Upper-limb paresis is the most common motor impairment post stroke. Current solutions to automate the assessment of upper-limb impairment impose a number of critical burdens on patients and their caregivers that preclude frequent assessment. In this work, we propose an approach to estimate upper-limb impairment in stroke survivors using two wearable inertial sensors, on the wrist and the sternum, and a minimally-burdensome motor task. Twenty-three stroke survivors with no, mild, or moderate upper-limb impairment performed two repetitions of one-to-two minute-long continuous, random (i.e., patternless), voluntary upper-limb movements spanning the entire range of motion. The three-dimensional time-series of upper-limb movements were segmented into a series of one-dimensional submovements by employing a unique movement decomposition technique. An unsupervised clustering algorithm and a supervised regression model were used to estimate Fugl-Meyer Assessment (FMA) scores based on features extracted from these submovements. Our regression model estimated FMA scores with a normalized root mean square error of 18.2% (r2=0.70) and needed as little as one minute of movement data to yield reasonable estimation performance. These results support the possibility of frequently monitoring stroke survivors’ rehabilitation outcomes, ultimately enabling the development of individually-tailored rehabilitation programs.

Individuals with cognitive impairments are evaluated using clinically validated cognitive assessment tools, which need to be administered by trained therapists. This serves as a major barrier for frequent and longitudinal monitoring of patients' cognitive impairment level. We introduce Neuro-World, a set of six mobile games designed to challenge visuospatial shortterm memory and selective attention, which allows one to selfadminister the assessment of his/her cognitive impairment level. Game performance is analyzed to estimate a widely-accepted clinical measure, the Mini Mental State Examination (MMSE), which highlights the translational impact of the system in realworld settings. We collected game-specific performance data from 12 post-stroke patients at baseline and a three-month follow-up, which were used to train supervised machine learning models to estimate the corresponding MMSE scores. The results presented herein show that the proposed approach can estimate the MMSE scores with a normalized root mean square error of 5.75%. We also validate the system's responsiveness to longitudinal changes in cognitive impairment level and demonstrate the system's positive usability in cognitively impaired individuals and their willingness to adhere to the longitudinal use. This study demonstrates that Neuro-world has great potential to be used to evaluate the cognitive impairment level and monitor its long-term change. This study enables new clinical and research opportunities for accurate, longitudinal assessment of cognitive function via mobile games.

Dog guides are favored by blind and low-vision (BLV) individuals for their ability to enhance independence and confidence by reducing safety concerns and increasing navigation efficiency compared to traditional mobility aids. However, only a relatively small proportion of BLV individuals work with dog guides due to their limited availability and associated maintenance responsibilities. There is considerable recent interest in addressing this challenge by developing legged guide dog robots. This study was designed to determine critical aspects of the handler-guide dog interaction and better understand handler needs to inform guide dog robot development. We conducted semi-structured interviews and observation sessions with 23 dog guide handlers and 5 trainers. Thematic analysis revealed critical limitations in guide dog work, desired personalization in handler-guide dog interaction, and important perspectives on future guide dog robots. Grounded on these findings, we discuss pivotal design insights for guide dog robots aimed for adoption within the BLV community.

Upper-limb impairments due to stroke can severely affect the quality of life in patients. Scientific evidence supports that repetitive rehabilitation exercises can improve motor ability in stroke patients. Rehabilitation games gained tremendous interest among researchers and clinicians because of their potential to make the seemingly mundane, enduring rehabilitation therapies more engaging. However, routine and longitudinal use of rehabilitation games in real-world clinical settings has not been investigated in depth. Particularly, we know little about current practices, challenges, and their potential impacts on therapeutic outcomes. To address this gap, we established a partnership with a rehabilitation hospital where game-assisted rehabilitation was routinely employed over a 2-year period. We then conducted an observational study, in which we observed 11 game-assisted therapy sessions and interviewed 15 therapists who moderated the therapy. Significant findings include (1) different engagement patterns of stroke patients in game-assisted therapy, (2) imperative roles of therapists in moderating games and challenges that therapists face during game-assisted therapy, and (3) lack of support for therapists in delivering patient-centered, personalized therapy to individual stroke patients. Furthermore, we discuss design implications for more effective rehabilitation game therapies that take into consideration both patients and therapists and their specific needs.

Previous cognitive training games for stroke survivors required the close supervision of therapists. We aim to demonstrate the preliminary therapeutic effectiveness of Neuro-World, serious mobile games for cognitive training, in chronic stroke survivors with mild-to-moderate cognitive impairment without therapist supervision. For that, we conducted a randomized, controlled clinical trial at a single long-term care rehabilitation center with 50 stroke survivors in the chronic stage with mild-to-moderate cognitive impairment. Participants were randomized to standard medical care (n = 25) or standard medical care plus administration of Neuro-World (n = 25) over 12 weeks. A two-way mixed model ANOVA and Tukey’s post hoc tests identified significant differences in outcomes between the experimental and the control groups at post-intervention but not at baseline. Within the experimental group, there were statistically significant improvements in all the outcomes except for the language category of the Mini-Mental State Examination and Digit Forward Span. The improvements were clinically significant for the total Mini-Mental State Examination, Digit Forward Span, and Digit Backward Span. Within the control group, there were no improvements in any of the outcomes. The practice of Neuro-World led to significant improvements in cognitive function and marginal mitigation of depressive symptoms in chronic stroke survivors with mild-to-moderate cognitive impairment.

Despite the harmful effect on health, e-cigarette and hookah smoking in youth in the U.S. has increased. Developing tailored e-cigarette and hookah cessation programs for youth is imperative. The aim of this study was to identify predictor variables such as social, mental, and environmental determinants that cause nicotine addiction in youth e-cigarette or hookah users and build nicotine addiction prediction models using machine learning algorithms. A total of 6511 participants were identified as ever having used e-cigarettes or hookah from the National Youth Tobacco Survey (2019) datasets. Prediction models were built by Random Forest with ReliefF and Least Absolute Shrinkage and Selection Operator (LASSO). ReliefF identified important predictor variables, and the Davies–Bouldin clustering evaluation index selected the optimal number of predictors for Random Forest. A total of 193 predictor variables were included in the final analysis. Performance of prediction models was measured by Root Mean Square Error (RMSE) and Confusion Matrix. The results suggested high performance of prediction. Identified predictor variables were aligned with previous research. The noble predictors found, such as ‘witnessed e-cigarette use in their household’ and ‘perception of their tobacco use’, could be used in public awareness or targeted e-cigarette and hookah youth education and for policymakers.