Title: Pattern Recognition Techniques for the Identification of Activities of Daily Living using Mobile Device Accelerometer,
Abstract: This paper focuses on the recognition of Activities of Daily Living (ADL)
applying pattern recognition techniques to the data acquired by the
accelerometer available in the mobile devices. The recognition of ADL is
composed by several stages, including data acquisition, data processing, and
artificial intelligence methods. The artificial intelligence methods used are
related to pattern recognition, and this study focuses on the use of Artificial
Neural Networks (ANN). The data processing includes data cleaning, and the
feature extraction techniques to define the inputs for the ANN. Due to the low
processing power and memory of the mobile devices, they should be mainly used
to acquire the data, applying an ANN previously trained for the identification
of the ADL. The main purpose of this paper is to present a new method
implemented with ANN for the identification of a defined set of ADL with a
reliable accuracy. This paper also presents a comparison of different types of
ANN in order to choose the type for the implementation of the final method.
Results of this research probes that the best accuracies are achieved with Deep
Learning techniques with an accuracy higher than 80%.