Pattern Recognition Techniques for the Identification of Activities of Daily Living Using a Mobile Device Accelerometer
Accelerometer
accelerometer
mobile devices
Sensors
Activities of daily living
Mobile devices
0202 electrical engineering, electronic engineering, information engineering
02 engineering and technology
activities of daily living
sensors
Arquitectura y Tecnología de Computadores
01 natural sciences
0104 chemical sciences
DOI:
10.3390/electronics9030509
Publication Date:
2020-03-20T11:29:07Z
AUTHORS (8)
ABSTRACT
The application of pattern recognition techniques to data collected from accelerometers available in off-the-shelf devices, such as smartphones, allows for the automatic recognition of activities of daily living (ADLs). This data can be used later to create systems that monitor the behaviors of their users. The main contribution of this paper is to use artificial neural networks (ANN) for the recognition of ADLs with the data acquired from the sensors available in mobile devices. Firstly, before ANN training, the mobile device is used for data collection. After training, mobile devices are used to apply an ANN previously trained for the ADLs’ identification on a less restrictive computational platform. The motivation is to verify whether the overfitting problem can be solved using only the accelerometer data, which also requires less computational resources and reduces the energy expenditure of the mobile device when compared with the use of multiple sensors. This paper presents a method based on ANN for the recognition of a defined set of ADLs. It provides a comparative study of different implementations of ANN to choose the most appropriate method for ADLs identification. The results show the accuracy of 85.89% using deep neural networks (DNN).
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