Background Wearable technologies have the potential to increase the quality of life and wellness for individuals with ASD and their families. However, there is a lack of research on WT for ASD and no research on understanding users. Thus, this interdisciplinary research was conducted to understand important design factors and preferred functions and design attributes for WT for ASD to guide the design process in the early-stages, and to develop and evaluate a WT prototype for ASD. Methods Individuals with ASD and their parents who are the potential users were recruited through purposive sampling. The data were analyzed through color-coding, major theme extraction, descriptive analysis, and a series of Welch's t-tests. A prototype was developed and evaluated based on the defined preferred functions and design attributes and design factors. Results First, the results about demographic backgrounds, prevalent symptoms, challenges in daily life, and user experiences related to WT were defined. Second, 12 important design factors of WT for ASD were identified. Third, individuals with ASD and their parents' preferred WT aspects on item types, functions, and design attributes, expected use frequency, use occasion, and data notification were identified. Lastly, a prototype was developed based on the results and evaluated for the future development of WT for ASD. Second, two groups were categorized according to the type of YOLO-disposition (high or low). Third, a well-being lifestyle based on a disposition type showed some significant differences in terms of both mental and physical health. Consumption value showed some significant differences in terms of differentiated value and social value. Conclusions The results are expected to help designersin the development process of WT for ASD and ultimately benefit individuals with ASD and their families and caregivers.
Convolutional Neural Networks (CNNs) exhibit remarkable performance in various machine learning tasks. As sensor-equipped Internet of Things (IoT) devices permeate into every aspect of modern life, the ability to execute CNN inference, a computationally intensive application, on resource constrained devices has become increasingly important. In this context, we present Cappuccino, a framework for synthesis of efficient inference software targeting mobile System-on-Chips (SoCs). We propose techniques for efficient parallelization of CNN inference targeting mobile SoCs, and explore the underlying tradeoffs. Experiments with different CNNs on three mobile devices demonstrate the effectiveness of our approach.
Non-invasive transabdominal fetal oximetry (TFO) has the potential to improve delivery outcomes by providing physicians with an objective metric of fetal well-being during labor. Fundamentally, the technology is based on sending light through the maternal abdomen to investigate deep fetal tissue, followed by detection and processing of the light that returns (via scattering) to the outside of the maternal abdomen. The placement of the photodetector in relation to the light source critically impacts TFO system performance, including its operational robustness in the face of fetal depth variation. However, anatomical differences between pregnant women cause the fetal depths to vary drastically, which further complicates the optical probe (optode) design optimization. In this paper, we present a methodology to solve this problem. We frame optode design space exploration as a multi-objective optimization problem, where hardware complexity (cost) and performance across a wider patient population (robustness) form competing objectives. We propose a model-based approach to characterize the Pareto-optimal points in the optode design space, through which a specific design is selected. Experimental evaluation via simulation and
in vivo
measurement on pregnant sheep support the efficacy of our approach.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.