Smart Assisted Living - Toward An Open Smart-Home Infrastructure
Smart Assisted Living - Toward An Open Smart-Home Infrastructure
复制标题
智能辅助生活 - 迈向开放的智能家居基础设施
DOI：
10.1007/978-3-030-25590-9_8
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Gao Y
中科院分区：
文献类型：
--
作者：
Gao Y
围产期中风（PS）是一种常常导致终身残疾的严重疾病，尤其是脑瘫（CP）。早期发现和早期干预可改善运动预后。在临床环境中，Prechtl的一般运动评估（GMA）可以使用格式塔方法对婴儿运动进行分类，识别出运动发育异常的高风险婴儿。培训和维护评估技能对于正确使用GMA至关重要且昂贵，但许多从业人员缺乏这些技能，妨碍了更大规模的筛查，并导致丢失受影响婴儿的重大风险。我们提出了一种自动化的GMA方法，基于人体穿戴的加速度计和一种新的传感器数据分析方法-判别模式发现（DPD），该方法旨在应对只有粗糙数据注释可用于模型训练的场景。我们在34名新生儿（21名发育正常的婴儿和13名运动异常的PS婴儿）的研究中证明了我们方法的有效性。我们的方法能够正确识别具有异常运动的试验，其准确率至少达到新训练的人类注释者（75%）的要求，这对我们的最终目标是一个可以在全人群中使用的自动筛选系统是令人鼓舞的。
Perinatal stroke (PS) is a serious condition that often leads to life-long disability, in particular cerebral palsy (CP). Early detection and early intervention could improve motor outcome. In clinical settings, Prechtl’s general movement assessment (GMA) can be used to classify infant movements using a Gestalt approach, identifying infants at high risk of abnormal motor development. Training and maintenance of assessment skills are essential and expensive for the correct use of GMA, yet many practitioners lack these skills, preventing larger-scale screening and leading to significant risks of missing affected infants. We present an automated approach to GMA, based on body-worn accelerometers and a novel sensor data analysis method—discriminative pattern discovery (DPD)—that is designed to cope with scenarios where only coarse annotations of data are available for model training. We demonstrate the effectiveness of our approach in a study with 34 newborns (21 typically developing infants and 13 PS infants with abnormal movements). Our method is able to correctly recognise the trials with abnormal movements with at least the accuracy that is required by newly trained human annotators (75%), which is encouraging towards our ultimate goal of an automated screening system that can be used population-wide.