Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devices

Abstract

The identification of Activities of Daily Living (ADL) is intrinsic with the user’s environment recognition. This detection can be executed through standard sensors present in every-day mobile devices. On the one hand, the main proposal is to recognize users’ environment and standing activities. On the other hand, these features are included in a framework for the ADL and environment identification. Therefore, this paper is divided into two parts—firstly, acoustic sensors are used for the collection of data towards the recognition of the environment and, secondly, the information of the environment recognized is fused with the information gathered by motion and magnetic sensors. The environment and ADL recognition are performed by pattern recognition techniques that aim for the development of a system, including data collection, processing, fusion and classification procedures. These classification techniques include distinctive types of Artificial Neural Networks (ANN), analyzing various implementations of ANN and choosing the most suitable for further inclusion in the following different stages of the developed system. The results present 85.89% accuracy using Deep Neural Networks (DNN) with normalized data for the ADL recognition and 86.50% accuracy using Feedforward Neural Networks (FNN) with non-normalized data for environment recognition. Furthermore, the tests conducted present 100% accuracy for standing activities recognition using DNN with normalized data, which is the most suited for the intended purpose.

Activities of Daily Living and Environment Recognition Using Mobile Devices: A Comparative Study

“…According to the previously proposed structure of a framework for the recognition of ADL and environments [2,[17][18][19][20][21][22][23][24][25], the main focus of this study was related to the data classification module, taking into account the implementations of the other modules performed in previous studies. Previously, the DNN method was implemented, and it reported reliable results.…”

“…This study consisted of the use of the same structure and data acquired by the research presented in [18,21,22,24,25] to implement a comparative study between three types of studies. The tests were conducted with the dataset available in [24], which included data related to the eight ADL and nine environments.…”

“…Data fusion and classification were included in the last stage of the ADL and environment recognition framework. The previous studies reported that the best accuracies were achieved with the DNN method [18,21,22,24,25], and all the features are presented in Table 1. This study presents the results of the test and validation of different methods, including IBk, AdaBoost with the decision stump, and AdaBoost with the decision tree, implemented in the Java programming language for compatibility with Android based devices.…”

“…The primary purpose of this paper is to find the best machine learning method for ADL and environment recognition. The results obtained show that IBk and AdaBoost reported better results, with complex data than the deep neural network methods.Electronics 2020, 9, 180 2 of 16The automatic recognition of ADL is widely researched [11][12][13][14][15][16], where the previously proposed framework [2,[17][18][19][20][21][22][23][24][25] was tested and validated with different types of Artificial Neural Networks (ANN) [26][27][28], verifying that the best results were achieved with Deep Neural Networks (DNN). The proposed framework allows the recognition of eight ADL, i.e., walking, running, standing, going upstairs, going downstairs, watching television, sleeping, driving, and other activities without motion, and nine environments, i.e., bar, classroom, gym, hall, kitchen, library, street, bedroom, and living room.…”

Promotion of Healthy Nutrition and Physical Activity Lifestyles for Teenagers: A Systematic Literature Review of The Current Methodologies

“…Research has shown that personalized approaches and systematic feature engineering and selection can help in improving the activity recognition accuracy [22][23][24]. In addition to using inertial sensors (i.e., accelerometers and gyroscopes) embedded in mobile devices, acoustic sensors can augment and improve the activity and environment recognition [25,26]. However, these methods have several limitations that should be considered during the development of these systems, such as availability of the sensors, weather conditions, battery lifetime, limited power processing, and memory capabilities, among others [27,28].…”