LiLo: ADL Localization with Conventional Luminaries and Ambient Light Sensor

Abstract

Indoor localization is a key factor for activities of daily living (ADLs)-related services. Many studies invest effort and money on high-cost infrastructure with modified devices. In this paper, an indoor localization system (LiLo) that utilizes ambient light sensor and orientation information on smartphones to recognize ADLs is proposed. Indoor ADLs are recognized by analyzing the data combination of visible light based localization, orientation and time. In the cold start period, LiLo estimates the location based on the computed luminance field map and the frequent orientation, validating the location result by the angle of arrival information. Then, LiLo produces the locations with a machine learning classifier. Compared with previous works, LiLo leaves out the laborious device configuration setup and data collection during the off-line phase. Another advantage is that LiLo utilizes a conventional luminaire and a standard smartphone, without extra infrastructure spreading in rooms. Therefore, every resident with a smartphone can benefit from this technology. An experimental study using data collected from smartphones shows that LiLo is able to achieve high localization accuracy at a low cost.