Department of Computer Science, Iowa State University, Ames, IA 50010, USA.
College of Computer Science and Engineering, Northeastern University, Shenyang 110000, China.
Sensors (Basel). 2018 Nov 19;18(11):4029. doi: 10.3390/s18114029.
Activity of daily living (ADL) is a significant predictor of the independence and functional capabilities of an individual. Measurements of ADLs help to indicate one's health status and capabilities of quality living. Recently, the most common ways to capture ADL data are far from automation, including a costly 24/7 observation by a designated caregiver, self-reporting by the user laboriously, or filling out a written ADL survey. Fortunately, ubiquitous sensors exist in our surroundings and on electronic devices in the Internet of Things (IoT) era. We proposed the that utilizes the sensor data from a single point of contact, such as smartphones, and conducts time-series sensor fusion processing. Raw data is collected from the constantly running on a user's smartphone with multiple embedded sensors, including the microphone, Wi-Fi scan module, heading orientation of the device, light proximity, step detector, accelerometer, gyroscope, magnetometer, etc. Key technologies in this research cover audio processing, Wi-Fi indoor positioning, proximity sensing localization, and time-series sensor data fusion. By merging the information of multiple sensors, with a time-series error correction technique, the is able to accurately profile a person's ADLs and discover his life patterns. This paper is particularly concerned with the care for the older adults who live independently.
日常生活活动（ADL）是个体独立性和功能能力的重要预测指标。ADL 的测量有助于表明一个人的健康状况和生活质量能力。最近，最常见的捕捉 ADL 数据的方法远非自动化，包括由指定护理人员进行昂贵的 24/7 观察、用户费力地自我报告或填写书面 ADL 调查。幸运的是，在物联网（IoT）时代，无处不在的传感器存在于我们周围和电子设备中。我们提出了一种利用单点接触（如智能手机）的传感器数据的方法，并进行时间序列传感器融合处理。原始数据是从用户智能手机上不断运行的 中收集的，该智能手机具有多个嵌入式传感器，包括麦克风、Wi-Fi 扫描模块、设备的朝向、光接近度、步数检测器、加速度计、陀螺仪、磁力计等。这项研究的关键技术包括音频处理、Wi-Fi 室内定位、接近感应定位和时间序列传感器数据融合。通过合并多个传感器的信息，并采用时间序列误差校正技术，能够准确地描绘一个人的日常生活活动，并发现他的生活模式。本文特别关注独立生活的老年人的护理。
