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This paper focuses on the recognition of Activities of Daily Living (ADL)applying pattern recognition techniques to the data acquired by theaccelerometer available in the mobile devices. The recognition of ADL iscomposed by several stages, including data acquisition, data processing, andartificial intelligence methods. The artificial intelligence methods used arerelated to pattern recognition, and this study focuses on the use of ArtificialNeural Networks (ANN). The data processing includes data cleaning, and thefeature extraction techniques to define the inputs for the ANN. Due to the lowprocessing power and memory of the mobile devices, they should be mainly usedto acquire the data, applying an ANN previously trained for the identificationof the ADL. The main purpose of this paper is to present a new methodimplemented with ANN for the identification of a defined set of ADL with areliable accuracy. This paper also presents a comparison of different types ofANN 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 DeepLearning techniques with an accuracy higher than 80%.

Pattern Recognition Techniques for the Identification of Activities of Daily Living using Mobile Device Accelerometer