Towards a Robotic System for Minimally Invasive Breast Interventions

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Semi-Supervised Generative Adversarial Network for Stress Detection Using Partially Labeled Physiological Data

Physiological measurements involves observing variables that attribute to the normative functioning of human systems and subsystems directly or indirectly. The measurements can be used to detect affective states of a person with aims such as improving human-computer interactions. There are several methods of collecting physiological data, but wearable sensors are a common, non-invasive tool for accurate readings. However, valuable information is hard to extract from the raw physiological data, especially for affective state detection. Machine Learning techniques are used to detect the affective state of a person through labeled physiological data. A clear problem with using labeled data is creating accurate labels. An expert is needed to analyze a form of recording of participants and mark sections with different states such as stress and calm. While expensive, this method delivers a complete dataset with labeled data that can be used in any number of supervised algorithms. An interesting question arises from the expensive labeling: how can we reduce the cost while maintaining high accuracy? Semi-Supervised learning (SSL) is a potential solution to this problem. These algorithms allow for machine learning models to be trained with only a small subset of labeled data (unlike unsupervised which use no labels). They provide a way of avoiding expensive labeling. This paper compares a fully supervised algorithm to a SSL on the public WESAD (Wearable Stress and Affect Detection) Dataset for stress detection. This paper shows that Semi-Supervised algorithms are a viable method for inexpensive affective state detection systems with accurate results.Comment: 12 page

Dynamic Control of 3-D Rolling Contacts in Two-Arm Manipulation

When two or more arms are used to manipulate a large object, it is preferable not to have a rigid grasp in order to gain more dexterity in manipulation. It may therefore be necessary to control contact motion between the object and the effector(s) on one or more arms. This paper addresses the dynamic control of two arms cooperatively manipulating a large object with rolling contacts. In the framework presented here, the motion of the object as well as the loci of the contact point either on the surface of each effector or on the object can be directly controlled. The velocity and acceleration equations for three-dimensional rolling contacts are derived in order to obtain a dynamic model of the system. A nonlinear feedback control algorithm that decouples and linearizes the system is developed. This is used to demonstrate the control of rolling motion along each arm and the adaptation of grasps to varying loads