A Review on the Artificial Intelligence Algorithms for the Recognition of Activities of Daily Living Using Sensors in Mobile Devices

Abstract

Smart environments and mobile devices are two technologies that when combined may allow the recognition of Activities of Daily Living (ADL) and its environments. This paper focuses on the literature review of the existing machine learning methods for the recognition of ADL and its environments, by means of comparison jointly with a proposal of a novel taxonomy in this context. The sensors used for this purpose depends on the nature of the system and the ADL to recognize. The available in the mobile devices are mainly motion, magnetic and location sensors, but the sensors available in the smart environments may have different types. Data acquired from several sensors can be used for the identification of ADL, where the motion, magnetic and location sensors handle the recognition of activities with movement, and the acoustic sensors handle the recognition of activities related with the environment.

The Use of Transfer Learning for Activity Recognition in Instances of Heterogeneous Sensing

“…The tasks consisted of ADLs and well-known scenarios. Three general scenarios were chosen, a bedroom-related scenario in which the subjects performed two of the ADLs, namely, personal hygiene and dressing [41][42][43], a breakfast-related scenario was chosen to embrace the ADL of feeding as it has extensively been used in literature [44][45][46], and free of obstacle scenario in which the subjects can walk alongside to demonstrate their transferring capabilities [44,46]. The script was designed with nine high-level activities (napping, wearing joggers, brushing teeth, combing hair, operating door, eating cereal, drinking, walking, and standing) to which the subjects were encouraged to perform as naturally as possible (Note that the activities were performed not simulated).…”