Abstract
The detection of the environment where the user is located, is of extreme use for the identification of Activities of Daily Living (ADL). ADL can be identified by use of the sensors available in many off-the-shelf mobile devices, including magnetic and motion, and the environment can be also identified using acoustic sensors. The main objective of this study is to recognize the environments and some standing ADL to include in the development of a framework for the recognition of ADL and its environments. The study presented in this paper is divided in two parts: firstly, we discuss the recognition of the environment using acoustic sensors (i.e., microphone), and secondly, we fuse this information with motion and magnetic sensors (i.e., motion and magnetic sensors) for the recognition of standing ADL. The recognition of the environments and the ADL are performed using pattern recognition techniques, in order to develop a system that includes data acquisition,data processing, data fusion, and classification methods. The classification methods explored in this study are composed by different types of Artificial Neural Networks (ANN), comparing the different types of ANN and selecting the best methods to implement in the different stages of the system developed. Conclusions point to the use of Deep Neural Networks (DNN)with normalized data for the identification of ADL with 85.89% of accuracy, the use of Feedforward Neural Networks (FNN) with non-normalized data for the identification of the environments with 86.50% of accuracy, and the use of DNN method with normalized data for the identification of standing activities with 100% of accuracy.
Highlights
The acquisition of data related to the Activities of Daily Living (ADL) [1] may be performed with the sensors available in off-the-shelf mobile devices, e.g., the accelerometer, the gyroscope, the magnetometer, the microphone, and the Global Positioning System (GPS) receiver
This study proposes the use of the microphone for the recognition of the environment, which is fused with the data acquired from the accelerometer, gyroscope and magnetometer sensors for the recognition of the activities with movement
For the implementation and testing of these methods, we proposed the use of Artificial Neural Networks (ANN), exploring the use of three types of ANN, such as the Multilayer Perception (MLP) with Backpropagation implemented with Neuroph [15], the Feedforward neural network with Backpropagation implemented with Encog [16], and Deep Learning implemented with DeepLearning4j [17]
Summary
The acquisition of data related to the Activities of Daily Living (ADL) [1] may be performed with the sensors available in off-the-shelf mobile devices, e.g., the accelerometer, the gyroscope, the magnetometer, the microphone, and the Global Positioning System (GPS) receiver. This study proposes the recognition of ADL, including running, walking, walking on stairs, standing, and sleeping, and the recognition of environments, including bar, classroom, gym, kitchen, library, street, hall, watching TV and bedroom. These methods are included in the development of a framework for the recognition of ADL and their environments, proposed in [5,6,7], composed by several modules, such as data acquisition, data processing, data fusion, and artificial intelligence methods. The advantages of recognition of the environments are not limited to the increasing of the number of ADL recognized, but it allows the framework to combine the environments with the ADL recognition returning different results, e.g., the user is walking on the street
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