Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2163-2169. doi: 10.1109/EMBC46164.2021.9630592.
Wearable devices are currently being considered to collect personalized physiological information, which is lately being used to provide healthcare services to individuals. One application is detecting depression by utilization of motor activity signals collected by the ActiGraph wearable wristbands. However, to develop an accurate classification model, we require to use a sufficient volume of data from several subjects, taking the sensitivity of such data into account. Therefore, in this paper, we present an approach to extract classification models for predicting depression based on a new augmentation technique for motor activity data in a privacy-preserving fashion. We evaluate our approach against the state-of-the-art techniques and demonstrate its performance based on the mental health datasets associated with the Norwegian INTROducing Mental health through Adaptive Technology (INTROMAT) Project.
可穿戴设备目前被认为可以收集个性化的生理信息，这些信息最近被用于为个人提供医疗保健服务。其中一个应用是通过利用 ActiGraph 可穿戴腕带收集的运动活动信号来检测抑郁症。然而，为了开发一个准确的分类模型，我们需要使用来自多个主体的足够数量的数据，并考虑到这些数据的敏感性。因此，在本文中，我们提出了一种基于新的运动活动数据扩充技术的隐私保护方法，用于提取预测抑郁的分类模型。我们针对最先进的技术评估了我们的方法，并根据与挪威通过自适应技术引入心理健康（INTROMAT）项目相关的心理健康数据集展示了其性能。
