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Personalized Mobile Physical Activity Monitoring for Everyday Life

  • Regular physical activity is essential to maintain or even improve an individual’s health. There exist various guidelines on how much individuals should do. Therefore, it is important to monitor performed physical activities during people’s daily routine in order to tell how far they meet professional recommendations. This thesis follows the goal to develop a mobile, personalized physical activity monitoring system applicable for everyday life scenarios. From the mentioned recommendations, this thesis concentrates on monitoring aerobic physical activity. Two main objectives are defined in this context. On the one hand, the goal is to estimate the intensity of performed activities: To distinguish activities of light, moderate or vigorous effort. On the other hand, to give a more detailed description of an individual’s daily routine, the goal is to recognize basic aerobic activities (such as walk, run or cycle) and basic postures (lie, sit and stand). With recent progress in wearable sensing and computing the technological tools largely exist nowadays to create the envisioned physical activity monitoring system. Therefore, the focus of this thesis is on the development of new approaches for physical activity recognition and intensity estimation, which extend the applicability of such systems. In order to make physical activity monitoring feasible in everyday life scenarios, the thesis deals with questions such as 1) how to handle a wide range of e.g. everyday, household or sport activities and 2) how to handle various potential users. Moreover, this thesis deals with the realistic scenario where either the currently performed activity or the current user is unknown during the development and training phase of activity monitoring applications. To answer these questions, this thesis proposes and developes novel algorithms, models and evaluation techniques, and performs thorough experiments to prove their validity. The contributions of this thesis are both of theoretical and of practical value. Addressing the challenge of creating robust activity monitoring systems for everyday life the concept of other activities is introduced, various models are proposed and validated. Another key challenge is that complex activity recognition tasks exceed the potential of existing classification algorithms. Therefore, this thesis introduces a confidence-based extension of the well known AdaBoost.M1 algorithm, called ConfAdaBoost.M1. Thorough experiments show its significant performance improvement compared to commonly used boosting methods. A further major theoretical contribution is the introduction and validation of a new general concept for the personalization of physical activity recognition applications, and the development of a novel algorithm (called Dependent Experts) based on this concept. A major contribution of practical value is the introduction of a new evaluation technique (called leave-one-activity-out) to simulate when performing previously unknown activities in a physical activity monitoring system. Furthermore, the creation and benchmarking of publicly available physical activity monitoring datasets within this thesis are directly benefiting the research community. Finally, the thesis deals with issues related to the implementation of the proposed methods, in order to realize the envisioned mobile system and integrate it into a full healthcare application for aerobic activity monitoring and support in daily life.

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Metadaten
Author:Attila Reiss
URN (permanent link):urn:nbn:de:hbz:386-kluedo-36817
Advisor:Didier Stricker
Document Type:Doctoral Thesis
Language of publication:English
Publication Date:2013/12/01
Year of Publication:2014
Publishing Institute:Technische Universität Kaiserslautern
Granting Institute:Technische Universität Kaiserslautern
Acceptance Date of the Thesis:2014/09/01
Date of the Publication (Server):2014/01/13
Tag:Activity recognition; Algorithm; Boosting; Classification; Dataset; Evaluation; Feasibility study; Intensity estimation; Machine learning; Mobile system; Personalisation; Pervasive health; Physical activity monitoring; Ubiquitous system; Wearable computing
Number of page:IX, 176
Faculties / Organisational entities:Fachbereich Informatik
CCS-Classification (computer science):J. Computer Applications / J.3 LIFE AND MEDICAL SCIENCES
H. Information Systems / H.4 INFORMATION SYSTEMS APPLICATIONS / H.4.m Miscellaneous
I. Computing Methodologies / I.2 ARTIFICIAL INTELLIGENCE / I.2.1 Applications and Expert Systems (H.4, J)
I. Computing Methodologies / I.5 PATTERN RECOGNITION / I.5.4 Applications
DDC-Cassification:0 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik
6 Technik, Medizin, angewandte Wissenschaften / 620 Ingenieurwissenschaften und Maschinenbau
Licence (German):Standard gemäß KLUEDO-Leitlinien vom 10.09.2012