Engineering Commons^™

Stationary Exercise Classification Using Imus And Deep Learning, Andrew M. Heroy, Zackary Gill, Samantha Sprague, David Stroud, John Santerre

SMU Data Science Review

In the current market, successful fitness tracking devices utilize heart rate and GPS to determine performance. These devices are useful, but don't extensively classify stationary exercise. This paper proposes a modern approach for tuning and investigating optimal neural network types on stationary exercises using Inertial Measurement Units (IMUs). Using three IMUs located on the ankle, waist, and wrist, data is collected to map the body as it moves during the stationary physical activity. A novel five-stage deep learning tuning system was written and deployed to classify user movement as one of three classes: air squats, jumping jacks, and kettlebell swings. …

Stability Analysis, Dynamic Modeling, And Kinematic Analysis Of Hydraulically Amplified Dielectric Elastomer Actuators And Robot Manipulators, Amir Hosein Zamanian

Mechanical Engineering Research Theses and Dissertations

In this dissertation, we have proposed a new dielectric elastomer actuator design and model that couples electrostatics, fluid mechanics, linear and nonlinear elastic deformations. The internal hydraulic pressure can amplify the structural deformation generated by induced electrostatic forces (Maxwell pressure), given the name of this class of actuators, hydraulically amplified dielectric elastomer actuators (HADEAs).

First, we developed novel lumped-parameter models (LPMs) using linear and hyper-elastic material models and second compared the LPM results with finite element analysis in quasi-static simulations. We analytically expressed the conditions for the snap-through instability in the HADEA, which appears after exceeding a certain voltage applied …