Cychosz Margaret, Cristia Alejandrina
Department of Hearing and Speech Sciences, University of Maryland, College Park, MD, United States; Center for Comparative and Evolutionary Biology of Hearing, University of Maryland, College Park, MD, United States.
Laboratoire de Sciences Cognitives et de Psycholinguistique, Département d'études cognitives, ENS, EHESS, CNRS, PSL University, Paris, France.
Adv Child Dev Behav. 2022;62:1-36. doi: 10.1016/bs.acdb.2021.12.001. Epub 2022 Feb 17.
Big data are everywhere. In this chapter, we focus on one source: long-form, child-centered recordings collected using wearable technologies. Because these recordings are simultaneously unobtrusive and encompassing, they may be a breakthrough technology for clinicians and researchers from several diverse fields. We demonstrate this possibility by outlining three applications for the recordings-clinical treatment, large-scale interventions, and language documentation-where we see the greatest potential. We argue that incorporating these recordings into basic and applied research will result in more equitable treatment of patients, more reliable measurements of the effects of interventions on real-world behavior, and deeper scientific insights with less observational bias. We conclude by outlining a proposal for a semistructured online platform where vast numbers of long-form recordings could be hosted and more representative, less biased algorithms could be trained.
大数据无处不在。在本章中,我们聚焦于一个来源:使用可穿戴技术收集的以儿童为中心的长篇记录。由于这些记录既不引人注目又涵盖广泛,它们可能成为多个不同领域的临床医生和研究人员的突破性技术。我们通过概述这些记录在临床治疗、大规模干预和语言记录这三个应用领域的可能性来证明这一点,我们认为在这些领域中它们具有最大的潜力。我们认为,将这些记录纳入基础研究和应用研究将带来对患者更公平的治疗、对干预措施对现实世界行为影响的更可靠测量,以及减少观察偏差的更深入科学见解。我们最后概述了一个半结构化在线平台的提案,在该平台上可以托管大量长篇记录,并可以训练更具代表性、偏差更小的算法。