Fall detection systems are concerned with rapidly detecting the occurrence of falls from elderly and disabled users using data from a body-worn inertial measurement unit (IMU), which is typically used in conjunction with machine learning-based classification. Such systems, however, necessitate the collection of high-resolution measurements that can violate users' privacy, such as revealing their gait, activities of daily living (ADLs), and relative position using dead reckoning. In this paper, for the first time, we present the design, implementation and evaluation of applying multi-party computation (MPC) to IMU-based fall detection for assuring the confidentiality of device measurements. The system is evaluated in a cloud-based setting that precludes parties from learning the underlying data using three parties deployed in geographically disparate locations in three cloud configurations. Using a publicly-available dataset comprising fall data from real-world users, we explore the applicability of derivative-based features to mitigate the complexity of MPC-based operations in a state-of-the-art fall detection system. We demonstrate that MPC-based fall detection from IMU measurements is both feasible and practical, executing in 365.2 milliseconds, which falls well below the required time window for on-device data acquisition (750ms).
翻译:秋天探测系统涉及迅速探测来自老年人和残疾用户的坠落情况,这些用户使用机形惯性测量单位(IMU)的数据,通常与机器学习分类一起使用,但这类系统需要收集高分辨率的测量,这种测量可能侵犯用户隐私,例如暴露其行踪、日常生活活动(ADLs),以及使用死亡计算法相对位置。在本文件中,我们首次介绍了对IMU的低度测量进行多方计算(MPC)的设计、实施和评估,以确保设备测量的保密性。该系统是在云层环境下评估的,使缔约方无法利用三个云形分布在不同地点的三个云形配置中的三个方学习基本数据。我们利用由真实世界用户秋季数据组成的公开数据集,探索衍生物特征的适用性,以缓解基于MPC的运行在最新水平的下降探测系统中的复杂性。我们表明,基于MPC的低度测量从IMU测量中进行下坠落检测既可行,又实用,在365.2毫秒内执行。