Graduation Year


Document Type




Degree Name

Master of Science (M.S.)

Degree Granting Department


Major Professor

Ravi Sankar, Ph.D.

Committee Member

Robert D. Frisina, Ph.D.

Committee Member

Babu Joseph, Ph.D.


Gait features, pressure sensor, statistical analysis, machine learning, neurodegenerative disease


Typically, a Parkinson’s disease (PD) patient would display instances of tremor and bradykinesia (slowness of movement) at an early stage of the disease and later develop gait disturbances and postural instability. So, it is important to measure the tremor occurrences in subjects to detect the onset of PD. Also, it is equally essential to monitor the gait impairments that the patient displays, as the order at which the PD symptoms appear in subjects vary from one to another.

The primary goal of this thesis is to develop a monitoring system for PD patients using wearable sensors. To achieve that objective, our work focused first on identifying the most significant features that would best distinguish between PD and normal healthy subjects. Here, the various gait and tremor features were extracted from the raw data collected from the wearable sensors and further analyzed using statistical analysis and pattern classification techniques to pick the most significant features. In statistical analysis, the analysis of variance (ANOVA) test was conducted to differentiate the subjects based on the values of the mean. Further, pattern classification was carried out using the Linear Discriminant Analysis (LDA) algorithm. The analysis of our results shows that the features of heel force, step distance, stance and swing phases contributed more significantly to achieving a better classification between a PD and a normal subject, in comparison with other features. Moreover, the tremor analysis based on the frequency-domain characteristics of the signal including amplitude, power distribution, frequency dispersion, and median frequency was carried out to identify PD tremor from different types of artifacts.