Domain Adaptation Methods for Lab-to-Field Human Context Recognition.

Human context recognition (HCR) using sensor data is a crucial task in Context-Aware (CA) applications in domains such as healthcare and security. Supervised machine learning HCR models are trained using smartphone HCR datasets that are scripted or gathered in-the-wild. Scripted datasets are most accurate because of their consistent visit patterns. Supervised machine learning HCR models perform well on scripted datasets but poorly on realistic data. In-the-wild datasets are more realistic, but cause HCR models to perform worse due to data imbalance, missing or incorrect labels, and a wide variety of phone placements and device types. Lab-to-field approaches learn a robust data representation from a scripted, high-fidelity dataset, which is then used for enhancing performance on a noisy, in-the-wild dataset with similar labels. This research introduces Triplet-based Domain Adaptation for Context REcognition (Triple-DARE), a lab-to-field neural network method that combines three unique loss functions to enhance intra-class compactness and inter-class separation within the embedding space of multi-labeled datasets: (1) domain alignment loss in order to learn domain-invariant embeddings; (2) classification loss to preserve task-discriminative features; and (3) joint fusion triplet loss. Rigorous evaluations showed that Triple-DARE achieved 6.3% and 4.5% higher F1-score and classification, respectively, than state-of-the-art HCR baselines and outperformed non-adaptive HCR models by 44.6% and 10.7%, respectively.

Malocclusion Among 10- to 12-Year-Old Male Schoolchildren in Qassim Region of Saudi Arabia: A Retrospective Epidemiological Study.

Objective To identify the prevalence of malocclusion in late mixed dentition in Qassim region of Saudi Arabia. This will be the first epidemiological study of its kind in this region. It will be very helpful for planning effective preventive measures and therapy programs. Materials and methods This study was performed in Qassim region, Saudi Arabia starting from October 2018 to March 2019. The examination was performed by two well-trained general dentists after using a specially prepared clinical examination form. A total of 536 children aged between 10 and 12 and those who met the inclusion criteria have been examined for Angle's relationship, overjet, overbite, crossbite, midline deviation and lip competent. Results Class I relation accounted for the highest percentage of the sample, whilst 31.3% presented with Class I ideal occlusion, and 48.9% Class I with malocclusion. This was followed by Class II malocclusion (12.5% of the sample), and Class III accounted for the lowest proportion (7.3%). Increased overjet was present in 34.4% of the sample, whereas 3.9% had edge-to-edge and 2.2% a reverse overjet. Regarding overbite, 39% reported increased overbite, whilst 3% had open bite. A total of 63 children presented with crossbite - 6.15% had anterior crossbite, 5% unilateral posterior, and 0.5% bilateral posterior. Regarding the midline, only visible and noticed deviation was recorded. The results showed that 90% had no deviation, while 10% had a deviated midline. Regarding lip competence, only 12.1% had an incompetent lip. Conclusion Early intervention and correction of occlusal discrepancies will facilitate the treatment and eliminate possible defects in developing dental arches.

Visual Analytics of Smartphone-Sensed Human Behavior and Health.

Smartphone health sensing tools, which analyze passively gathered human behavior data, can provide clinicians with a longitudinal view of their patients' ailments in natural settings. In this Visualization Viewpoints article, we postulate that interactive visual analytics (IVA) can assist data scientists during the development of such tools by facilitating the discovery and correction of wrong or missing user-provided ground-truth health annotations. IVA can also assist clinicians in making sense of their patients' behaviors by providing additional contextual and semantic information. We review the current state-of-the-art, outline unique challenges, and illustrate our viewpoints using our work as well as those of other researchers. Finally, we articulate open challenges in this exciting and emerging field of research.