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Article

Sensor Fusion for Recognition of Activities of Daily Living

by 1,2, 1,* and 1
1
Department of Computer Science, Iowa State University, Ames, IA 50010, USA
2
College of Computer Science and Engineering, Northeastern University, Shenyang 110000, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(11), 4029; https://doi.org/10.3390/s18114029
Received: 30 September 2018 / Revised: 30 October 2018 / Accepted: 15 November 2018 / Published: 19 November 2018
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 ADL Recognition System 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 ADL Recorder App 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 ADL Recognition System 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. View Full-Text
Keywords: activity of daily living; time-series; sensor fusion; smartphone; machine learning; big data activity of daily living; time-series; sensor fusion; smartphone; machine learning; big data
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MDPI and ACS Style

Wu, J.; Feng, Y.; Sun, P. Sensor Fusion for Recognition of Activities of Daily Living. Sensors 2018, 18, 4029. https://doi.org/10.3390/s18114029

AMA Style

Wu J, Feng Y, Sun P. Sensor Fusion for Recognition of Activities of Daily Living. Sensors. 2018; 18(11):4029. https://doi.org/10.3390/s18114029

Chicago/Turabian Style

Wu, Jiaxuan; Feng, Yunfei; Sun, Peng. 2018. "Sensor Fusion for Recognition of Activities of Daily Living" Sensors 18, no. 11: 4029. https://doi.org/10.3390/s18114029

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