Abstract

A new wearable band is developed which uses three Photoplethysmography (PPG) sensors for the purpose of hand gesture recognition (HGR). These sensors are typically used for heart rate estimation and detection of cardiovascular diseases. Heart rate estimates obtained from these sensors are disregarded when the arm is in motion on account of artifacts. This research suggests and demonstrates that these artifacts are repeatable in nature based on the gestures performed. A comparative study is made between the developed band and the Myo Armband which uses surface-Electromyography (s-EMG) for gesture recognition. Based on the results of this paper which employs supervised machine learning techniques, it can be seen that PPG can be utilized as a viable alternative modality for gesture recognition applications.

Library of Congress Subject Headings

Plethysmography; Wearable technology; Hand--Movements--Measurement; Pattern perception; Machine learning

Publication Date

7-2020

Document Type

Thesis

Student Type

Graduate

Degree Name

Electrical Engineering (MS)

Department, Program, or Center

Electrical Engineering (KGCOE)

Advisor

Ferat Sahin

Advisor/Committee Member

Gill Tsouri

Advisor/Committee Member

Jamison Heard

Campus

RIT – Main Campus

Plan Codes

EEEE-MS

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