Over the last years, the increasing ubiquity of smartphone devices allowed to completely redraw their traditional concept and applicability. The increasing advances on technology allowed integrating a multitude of different sensors, capable to ’sense’ the world that surrounds the users and their actions, allowing to perform a continuous Human Activity Recognition (HAR). This topic has become of high interest for medical applications. On the one hand, patients with chronic diseases are required to follow pre-defined exercises as part of their long-term treatments. On the other hand, the evolution of social interactions promoted the interest and need for ’self-measurement’.
Traditionally, Human Activity Recognition (HAR) uses a first layer of information based on inertial data which proved to be a valuable asset. However, common available architectures can be improved, if additional sensing mechanisms are introduced to sense the context where the user is inserted.
This project introduced a new layer of information using pervasive sound analysis from the built-in smartphone’s microphones. Sound is everywhere on daily life and certain actions and environments create well-defined fingerprints which can help to increase today’s HAR architectures performance11. Examples of such actions include the sound promoted by closing/opening doors, walking outdoors/indoors and walking on stairs, which help to locate user and help to identify his activity; the sounds of Human speech, which may eventually address social behaviours; the absence of sound, which may help to assure correct circadian rhythms are being followed. Ultimately, an earlier identification of continuous exposure to high intensity noises, which constitute a potential health hazard, may also prevent potential ear diseases.
This project focused on the development of a framework for HAR and Indoor Location using the sound perceived through the device’s microphone, which can enhance the current system’s performance, based on their actions and context.
Author: Ricardo Leonardo
Type: MSc thesis
Partner: Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa