Emotion Recognition from Physiological Signals Collected with a Wrist Device and Emotional Recall.

Implementing affective engineering in real-life applications requires the ability to effectively recognize emotions using physiological measurements. Despite being a widely researched topic, there seems to be a lack of systems that translate results from data collected in a laboratory setting to higher technology readiness levels. In this paper, we delve into the feasibility of emotion recognition beyond controlled laboratory environments. For this reason, we create a minimally-invasive experimental setup by combining emotional recall via autobiographical emotion memory tasks with a user-friendly Empatica wristband measuring blood volume pressure, electrodermal activity, skin temperature, and acceleration. We employ standard practices of feature-based supervised learning and specifically use support vector machines to explore subject dependency through various segmentation methods. We collected data from 45 participants. After preprocessing, using a data set of 134 segments from 40 participants, the accuracy of the classifier after 10-fold cross-validation was barely better than random guessing (36% for four emotions). However, when extracting multiple segments from each emotion task per participant using 10-fold cross-validation (i.e., including subject-dependent data in the training set), the classification rate increased to up to 75% for four emotions but was still as low as 32% for leave-one-subject-out cross-validation (i.e., subject-independent training). We conclude that highly subject-dependent issues might pose emotion recognition.

Calibrating Range Measurements of Lidars Using Fixed Landmarks in Unknown Positions.

We consider the problem of calibrating range measurements of a Light Detection and Ranging (lidar) sensor that is dealing with the sensor nonlinearity and heteroskedastic, range-dependent, measurement error. We solved the calibration problem without using additional hardware, but rather exploiting assumptions on the environment surrounding the sensor during the calibration procedure. More specifically we consider the assumption of calibrating the sensor by placing it in an environment so that its measurements lie in a 2D plane that is parallel to the ground. Then, its measurements come from fixed objects that develop orthogonally w.r.t. the ground, so that they may be considered as fixed points in an inertial reference frame. Moreover, we consider the intuition that moving the distance sensor within this environment implies that its measurements should be such that the relative distances and angles among the fixed points above remain the same. We thus exploit this intuition to cast the sensor calibration problem as making its measurements comply with this assumption that "fixed features shall have fixed relative distances and angles". The resulting calibration procedure does thus not need to use additional (typically expensive) equipment, nor deploy special hardware. As for the proposed estimation strategies, from a mathematical perspective we consider models that lead to analytically solvable equations, so to enable deployment in embedded systems. Besides proposing the estimators we moreover analyze their statistical performance both in simulation and with field tests. We report the dependency of the MSE performance of the calibration procedure as a function of the sensor noise levels, and observe that in field tests the approach can lead to a tenfold improvement in the accuracy of the raw measurements.