Bolleddula Nithish, Chun Hung Geoffrey Yau, Ma Daren, Noorian Hoda, Woodbridge Diane Myung-Kyung
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:3927-3930. doi: 10.1109/EMBC44109.2020.9176631.
As the world's older population grows dramatically, the needs of continuing care retirement communities increases. Studies show that privacy can be a major concern for adopting technologies, while the older population prefers smart homes [1]. In order to minimize the number of sensors to be installed in each house, we performed Principal Component Analysis (PCA) to filter out the relatively unimportant sensors. We applied a machine learning model to classify residents' activity types, using a different set of sensors chosen by PCA. Then, we validated the trade-off between the classification model accuracy and the number of sensors used in classification. Our experiment shows that feature engineering helps reduce accuracy degradation for activity type classification when using fewer sensors in smart homes.
随着全球老年人口急剧增长，持续照料退休社区的需求也在增加。研究表明，隐私可能是采用技术的一个主要担忧，而老年人群更喜欢智能家居[1]。为了尽量减少每户需安装的传感器数量，我们进行了主成分分析（PCA）以滤除相对不重要的传感器。我们应用机器学习模型，使用PCA选择的不同传感器集对居民的活动类型进行分类。然后，我们验证了分类模型准确性与分类中使用的传感器数量之间的权衡。我们的实验表明，在智能家居中使用较少传感器时，特征工程有助于减少活动类型分类的准确性下降。
