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- All Subjects: Parkinson's disease
- Creators: Krishnamurthi, Narayanan
- Creators: Electrical Engineering Program
In this feasibility study, we have developed a treadmill-based experimental paradigm to provide feedback of step length and upright posture in real-time. Ten subjects (mean age 65.9 ± 7.6 years) with mild to moderate PD (Hoehn and Yahr stage III or below) were evaluated in their ability to successfully utilize real-time feedback presented during quiet standing and treadmill walking tasks during a single data collection session in their medication-on state. During quiet standing tasks in which back angle feedback was provided, subjects were asked to utilize the feedback to maintain upright posture. During treadmill walking tasks, subjects walked at their self-selected speed for five minutes without feedback, with feedback of back angle, or with feedback of step length. During walking tasks with back angle feedback, subjects were asked to utilize the feedback to maintain upright posture. During walking tasks with step length feedback, subjects were asked to utilize the feedback to walk with increased step length. During quiet standing tasks, measurements of back angle were obtained; during walking tasks, measurements of back angle, step length, and step time were obtained.
Subjects stood and walked with significantly increased upright posture during the tasks with real-time back angle feedback compared to tasks without feedback. Similarly, subjects walked with significantly increased step length during tasks with real-time step length feedback compared to tasks without feedback. These results demonstrate that people with PD can utilize real-time feedback to improve upright posture and gait.
Motivated by recent studies in motor control and therapy, in this thesis an existing computational framework is used to assess balance impairment and disease severity in people suffering from Parkinson's disease. The framework uses high-dimensional shape descriptors of the reconstructed phase space, of the subjects' center of pressure (CoP) tracings while performing dynamical postural shifts. The performance of the framework is evaluated using a dataset collected from 43 healthy and 17 Parkinson's disease impaired subjects, and outperforms other methods, such as dynamical shift indices and use of chaotic invariants, in assessment of balance impairment.
In this thesis, an unsupervised method is also proposed that measures movement quality assessment of simple actions like sit-to-stand and dynamic posture shifts by modeling the deviation of a given movement from an ideal movement path in the configuration space, i.e. the quality of movement is directly related to similarity to the ideal trajectory, between the start and end pose. The S^1xS^1 configuration space was used to model the interaction of two joint angles in sit-to-stand actions, and the R^2 space was used to model the subject's CoP while performing dynamic posture shifts for application in movement quality estimation.
This paper serves to report the research performed towards detecting PD and the effects of medication through the use of machine learning and finger tapping data collected through mobile devices. The primary objective for this research is to prototype a PD classification model and a medication classification model that predict the following: the individual’s disease status and the medication intake time relative to performing the finger-tapping activity, respectively.
This paper serves to report the research performed towards detecting PD and the effects of medication through the use of machine learning and finger tapping data collected through mobile devices. The primary objective for this research is to prototype a PD classification model and a medication classification model that predict the following: the individual’s disease status and the medication intake time relative to performing the finger-tapping activity, respectively.