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dc.contributor.author
Heyn, Robert
dc.contributor.supervisor
Wittneben, Armin
dc.contributor.supervisor
Witrisal, Klaus
dc.date.accessioned
2023-03-20T11:22:01Z
dc.date.available
2023-03-19T18:32:07Z
dc.date.available
2023-03-20T11:22:01Z
dc.date.issued
2023
dc.identifier.uri
http://hdl.handle.net/20.500.11850/603793
dc.identifier.doi
10.3929/ethz-b-000603793
dc.description.abstract
This thesis concerns wearable ultra-wideband (UWB) wireless systems for human posture recognition. Our main objective is a technical proposal for a low-complexity realization of such a system. Posture recognition is a key enabler of various applications in the emerging field of technology-assisted approaches to maintain or improve human health. Especially fall prevention in the context of assisted living for elderly citizens relies on accurate body posture monitoring to detect and prevent imminent falls. Wireless body area networks (WBANs) pose a suitable means for continuous posture monitoring while preserving the user’s privacy. Such wireless systems directly offer secondary uses such as sensor data transmission. The difficulty of accurately modeling the time-varying wireless channels around the human body hinders analytical solution attempts to posture recognition. This thesis follows a measurement-based approach to posture recognition. For this purpose, we conduct a comprehensive measurement campaign. Therein we acquire the channel matrix between 18 body-mounted wireless nodes for a wide frequency range and a wide variety of postures. The postures are selected to cover an extensive range of daily activities. Measurements are performed in different environments for various test subjects of different physique in order to ensure a diverse and representative dataset. The acquired measurement data provides a solid foundation for a feasibility analysis and selection of key parameters of a posture recognition system for fall prevention from WBAN signals. The constraints of a wearable battery-powered system require the on-body wireless hardware to be of low complexity. Based on the acquired high-resolution measurement data, we distinguish three different levels of measured raw data corresponding to different hardware complexity levels. In particular, we consider the complex-valued channel impulse responses, their magnitude, and the received signal energy for each on-body link. We evaluate suitable physical layer options to measure the respective information for each level, which is subsequently used for posture recognition. In the course of a preliminary feasibility analysis, we develop a simplified system model and provide the corresponding maximum likelihood classifiers for the posture recognition problem for all three levels of measured data. Our analysis shows that posture classification based on the acquired data is feasible. It furthermore allows us to define an operating range of crucial parameters for the further system design. For the classification of postures based on channel measurements, we consider a variety of standard machine learning algorithms. Based on their quantitative comparison, Random Forests are selected as a suitable classification method which can identify postures reliably from low-complexity energy measurements. In order to further reduce the complexity and hardware demands, we minimize the number of required WBAN nodes. We demonstrate that accurate posture classification is possible with few suitably placed low-complexity nodes. This low-cost and low-complexity system design is thoroughly tested regarding its robustness towards additional disturbances and limitations of the data. The analysis includes an evaluation of the influence of the environment, cross-subject testing, and the reliability for the classification of unknown postures which are not present in the training data. Based on the findings of this work, we propose a conceptual design for a wearable posture recognition system. We outline the requirements and limitations of this design proposal for fall prevention applications and provide a realistic performance assessment of its implementation.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
ETH Zurich
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
wireless sensor networks
en_US
dc.subject
body area networks
en_US
dc.subject
ultrawideband (UWB)
en_US
dc.subject
posture recognition
en_US
dc.subject
on-body sensors
en_US
dc.subject
machine learning
en_US
dc.subject
classification
en_US
dc.title
Posture Recognition for Fall Prevention with Low-Complexity UWB WBANs
en_US
dc.type
Doctoral Thesis
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2023-03-20
ethz.size
158 p.
en_US
ethz.code.ddc
DDC - DDC::6 - Technology, medicine and applied sciences::600 - Technology (applied sciences)
en_US
ethz.code.ddc
DDC - DDC::6 - Technology, medicine and applied sciences::620 - Engineering & allied operations
en_US
ethz.identifier.diss
29013
en_US
ethz.publication.place
Zurich
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02637 - Institut für Kommunikationstechnik / Communication Technology Laboratory::03608 - Wittneben, Armin (emeritus) / Wittneben, Armin (emeritus)
en_US
ethz.date.deposited
2023-03-19T18:32:08Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2023-03-20T11:22:02Z
ethz.rosetta.lastUpdated
2024-02-02T21:09:29Z
ethz.rosetta.versionExported
true
ethz.COinS
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