Development and validation of a progressive web application to educate partial denture wearers

Effective delivery of post-insertion instructions is essential for denture care and oral health. This study aimed to develop a progressive web application (PWA) to educate patients’ chairside and serve as a reference material. A need analysis was conducted before prototype development. Subsequently, the prototype was subjected to content verification, design appraisal and usability testing. The results of usability testing revealed a user task success rate of 94.4%, with an adjusted Wald 95% confidence interval of 83–100%. User satisfaction assessed using the Single Ease Questionnaire and System Usability Score reported a mean score of 6.13 (95% CI: 5.69–6.55) and 85.9 (95% CI: 82.2–89.6), respectively, indicating good usability. This study highlights the systematic approach of developing an evidence-based educational PWA to meet the usability standards for mobile applications. This PWA is useful in clinical studies to explore mobile technologies’ potential in educating denture wearers, especially in the older population.

Use of surgical video–based automated performance metrics to predict blood loss and success of simulated vascular injury control in neurosurgery: a pilot study

OBJECTIVE Experts can assess surgeon skill using surgical video, but a limited number of expert surgeons are available. Automated performance metrics (APMs) are a promising alternative but have not been created from operative videos in neurosurgery to date. The authors aimed to evaluate whether video-based APMs can predict task success and blood loss during endonasal endoscopic surgery in a validated cadaveric simulator of vascular injury of the internal carotid artery. METHODS Videos of cadaveric simulation trials by 73 neurosurgeons and otorhinolaryngologists were analyzed and manually annotated with bounding boxes to identify the surgical instruments in the frame. APMs in five domains were defined—instrument usage, time-to-phase, instrument disappearance, instrument movement, and instrument interactions—on the basis of expert analysis and task-specific surgical progressions. Bounding-box data of instrument position were then used to generate APMs for each trial. Multivariate linear regression was used to test for the associations between APMs and blood loss and task success (hemorrhage control in less than 5 minutes). The APMs of 93 successful trials were compared with the APMs of 49 unsuccessful trials. RESULTS In total, 29,151 frames of surgical video were annotated. Successful simulation trials had superior APMs in each domain, including proportionately more time spent with the key instruments in view (p < 0.001) and less time without hemorrhage control (p = 0.002). APMs in all domains improved in subsequent trials after the participants received personalized expert instruction. Attending surgeons had superior instrument usage, time-to-phase, and instrument disappearance metrics compared with resident surgeons (p < 0.01). APMs predicted surgeon performance better than surgeon training level or prior experience. A regression model that included APMs predicted blood loss with an R2 value of 0.87 (p < 0.001). CONCLUSIONS Video-based APMs were superior predictors of simulation trial success and blood loss than surgeon characteristics such as case volume and attending status. Surgeon educators can use APMs to assess competency, quantify performance, and provide actionable, structured feedback in order to improve patient outcomes. Validation of APMs provides a benchmark for further development of fully automated video assessment pipelines that utilize machine learning and computer vision.

A New Look at Infant Problem-Solving: Using DeepLabCut to Investigate Exploratory Problem-Solving Approaches

When confronted with novel problems, problem-solvers must decide whether to copy a modeled solution or to explore their own unique solutions. While past work has established that infants can learn to solve problems both through their own exploration and through imitation, little work has explored the factors that influence which of these approaches infants select to solve a given problem. Moreover, past work has treated imitation and exploration as qualitatively distinct, although these two possibilities may exist along a continuum. Here, we apply a program novel to developmental psychology (DeepLabCut) to archival data (Lucca et al., 2020) to investigate the influence of the effort and success of an adult’s modeled solution, and infants’ firsthand experience with failure, on infants’ imitative versus exploratory problem-solving approaches. Our results reveal that tendencies toward exploration are relatively immune to the information from the adult model, but that exploration generally increased in response to firsthand experience with failure. In addition, we found that increases in maximum force and decreases in trying time were associated with greater exploration, and that exploration subsequently predicted problem-solving success on a new iteration of the task. Thus, our results demonstrate that infants increase exploration in response to failure and that exploration may operate in a larger motivational framework with force, trying time, and expectations of task success.

