Simple and Complex Activity Recognition through Smart Phones

Dernbach, Stefan; Das, Barnan; Krishnan, Narayanan C.; Thomas, Brian L.; Cook, Diane J.

doi:10.1109/ie.2012.39

Cited by 277 publications

(190 citation statements)

References 15 publications

Supporting

Mentioning

155

Contrasting

Unclassified

Order By: Relevance

“…It is challenging to objectively map the privacy concerns that may be associated with any particular sensor, since they can vary across users and strongly depend on what other data is available. For instance, gyroscope is typically used together with accelerometer to detect activities such as walking, standing, sitting and lying can be recognised with high accuracy 96% [3], while on the other hand complex activities (e.g., cooking, cleaning and sweeping) are still considered challenging to recognise [13]. Therefore, we rely on subjective assessment, heuristics, and our review of literature [19,22,28] to rate the privacy concerns for each sensor.…”

Section: Studymentioning

confidence: 99%

A data hiding approach for sensitive smartphone data

Luo

Fylakis

Partala

et al. 2016

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

View full text Add to dashboard Cite

We develop and evaluate a data hiding method that enables smartphones to encrypt and embed sensitive information into carrier streams of sensor data. Our evaluation considers multiple handsets and a variety of data types, and we demonstrate that our method has a computational cost that allows real-time data hiding on smartphones with negligible distortion of the carrier stream. These characteristics make it suitable for smartphone applications involving privacysensitive data such as medical monitoring systems and digital forensics tools.

show abstract

Section: Studymentioning

confidence: 99%

A data hiding approach for sensitive smartphone data

Luo

Fylakis

Partala

et al. 2016

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

View full text Add to dashboard Cite

show abstract

“…They concluded that accelerometer and gyroscope complement each other in general, however magnetometer is not so promising due to its dependency on directions. Dernbach et al [29] investigated the ability to recognise not only locomotion activities, but also high level activities, such as cooking and cleaning. In general, the recognition accuracy using on-board sensors alone is lower than other approaches in which specific sensors are attached to targeted areas of human body.…”

Section: Smartphone Based Activity Recognitionmentioning

confidence: 99%

Collaborative streaming of on demand videos for mobile devices

Zhong

2016

2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)

View full text Add to dashboard Cite

The number of mobile users with a variety of applications that require on demand video content is growing rapidly. However, bandwidth insufficiency is an obstacle in providing high quality smooth video playout in cellular networks. There are two kinds of solutions in the literature that provide high bandwidth communication channels. One is bandwidth aggregation that aggregates the bandwidth from multiple radio access technologies (RATs) on a single device to create a high-bandwidth logical link for an application. Another is collaboration with peers. This kind of content distribution solutions originated from content sharing in peer-to-peer networks, and then evolved through BitTorrent (which maximises the downloading performance) to the mobile environment. To provide effective collaborative streaming to mobile devices, several functionalities are required including e.g. device discovery, activity recognition and bandwidth estimation.In the current approaches to collaborative streaming, the first step of collaborative streaming has not been fully addressed, because unlike traditional device discovery methods in P2Pand BitTorrent in which peers share the same network, the discovery of devices in heterogeneous wireless networks is a very challenging problem. These devices access the Internet via heterogeneous wireless networks without pre-knowledge of the existence of nearby devices, and no effective solution exists except for very energy costly GPS based methods that track device locations all the time. In addition, in mobile environments user mobility and available bandwidth directly affect the performance of collaborative streaming. For example, the connection between a stationary user and a user in vehicle drops within a few seconds. Thus finding the collaborative devices with similar mobility pattern and high-bandwidth is important for collaborative streaming, but the existing solutions on activity recognition and on bandwidth estimation perform poorly on mobile devices. As for activity recognition, there exist solutions for smart environments and wearable sensor networks, however they cannot be simply applied to mobile devices. Google published its activity recognition service on Android phones in 2013, and it is the first publicly available service providing activity recognition on Android phones. However its recognition accuracy is not very high and needs to be improved. Bandwidth estimation is important for collaborative streaming as the quality of the streamed video depends to a large extent on the bandwidth of the selected collaborating devices. Most of the existing bandwidth estimation approaches designed for wired and wireless computer networks fail in cellular networks, due to the bandwidth fluctuation in these networks and the cost of servers and user's data quota.i The research on the functionalities of collaborative streaming for mobile devices are still in early stages. The primary goal of the research presented in this thesis is to develop new algorithms and mechanisms for collaborati...

show abstract

“…For example, the Opportunity Project has recorded a set of ADL in a sensor-rich environment using 72 environmental and body sensors. Similarly, other works have provided datasets such as [Tapia et al, 2006] and [Dernbach et al, 2012].…”

Section: Experimental Data Collectionmentioning

confidence: 99%

Smartphone-Based Human Activity Recognition

Ortiz

2015

Springer Theses

View full text Add to dashboard Cite

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...

show abstract

Simple and Complex Activity Recognition through Smart Phones

Cited by 277 publications

References 15 publications

A data hiding approach for sensitive smartphone data

A data hiding approach for sensitive smartphone data

Collaborative streaming of on demand videos for mobile devices

Smartphone-Based Human Activity Recognition

Contact Info

Product

Resources

About