Research Projects

Unsupervised Deep Feature Extraction for Visual Complexity Analysis

In this work, we propose a new direction in unsupervised information extraction from intermediate convolutional layers of deep neural networks. We focus on visual com- plexity, an image attribute that humans can subjectively evaluate based on the level of detail in the image. We derive an activation energy (AE) metric that combines convolutional layer activations to quantify visual complexity. In addition, we introduce Savoias, a visual complexity dataset that compromises of more than 1,400 images from seven diverse image categories.

ExerciseCheck: Remote Monitoring and Evaluation Platform for Home Based Physical Therapy

We present ExerciseCheck, a remote monitoring and evaluation platform for individuals involved in a home exercise program. The goal of the platform is to give patients feedback about their performance and, if needed, and how they should adjust their movements. ExerciseCheck is designed for a therapist to remotely monitor a patient in real time, enabling the therapist to give instant feedback or further instructions.

Dynamic Adjustment of Physical Exercises Using Reinforcement Learning

In this project, we propose a system for divculty adjustment of exercises in physical therapy. We use pattern recognition algorithms (Dynamic Time Warping and Spectral Arc Length), to extract performance measures; then we design our online reinforcement learning algorithm to dynamically adjusts the divculty conguration of exercises based on the users performance.

Posture Modeling for Exercise Assessment

We propose a method to capture and model the postures of a user for during a physical therapy exercise. We capture the skeletal data using the Microsoft Kinect, extract features and train a Gaussian Mixture Model (GMM) to represent postures of the user during an exercise. Our model enables autonomous detection of incorrect postures during an exercise.

Publications