The Joint Log-Lift Task: A Social Foraging Paradigm

Behavioural cooperation is under intense research. Yet, popular experimental paradigms often employ artificial tasks, require training, or do not permit partner choice, possibly limiting their biological relevance. We developed the joint log-lift task, a social foraging paradigm in which animals have to jointly lift a log to each obtain a food reward. The task relies on an obligate strategy, meaning that the only way to benefit is to work jointly. We hypothesised that (1) animals learn to spontaneously solve the task, and that (2) kin and (3) more sociable individuals would engage more often together in the task and achieve greater success than non-kin and less sociable individuals, respectively. We presented the task to 8 groups of juvenile domestic pigs (Sus scrofa domesticus) in their home pen for 30 min daily. Over the course of 9 days, the pigs showed evidence of learning by progressively switching from individual to joint behaviours, leading to 68% (62 out of 91 pigs) spontaneously solving the task. Success was influenced by sociability, but not kinship. There were large differences in success among dyads, hinting at the possible role of social dynamics and inter-individual differences in the ability and/or motivation to solve the task. The joint log-lift task allows researchers to investigate spontaneous cooperative tendencies of individuals, dyads and groups in the home environment through ad libitum engagement with the apparatus. This ecologically relevant paradigm opens the way to investigate social foraging experimentally at large scale, by giving animals free choice about when and with whom to work jointly.

User Satisfaction Reward Estimation Across Domains: Domain-independent Dialogue Policy Learning

Learning suitable and well-performing dialogue behaviour in statistical spoken dialogue systems has been in the focus of research for many years. While most work that is based on reinforcement learning employs an objective measure like task success for modelling the reward signal, we propose to use a reward signal based on user satisfaction. We propose a novel estimator and show that it outperforms all previous estimators while learning temporal dependencies implicitly. We show in simulated experiments that a live user satisfaction estimation model may be applied resulting in higher estimated satisfaction whilst achieving similar success rates. Moreover, we show that a satisfaction estimation model trained on one domain may be applied in many other domains that cover a similar task. We verify our findings by employing the model to one of the domains for learning a policy from real users and compare its performance to policies using user satisfaction and task success acquired directly from the users as reward.

Robot Motion Planner for Under-Constrained Trajectories With Part-Specific Geometric Variances

Industrial manipulators often interact with large and complex objects for a variety of automation tasks. Finding a feasible path for the robot end-effector that ensures task success is often non-trivial due to considerations such as reachability, singularity avoidance, and collision avoidance. This paper proposes an approach to expand the search space for feasible robot trajectories (and search for an optimal solution) by taking advantage of task redundancy for certain tasks while ensuring task completion. The effort builds on previous work enabling virtual fixture generation for complex shapes given CAD or scan data. The proposed method has been developed into a trajectory planning library on the ROS (Robot Operating System) framework and tested by simulating an interaction of a six-axis industrial robot with an aircraft fuselage. Results show increased coverage of task area with minimal robot base placements.

Technological problem solving: an investigation of differences associated with levels of task success

Research into technological problem solving has shown it to exist in a range of forms and draw upon different processes and knowledge types. This paper adds to this understanding by identifying procedural and epistemic differences in relation to task performance for pupils solving a well-defined technological problem. The study is theoretically grounded in a transformative epistemology of technology education. 50 pupils in small groups worked through a cantilever problem, the most and least successful solutions to which were identified using a Delphi technique. Time-interval photography, verbal interactions, observations and supplementary data formed a composite representation of activity which was analysed with successively less contrasting groups to isolate sustained differences. Analyses revealed key differences in three areas. First, more successful groups used better and more proactive process-management strategies including use of planning, role and task allocation with lower levels of group tension. Second, they made greater use of reflection in which knowledge associated with the technological solution was explicitly verblised. This was defined as ‘analytical reflection’ and reveals aspects of pupils’ qualitative technical knowledge. Third, higher-performing groups exhibited greater levels of tacit-procedural knowledge within their solutions. There was also evidence that less successful groups were less affected by competition and not as comprehensive in translating prior conceptual learning into their tangible technological solutions. Overall findings suggest that proactive management, and making contextual and technical connections, are important for pupils solving well-defined technological problems. This understanding can be used to support classroom pedagogies that help pupils learn to problem solve more effectively.

Hierarchical motor adaptations negotiate failures during force field learning

Humans have the amazing ability to learn the dynamics of the body and environment to develop motor skills. Traditional motor studies using arm reaching paradigms have viewed this ability as the process of ‘internal model adaptation’. However, the behaviors have not been fully explored in the case when reaches fail to attain the intended target. Here we examined human reaching under two force fields types; one that induces failures (i.e., target errors), and the other that does not. Our results show the presence of a distinct failure-driven adaptation process that enables quick task success after failures, and before completion of internal model adaptation, but that can result in persistent changes to the undisturbed trajectory. These behaviors can be explained by considering a hierarchical interaction between internal model adaptation and the failure-driven adaptation of reach direction. Our findings suggest that movement failure is negotiated using hierarchical motor adaptations by humans.