Abstract
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.
Highlights
In the era of Internet of Things (IoT), activity recognition has mostly been studied in supervised laboratory settings
We propose the Activity of daily living (ADL) recognition system [18] that paves a road for recording, detection, recognition, and electronic documentation of user’s ADL
Wi-Fi-based localization module is built on utilizing the Wi-Fi Received Signal Strength Indication (RSSI) feature; acoustic features of environmental sound are extracted and recognized from audio files; activity recognition focuses on the data collected from the accelerometer, gyroscope sensors, etc
Summary
In the era of Internet of Things (IoT), activity recognition has mostly been studied in supervised laboratory settings. Many such studies are concerned with the quality of life of older adults who live independently in places such as at home. Many kinds of sensors widely exist in our daily environments, including in a house, in a car, in an airplane or when using a smartphone. Many researchers have utilized sensors to detect and recognize people’s activities of daily living. The remainder of the paper proceeds as follows: the following section reviews some existing activity recognition projects, and points out the importance of sensor fusion.
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