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).
Highlights
The accelerometer sensor commonly available in off-the-shelf mobile devices [1,2] measures the acceleration of the movement of the mobile device, enabling the recognition of activities of daily living (ADLs) [3]
The accelerometer was used for the identification of ADLs while comparing some implementations of artificial neural networks (ANN) with different frameworks, such as the multilayer perception (MLP) with Neuroph [17] and Encog [18] frameworks, and the deep neural network (DNN) method with the DeepLearning4j [19] framework
This paper presents several approaches that use the accelerometer sensor commonly available in mobile devices for ADLs recognition
Summary
The accelerometer sensor commonly available in off-the-shelf mobile devices [1,2] measures the acceleration of the movement of the mobile device, enabling the recognition of activities of daily living (ADLs) [3]. The accelerometer was used for the identification of ADLs while comparing some implementations of ANN with different frameworks, such as the multilayer perception (MLP) with Neuroph [17] and Encog [18] frameworks, and the deep neural network (DNN) method with the DeepLearning4j [19] framework. The authors aimed to find the model that achieves the best accuracy in recognition of running, walking, walking downstairs, walking upstairs, and standing. These five ADLs were selected based on the literature review, wherein different studies reported reliable results for these activities, to allow the comparison with the method implemented in this research. The main objective of this paper is to explore the use of different sets of features obtained using the accelerometer with the same datasets acquired for the previous study
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