Herbuela Von Ralph Dane Marquez, Karita Tomonori, Furukawa Yoshiya, Wada Yoshinori, Yagi Yoshihiro, Senba Shuichiro, Onishi Eiko, Saeki Tatsuo
Department of Special Needs Education, Graduate School of Education, Ehime University, Matsuyama, Ehime, Japan.
Graduate School of Humanities and Social Sciences, Hiroshima University, Higashihiroshima, Hiroshima, Japan.
JMIR Rehabil Assist Technol. 2021 Jun 7;8(2):e28020. doi: 10.2196/28020.
Children with profound intellectual and multiple disabilities (PIMD) or severe motor and intellectual disabilities (SMID) only communicate through movements, vocalizations, body postures, muscle tensions, or facial expressions on a pre- or protosymbolic level. Yet, to the best of our knowledge, there are few systems developed to specifically aid in categorizing and interpreting behaviors of children with PIMD or SMID to facilitate independent communication and mobility. Further, environmental data such as weather variables were found to have associations with human affects and behaviors among typically developing children; however, studies involving children with neurological functioning impairments that affect communication or those who have physical and/or motor disabilities are unexpectedly scarce.
This paper describes the design and development of the ChildSIDE app, which collects and transmits data associated with children's behaviors, and linked location and environment information collected from data sources (GPS, iBeacon device, ALPS Sensor, and OpenWeatherMap application programming interface [API]) to the database. The aims of this study were to measure and compare the server/API performance of the app in detecting and transmitting environment data from the data sources to the database, and to categorize the movements associated with each behavior data as the basis for future development and analyses.
This study utilized a cross-sectional observational design by performing multiple single-subject face-to-face and video-recorded sessions among purposively sampled child-caregiver dyads (children diagnosed with PIMD/SMID, or severe or profound intellectual disability and their primary caregivers) from September 2019 to February 2020. To measure the server/API performance of the app in detecting and transmitting data from data sources to the database, frequency distribution and percentages of 31 location and environment data parameters were computed and compared. To categorize which body parts or movements were involved in each behavior, the interrater agreement κ statistic was used.
The study comprised 150 sessions involving 20 child-caregiver dyads. The app collected 371 individual behavior data, 327 of which had associated location and environment data from data collection sources. The analyses revealed that ChildSIDE had a server/API performance >93% in detecting and transmitting outdoor location (GPS) and environment data (ALPS sensors, OpenWeatherMap API), whereas the performance with iBeacon data was lower (82.3%). Behaviors were manifested mainly through hand (22.8%) and body movements (27.7%), and vocalizations (21.6%).
The ChildSIDE app is an effective tool in collecting the behavior data of children with PIMD/SMID. The app showed high server/API performance in detecting outdoor location and environment data from sensors and an online API to the database with a performance rate above 93%. The results of the analysis and categorization of behaviors suggest a need for a system that uses motion capture and trajectory analyses for developing machine- or deep-learning algorithms to predict the needs of children with PIMD/SMID in the future.
患有严重智力和多重残疾（PIMD）或严重运动和智力残疾（SMID）的儿童仅通过前符号或原符号水平的动作、发声、身体姿势、肌肉紧张或面部表情进行交流。然而，据我们所知，很少有系统专门用于帮助对患有PIMD或SMID的儿童的行为进行分类和解释，以促进独立交流和行动能力。此外，已发现环境数据（如天气变量）与正常发育儿童的人类情感和行为有关；然而，涉及影响沟通的神经功能受损儿童或有身体和/或运动残疾儿童的研究却出人意料地稀少。
本文描述了儿童情境交互与数据收集（ChildSIDE）应用程序的设计与开发，该应用程序收集并传输与儿童行为相关的数据，以及从数据源（全球定位系统、iBeacon设备、阿尔卑斯传感器和开放天气地图应用程序编程接口[API]）收集的相关位置和环境信息到数据库。本研究的目的是测量和比较该应用程序在检测和将环境数据从数据源传输到数据库方面的服务器/API性能，并将与每个行为数据相关的动作进行分类，作为未来开发和分析的基础。
本研究采用横断面观察设计，在2019年9月至2020年2月期间，对有目的地抽样的儿童-照顾者二元组（被诊断为PIMD/SMID或严重或极重度智力残疾的儿童及其主要照顾者）进行多次单受试者面对面和视频记录会话。为了测量该应用程序在检测和将数据从数据源传输到数据库方面的服务器/API性能，计算并比较了31个位置和环境数据参数的频率分布和百分比。为了对每种行为涉及哪些身体部位或动作进行分类，使用了评分者间一致性κ统计量。
该研究包括涉及20个儿童-照顾者二元组的150次会话。该应用程序收集了371条个体行为数据，其中327条具有来自数据收集源的相关位置和环境数据。分析表明，ChildSIDE在检测和传输户外位置（全球定位系统）和环境数据（阿尔卑斯传感器、开放天气地图API）方面的服务器/API性能>93%，而iBeacon数据的性能较低（82.3%）。行为主要通过手部动作（22.8%）、身体动作（27.7%）和发声（21.6%）表现出来。
ChildSIDE应用程序是收集患有PIMD/SMID儿童行为数据的有效工具。该应用程序在从传感器和在线API检测户外位置和环境数据并传输到数据库方面显示出较高的数据服务器/API性能，性能率超过93%。行为分析和分类结果表明，未来需要一个使用动作捕捉和轨迹分析来开发机器或深度学习算法以预测患有PIMD/SMID儿童需求的系统。
