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Full-Text Articles in Computer Engineering
Multi-Classifier Fusion Strategy For Activity And Intent Recognition Of Torso Movements, Abhijit Kadrolkar
Multi-Classifier Fusion Strategy For Activity And Intent Recognition Of Torso Movements, Abhijit Kadrolkar
Doctoral Dissertations
As assistive, wearable robotic devices are being developed to physically assist their users, it has become crucial to develop safe, reliable methods to coordinate the device with the intentions and motions of the wearer. This dissertation investigates the recognition of user intent during flexion and extension of the human torso in the sagittal plane to be used for control of an assistive exoskeleton for the human torso. A multi-sensor intent recognition approach is developed that combines information from surface electromyogram (sEMG) signals from the user’s muscles and inertial sensors mounted on the user’s body. Intent recognition is implemented by following …
Intent Recognition Of Rotation Versus Translation Movements In Human-Robot Collaborative Manipulation Tasks, Vinh Q. Nguyen
Intent Recognition Of Rotation Versus Translation Movements In Human-Robot Collaborative Manipulation Tasks, Vinh Q. Nguyen
Masters Theses
The goal of this thesis is to enable a robot to actively collaborate with a person to move an object in an efficient, smooth and robust manner. For a robot to actively assist a person it is key that the robot recognizes the actions or phases of a collaborative tasks. This requires the robot to have the ability to estimate a person’s movement intent. A hurdle in collaboratively moving an object is determining whether the partner is trying to rotate or translate the object (the rotation versus translation problem). In this thesis, Hidden Markov Models (HMM) are used to recognize …
Activity Intent Recognition Of The Torso Based On Surface Electromyography And Inertial Measurement Units, Zhe Zhang
Masters Theses 1911 - February 2014
This thesis presents an activity mode intent recognition approach for safe, robust and reliable control of powered backbone exoskeleton. The thesis presents the background and a concept for a powered backbone exoskeleton that would work in parallel with a user. The necessary prerequisites for the thesis are presented, including the collection and processing of surface electromyography signals and inertial sensor data to recognize the user’s activity. The development of activity mode intent recognizer was described based on decision tree classification in order to leverage its computational efficiency. The intent recognizer is a high-level supervisory controller that belongs to a three-level …