User Environment Detection with Acoustic Sensors Embedded on Mobile Devices for the Recognition of Activities of Daily Living

TL;DR

This paper presents a system that uses acoustic, motion, and magnetic sensors on mobile devices combined with neural networks to accurately recognize environments and daily activities, improving activity detection in real-world settings.

Contribution

It introduces a multi-sensor data fusion approach with optimized neural network models for environment and activity recognition on mobile devices.

Findings

Deep Neural Networks achieved 85.89% accuracy in ADL recognition.

Feedforward neural networks achieved 86.50% accuracy in environment recognition.

DNN with normalized data achieved 100% accuracy in standing activity detection.

Abstract

The detection of the environment where 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 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 activities of daily living. 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 artificial intelligence methods. The artificial intelligence 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 with non-normalized data for the identification of the environments with 86.50% of accuracy, and the use of DNN with normalized data for the identification of standing activities with 100% of accuracy.