My real life tasks would have included daily bike rides, weekly whitewater canoeing, xc skiing in winter, running, sea kayaking on Lake Superior.
http://etheses.bham.ac.uk/7763/
Nabiei, Roozbeh (2017)
Ph.D. thesis, University of Birmingham.
Ph.D. thesis, University of Birmingham.
Nabiei17PhD.pdf
PDF - Accepted Version Restricted to Repository staff only until 01 December 2017.
| Abstract
Assisting
patients to perform activities of daily living (ADLs) is a challenging
task for both human and machine. Hence, developing a computer-based
rehabilitation system to re-train patients to carry out daily activities
is an essential step towards facilitating rehabilitation of stroke
patients with apraxia and action disorganization syndrome (AADS). This
thesis presents a real-time Hidden Markov Model (HMM) based human
activity recognizer, and proposes a technique to reduce the time delay
occurred during the decoding stage. Results are reported for complete
tea-making trials. In this study, the input features are recorded using
sensors attached to the objects involved in the tea making task, plus
hand coordinate data captured using Kinect sensor. A coaster of sensors,
comprising an accelerometer and three force-sensitive resistors, are
packaged in a unit which can be easily attached to the base of an
object. A parallel asynchronous set of detectors, each responsible for
the detection of one sub-goal in the tea-making task, are used to
address challenges arising from overlaps between human actions.
In this work HMMs are used to exploit temporal dependencies between actions and emission distributions are modelled by two generative and discriminative modelling techniques namely Gaussian Mixture Models (GMMs) and Deep Neural Networks (DNNs). Our experimental results show that HMM-DNN based systems outperform the GMM-HMM based systems by 18%. The proposed activity recognition system with the modified HMM topology provides a practical solution to the action recognition problem and reduces the time delay by 64% with no loss in accuracy. |
| Type of Work: | Ph.D. thesis. |
|---|---|
| Supervisor(s): | Russell, Martin and Jancovic, Peter |
| School/Faculty: | Colleges (2008 onwards) > College of Engineering & Physical Sciences |
| Department: | School of Electronic, Electrical and System Engineering |
| Subjects: | RC Internal medicine TK Electrical engineering. Electronics Nuclear engineering |
| Institution: | University of Birmingham |
| ID Code: | 7763 |
Just because this program gives details about every step does not mean a stroke survivor has a prayer of producing the required motion. Better suited to clients with cognitive deficits like those with a closed head injury or mental retardation.
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