Smartphone-Based Human Activity Recognition

Abstract

ADVERTIMENT. La consulta d'aquesta tesi queda condicionada a l'acceptació de les següents condicions d'ús: La difusió d'aquesta tesi per mitjà del servei TDX (www.tesisenxarxa.net) ha estat autoritzada pels titulars dels drets de propietat intel·lectual únicament per a usos privats emmarcats en activitats d'investigació i docència. No s'autoritza la seva reproducció amb finalitats de lucre ni la seva difusió i posada a disposició des d'un lloc aliè al servei TDX. No s'autoritza la presentació del seu contingut en una finestra o marc aliè a TDX (framing). Aquesta reserva de drets afecta tant al resum de presentació de la tesi com als seus continguts. En la utilització o cita de parts de la tesi és obligat indicar el nom de la persona autora.ADVERTENCIA. La consulta de esta tesis queda condicionada a la aceptación de las siguientes condiciones de uso: La difusión de esta tesis por medio del servicio TDR (www.tesisenred.net) ha sido autorizada por los titulares de los derechos de propiedad intelectual únicamente para usos privados enmarcados en actividades de investigación y docencia. No se autoriza su reproducción con finalidades de lucro ni su difusión y puesta a disposición desde un sitio ajeno al servicio TDR. No se autoriza la presentación de su contenido en una ventana o marco ajeno a TDR (framing). Esta reserva de derechos afecta tanto al resumen de presentación de la tesis como a sus contenidos. En la utilización o cita de partes de la tesis es obligado indicar el nombre de la persona autora. WARNING.On having consulted this thesis you're accepting the following use conditions: Spreading this thesis by the TDX (www.tesisenxarxa.net) service has been authorized by the titular of the intellectual property rights only for private uses placed in investigation and teaching activities. Reproduction with lucrative aims is not authorized neither its spreading and availability from a site foreign to the TDX service. Introducing its content in a window or frame foreign to the TDX service is not authorized (framing). This rights affect to the presentation summary of the thesis as well as to its contents. In the using or citation of parts of the thesis it's obliged to indicate the name of the author The first stage to accomplish HAR requires to make observations from ambient or wearable sensor technologies. However, in the second case, the search for pervasive, unobtrusive, low-powered, and low-cost devices for achieving this challenging task still has not been fully addressed. In this thesis, we explore the use of smartphones as an alternative approach for performing the identification of physical activities. These self-contained devices, which are widely available in the market, are provided with embedded sensors, powerful computing capabilities and wireless communication technologies that make them highly suitable for this application.This work presents a series of contributions regarding the development of HAR systems with smartphones. In the first place we propose a fully operational system that recognizes in real...

Motion sensor data anonymization by time-frequency filtering

“…We used the public dataset Motion-Sense to assess the performance of our approach. It includes data sensed from 3-axis motion sensors at a constant frequency of 50 Hz collected with an iPhone 6s kept in the participant's front pocket [14]. Overall, a total of 24 participants have performed six activities during 15 trials in the same environment and conditions.…”

Privacy-preserving IoT Framework for Activity Recognition in Personal Healthcare Monitoring

“…Figure 3 displays accelerometer signal of one of the experiments and the associated activities. The protocol of activities is detailed in [57]. The duration of an entire experiment was around 15 minutes and was repeated ten times.…”

Multi-Label Classification for Physical Activity Recognition From Various Accelerometer Sensor Positions

“…This is because more nodes will memorize the form of information instead of generalizing the patterns. Few works related to HAR claimed that MLP yielded good accuracy (Reyes Ortiz, 2015;Shoaib et al, 2013) but consumed a longer time in its implementation (Alsheikh et al, 2015). MLP also showed great performance in another domain area such as forex trend movement in order to analyse the trend pattern based on historical performance (Tiong, Ow, Chek, Ngo, & Lee, 2016).…”

“…This procedure was required in order to trace how the signals changed over the time period. Then, the Butterworth low-pass filter was used to separate the body acceleration from the gravitational acceleration signals (Acharjee, Mukherjee, Mandal, & Mukherjee, 2015;Anguita, Ghio, Oneto, Parra, & Reyes-Ortiz, 2013;Arif et al, 2014;Machado, Luisa Gomes, Gamboa, Paixao, & Costa, 2015;Reyes Ortiz, 2015;Sun, Zhang, Li, Guo, & Li, 2010). The remaining body acceleration signals later would be used for further process.…”