in our activity. Building upon an initial design tested with 17 healthy children in a preliminary school, we iteratively ported the activity to an occupational therapy context in 2 different therapy centers, in the context of 3 different summer school camps involving a total of 12 children having writing difficulties. The various iterations allowed us to uncover insights about the design of robot-enhanced writing activities for special education, specifically highlighting the importance of ease of modification of the duration of an activity as well as of adaptable frequency, content, flow and game-play and of providing a range of evaluation test alternatives. Results show that the use of robot-assisted handwriting activities could have a positive impact on the learning of the representation of letters in the context of occupational therapy (V = 1, 449, p < 0.001, r = 0.42). Results also highlight how the design changes made across the iterations affected the outcomes of the handwriting sessions, such as the evaluation of the performances, monitoring of the performances, and the connectedness of the handwriting.
The transferability of handwriting skills: from the Cyrillic to the Latin alphabet
Do handwriting skills transfer when a child writes in two different scripts, such as the Latin and Cyrillic alphabets? Are our measures of handwriting skills intrinsically bound to one alphabet or will a child who faces handwriting difficulties in one script experience similar difficulties in the other script? To answer these questions, 190 children from grades 1-4 were asked to copy a short text using both the Cyrillic and Latin alphabets on a digital tablet. A recent change of policy in Kazakhstan gave us an opportunity to measure transfer, as the Latin-based Kazakh alphabet has not yet been introduced. Therefore, pupils in grade 1 had a 6-months experience in Cyrillic, and pupils in grades 2, 3, and 4 had 1.5, 2.5, and 3.5 years of experience in Cyrillic, respectively. This unique situation created a quasi-experimental situation that allowed us to measure the influence of the number of years spent practicing Cyrillic on the quality of handwriting in the Latin alphabet. The results showed that some of the differences between the two scripts were constant across all grades. These differences thus reflect the intrinsic differences in the handwriting dynamics between the two alphabets. For instance, several features related to the pen pressure on the tablet are quite different. Other features, however, revealed decreasing differences between the two scripts across grades. While we found that the quality of Cyrillic writing increased from grades 1-4, due to increased practice, we also found that the quality of the Latin writing increased as well, despite the fact that all of the pupils had the same absence of experience in writing in Latin. We can therefore interpret this improvement in Latin script as an indicator of the transfer of fine motor control skills from Cyrillic to Latin. This result is especially surprising given that one could instead hypothesize a negative transfer, i.e., that the finger controls automated for one alphabet would interfere with those required by the other alphabet. One interesting side-effect of these findings is that the algorithms that we developed for the diagnosis of handwriting difficulties among French-speaking children could be relevant for other alphabets, paving the way for the creation of a cross-lingual model for the detection of handwriting difficulties.
"It Is Not the Robot Who Learns, It Is Me." Treating Severe Dysgraphia Using Child-Robot Interaction
(FRONTIERS MEDIA SA, 2021-02-23)
Writing disorders are frequent and impairing. However, social robots may help to improve children's motivation and to propose enjoyable and tailored activities. Here, we have used the Co-writer scenario in which a child is asked to teach a robot how to write via demonstration on a tablet, combined with a series of games we developed to train specifically pressure, tilt, speed, and letter liaison controls. This setup was proposed to a 10-year-old boy with a complex neurodevelopmental disorder combining phonological disorder, attention deficit/hyperactivity disorder, dyslexia, and developmental coordination disorder with severe dysgraphia. Writing impairments were severe and limited his participation in classroom activities despite 2 years of specific support in school and professional speech and motor remediation. We implemented the setup during his occupational therapy for 20 consecutive weekly sessions. We found that his motivation was restored; avoidance behaviors disappeared both during sessions and at school; handwriting quality and posture improved dramatically. In conclusion, treating dysgraphia using child-robot interaction is feasible and improves writing. Larger clinical studies are required to confirm that children with dysgraphia could benefit from this setup.
A Comparison of Social Robot to Tablet and Teacher in a New Script Learning Context.
(Frontiers Media SA, 2020)
This research occurred in a special context where Kazakhstan's recent decision to switch from Cyrillic to the Latin-based alphabet has resulted in challenges connected to teaching literacy, addressing a rare combination of research hypotheses and technical objectives about language learning. Teachers are not necessarily trained to teach the new alphabet, and this could result in a challenge for children with learning difficulties. Prior research studies in Human-Robot Interaction (HRI) have proposed the use of a robot to teach handwriting to children (Hood et al., 2015; Lemaignan et al., 2016). Drawing on the Kazakhstani case, our study takes an interdisciplinary approach by bringing together smart solutions from robotics, computer vision areas, and educational frameworks, language, and cognitive studies that will benefit diverse groups of stakeholders. In this study, a human-robot interaction application is designed to help primary school children learn both a newly-adopted script and also its handwriting system. The setup involved an experiment with 62 children between the ages of 7-9 years old, across three conditions: a robot and a tablet, a tablet only, and a teacher. Based on the paradigm-learning by teaching-the study showed that children improved their knowledge of the Latin script by interacting with a robot. Findings reported that children gained similar knowledge of a new script in all three conditions without gender effect. In addition, children's likeability ratings and positive mood change scores demonstrate significant benefits favoring the robot over a traditional teacher and tablet only approaches.
Acquisition of handwriting in children with and without dysgraphia: A computational approach
(PUBLIC LIBRARY SCIENCE, 2020-09-11)
Handwriting is a complex skill to acquire and it requires years of training to be mastered. Children presenting dysgraphia exhibit difficulties automatizing their handwriting. This can bring anxiety and can negatively impact education. 280 children were recruited in schools and specialized clinics to perform the Concise Evaluation Scale for Children's Handwriting (BHK) on digital tablets. Within this dataset, we identified children with dysgraphia. Twelve digital features describing handwriting through different aspects (static, kinematic, pressure and tilt) were extracted and used to create linear models to investigate handwriting acquisition throughout education. K-means clustering was performed to define a new classification of dysgraphia. Linear models show that three features only (two kinematic and one static) showed a significant association to predict change of handwriting quality in control children. Most kinematic and statics features interacted with age. Results suggest that children with dysgraphia do not simply differ from ones without dysgraphia by quantitative differences on the BHK scale but present a different development in terms of static, kinematic, pressure and tilt features. The K-means clustering yielded 3 clusters (Ci). Children in C1 presented mild dysgraphia usually not detected in schools whereas children in C2 and C3 exhibited severe dysgraphia. Notably, C2 contained individuals displaying abnormalities in term of kinematics and pressure whilst C3 regrouped children showing mainly tilt problems. The current results open new opportunities for automatic detection of children with dysgraphia in classroom. We also believe that the training of pressure and tilt may open new therapeutic opportunities through serious games.
Automated human-level diagnosis of dysgraphia using a consumer tablet
(NATURE RESEARCH, 2018-08-31)
The academic and behavioral progress of children is associated with the timely development of reading and writing skills. Dysgraphia, characterized as a handwriting learning disability, is usually associated with dyslexia, developmental coordination disorder (dyspraxia), or attention deficit disorder, which are all neuro-developmental disorders. Dysgraphia can seriously impair children in their everyday life and require therapeutic care. Early detection of handwriting difficulties is, therefore, of great importance in pediatrics. Since the beginning of the 20th century, numerous handwriting scales have been developed to assess the quality of handwriting. However, these tests usually involve an expert investigating visually sentences written by a subject on paper, and, therefore, they are subjective, expensive, and scale poorly. Moreover, they ignore potentially important characteristics of motor control such as writing dynamics, pen pressure, or pen tilt. However, with the increasing availability of digital tablets, features to measure these ignored characteristics are now potentially available at scale and very low cost. In this work, we developed a diagnostic tool requiring only a commodity tablet. To this end, we modeled data of 298 children, including 56 with dysgraphia. Children performed the BHK test on a digital tablet covered with a sheet of paper. We extracted 53 handwriting features describing various aspects of handwriting, and used the Random Forest classifier to diagnose dysgraphia. Our method achieved 96.6% sensibility and 99.2% specificity. Given the intra-rater and inter-rater levels of agreement in the BHK test, our technique has comparable accuracy for experts and can be deployed directly as a diagnostics tool.
Effectiveness of WhatsApp online group discussion for smoking relapse prevention: protocol for a pragmatic randomized controlled trial.
BACKGROUND AND AIMS: Sustained psychosocial support via online social groups may help former tobacco users maintain abstinence. This study aims to examine the effectiveness of participating in a WhatsApp social group for long-term smoking cessation. DESIGN: Two-arm, open-labelled, pragmatic, individually randomized controlled trial. SETTING: All participants are service users of smoking cessation clinics, and all interventions are delivered via mobile phones. PARTICIPANTS: Participants included 1008 adult quitters who self-report no tobacco use in the past 3-30 days. INTERVENTIONS: The intervention group (n = 504) will join a WhatsApp social group to receive standardized and theory-based reminders of smoking relapse prevention and participate in discussion with other WhatsApp group members using their own mobile phones. All social groups will be led by counselors or specialist nurse practitioners. The control group (n = 504) will receive similar reminders via short messages to their own mobile phones but will not interact with other participants. The intervention duration for both groups is 8 weeks. Both groups will receive a booklet at baseline about how to prevent smoking relapse. MEASUREMENTS: The primary outcome is biochemically validated tobacco abstinence at 12 months after consent. COMMENTS: The findings will provide evidence concerning the utility of operating online social group discussion for prevention of smoking relapse and sustaining long-term abstinence.
