143 research outputs found
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
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