Download Data driven approaches to Human Activity Recognition using

Title
Data driven approaches to human activity recognition using smartphone and environmental
based sensors
Proposed Supervisor
Dr Mark Donnelly, School of Computing and Mathematics, Jordanstown, Room 16J14, Tel:
02890366330, email: [email protected])
Aim and Rationale
With the emergence of low cost, low power wireless devices, dense sensing, coupled with
onboard smartphone sensors offers the potential to unobtrusively monitor and recognize a
range of human activity as a user transitions between environments, for example,
transitioning from breakfast at home, to travelling to work, to undertaking work related
activity. This project aims to investigate and compare the extent to which data driven
approaches can determine transitional activities of daily living via a combination of onboard
smartphone and environmental sensor events.
Methodology
The project will involve a review of the state of the art in sensor based activity recognition.
Consequently, the student will be required to undertake a range of experiments to collect
and annotate sensor events during a lab-based simulation of different human activities. The
experiment should consider the number and deployment location of sensors, required for
successfully recognizing different activities. Consequently, for each deployment
configuration, the project will compare computational approaches against unseen test sets.
Feature selection / extraction will form part of the optimization process.
Anticipated outcomes
The outcomes from this project should provide an increased understanding of the
opportunities and challenges for data driven sensor based activity recognition across
different environments. The project should also report on those sensor features and
deployment configurations that are optimal for recognizing different human activities.
Resources Required
The hardware required to undertaken this research can be supported by the Smart
Environments Research Group through access to a range of environmental based sensors for
in-situ experiments. Student undertaking this project should ideally have his or her own
access to an accelerometer enabled smartphone.
Health and Safety Issues
It is the responsibility of the student to ensure that no dangerous or irresponsible activities
are undertaken during the abovementioned experiments.
Ethical Considerations
The project will involve the collection of data, representing human activity. As such, should
the student wish to recruit participants then appropriate ethical approval will be required.