The PsyTAR dataset: From patients generated narratives to a corpus of adverse drug events and effectiveness of psychiatric medications.
(Elsevier BV, 2019-06)
The "Psychiatric Treatment Adverse Reactions" (PsyTAR) dataset contains patients' expression of effectiveness and adverse drug events associated with psychiatric medications. The PsyTAR was generated in four phases. In the first phase, a sample of 891 drugs reviews posted by patients on an online healthcare forum, "askapatient.com", was collected for four psychiatric drugs: Zoloft, Lexapro, Cymbalta, and Effexor XR. For each drug review, patient demographic information, duration of treatment, and satisfaction with the drugs were reported. In the second phase, sentence classification, drug reviews were split to 6009 sentences, and each sentence was labeled for the presence of Adverse Drug Reaction (ADR), Withdrawal Symptoms (WDs), Sign/Symptoms/Illness (SSIs), Drug Indications (DIs), Drug Effectiveness (EF), Drug Infectiveness (INF), and Others (not applicable). In the third phases, entities including ADRs (4813 mentions), WDs (590 mentions), SSIs (1219 mentions), and DIs (792 mentions) were identified and extracted from the sentences. In the four phases, all the identified entities were mapped to the corresponding UMLS Metathesaurus concepts (916) and SNOMED CT concepts (755). In this phase, qualifiers representing severity and persistency of ADRs, WDs, SSIs, and DIs (e.g., mild, short term) were identified. All sentences and identified entities were linked to the original post using IDs (e.g., Zoloft.1, Effexor.29, Cymbalta.31). The PsyTAR dataset can be accessed via Online Supplement #1 under the CC BY 4.0 Data license. The updated versions of the dataset would also be accessible in https://sites.google.com/view/pharmacovigilanceinpsychiatry/home.
BioCaster: detecting public health rumors with a Web-based text mining system.
(Oxford University Press (OUP), 2008-12-15)
SUMMARY: BioCaster is an ontology-based text mining system for detecting and tracking the distribution of infectious disease outbreaks from linguistic signals on the Web. The system continuously analyzes documents reported from over 1700 RSS feeds, classifies them for topical relevance and plots them onto a Google map using geocoded information. The background knowledge for bridging the gap between Layman's terms and formal-coding systems is contained in the freely available BioCaster ontology which includes information in eight languages focused on the epidemiological role of pathogens as well as geographical locations with their latitudes/longitudes. The system consists of four main stages: topic classification, named entity recognition (NER), disease/location detection and event recognition. Higher order event analysis is used to detect more precisely specified warning signals that can then be notified to registered users via email alerts. Evaluation of the system for topic recognition and entity identification is conducted on a gold standard corpus of annotated news articles. AVAILABILITY: The BioCaster map and ontology are freely available via a web portal at http://www.biocaster.org.
Exploring Physician Attitudes Regarding Electronic Documentation of E-cigarette Use: A Qualitative Study.
(SAGE Publications, 2018)
Background: In this article, we present qualitative work designed to explore physicians' attitudes toward and knowledge of electronic cigarettes (or Electronic Nicotine Delivery Systems-ENDS), particularly focusing on personal attitudes held by physicians regarding ENDS use, physician beliefs regarding the relative safety of ENDS, attitudes regarding the efficacy of ENDS as a smoking cessation tool, and how physicians' document ENDS use in the electronic health record (EHR). Methods: We completed a total of 17 semistructured qualitative interviews with physicians in 4 different outpatient clinic locations. Clinics were selected with the goal of reaching patient panels across a diversity of socioeconomic and local geographic locations. Results: The findings from our qualitative analysis suggest that physicians feel uninformed about the long-term health risks of ENDS and believe that they lack the critical medical knowledge required for discussing ENDS with their patients who smoke. Although physician responses did not endorse the view that ENDS use is a safer alternative to combustible tobacco use, approximately one-third of our physician sample did not hold strong objections to ENDS usage. Physicians placed varying degrees of importance on the issue of ENDS documentation practices. Discussion: Three overarching themes were revealed from our analysis. These themes included (1) physicians' attitudes regarding the use of ENDS for smoking cessation, (2) physicians' guidance and advisement to patients in the use of ENDS for smoking cessation, and (3) current practices of clinical documentation of ENDS use in an EHR. Our qualitative results indicate that physicians in our study rarely screen patients for ENDS use, even for those patients who are both documented smokers and recipients of physician-led tobacco cessation counseling. However, most physicians agreed that the prospect of creating a structured data field specifically for the documentation of ENDS use within the EHR would result in the likelihood of increased screening and documentation of ENDS use patterns.