Master of Engineering Science
Naish, Michael D.
Parkinson's disease (PD) is the second most prevalent neurodegenerative disease, with over 10 million individuals diagnosed with PD world-wide. The most common symptom characterized by PD is tremor. Tremor is an involuntary oscillatory motion that most prominently occurs in upper limb, specifically in the hand and wrist that has a measurable frequency and amplitude. This thesis aims to evaluate the usability and functionality of a tremor sensing device designed to collect quantitative data on individuals with PD. The designed device uses 23 commercially-available inertial measuring units (IMUs) located between 21 joints: distal interphalangeal (DIP) joints, proximal interphalangeal (PIP) joints, Interphalangeal (IP) joint, metacarpophalangeal (MCP) joints, carpometacarpal (CMC) joint, trapeziometacarpal (TMC) joint, radiocarpal joint, and the elbow joint. The IMU sensors include a 3 degree of freedom (DOF) accelerometer and a 3 DOF gyroscope activated during data collection. In specific, this thesis evaluates the device with trials on healthy participants by collecting data in the time and frequency domain during activities of daily living (ADL) over 48 hours in a home setting.
A total of 7 healthy participants were recruited to wear the device in a home setting over 2 days. The linear acceleration and angular velocity signals were captured, which were later used to analyze the data in the frequency domain, similar to if it were for tremor signals. If the voluntary motion signals in the time and frequency domain are close to the accepted values for voluntary motion, the battery life is sufficient, and data is collected effectively, the device functionality will be validated and can be used to capture tremor data.
Summary for Lay Audience
Tremor is one of the most common symptoms of Parkinson's Disease (PD), often making it hard to perform daily tasks such as typing, writing, eating etc. Individuals with PD generally do not see a neurologist regularly, and the progression and behaviour of tremor is not examined as often as possible. Neurologists can conduct an assessment on individuals with PD to examine their motor skills, but the assessment is based on visual observation instead of quantitative data in a short time frame, and in a clinical setting. Some individuals may be nervous when visiting a neurologist, or their tremor may not behave the same as it does in a home setting. If their tremor during the assessment does not accurately reflect how tremor acts in their daily life, their symptoms may not be given the most effective treatment. A portable device that can collect motion data from individuals could help capture tremor and better understand how tremor impacts daily living, how often they occur, and other features.
A total of 7 healthy participants were recruited to validate a developed wearable device that uses sensors to collect voluntary motion data in a home setting during daily tasks. The participants were asked to wear the device over 2 days to help validate the functionality of the device, so that in the future it can be used to collect data on individuals with PD in a home setting.
The results of this study show that the data collected for voluntary motion fall within the expected range for linear acceleration and the data can be used to find frequency of movements and power. The same procedure can be followed to analyze tremor data in the future. Some of the sensors had similarities between certain joints on different fingers, so it is possible to eventually use this work as a basis to create more compact designs in the future. In addition, the results from the participants' assessments of the device and trial can be taken into consideration when developing improved wearable devices.
Kalsi, Jaspreet K., "The Development of a Motion Sensing Device for Use in a Home Setting" (2022). Electronic Thesis and Dissertation Repository. 8847.
Biomedical Commons, Biomedical Devices and Instrumentation Commons, Electrical and Electronics Commons, Electronic Devices and Semiconductor Manufacturing Commons, Health Information Technology Commons, Other Biomedical Engineering and Bioengineering Commons, Signal Processing Commons