Abstract

Abstract
Inertial Measurement Units (IMUs) have become a popular solution for tracking human motion. The main problem of using IMU data for deriving the position of different body segments throughout time is related to the accumulation of the errors in the inertial data. The solution to this problem is necessary to improve the use of IMUs for position tracking. In this work, we present several Machine Learning (ML) methods to improve the position tracking of various body segments when performing different movements. Firstly, classifiers were used to identify the periods in which the IMUs were stopped (zero-velocity detection). The models Random Forest, Support Vector Machine (SVM) and neural networks based on Long-Short-Term Memory (LSTM) layers were capable of identifying those periods independently of the motion and body segment with a substantially higher performance than the traditional fixed-threshold zero-velocity detectors. Afterwards, these techniques were combined with ML regression models based on LSTMs capable of estimating the displacement of the sensors during periods of movement. These models did not show significant improvements when compared with the more straightforward double integration of the linear acceleration data with drift removal for translational motion estimate. Finally, we present a model based on LSTMs that combined simultaneously zero-velocity detection with the translational motion of sensors estimate. This model revealed a lower average error for position tracking than the combination of the previously referred methodologies.

Abstract
Despite recent efforts, accuracy in group emotion recognition is still generally low. One of the reasons for these underwhelming performance levels is the scarcity of available labeled data which, like the literature approaches, is mainly focused on still images. In this work, we address this problem by adapting an inflated ResNet-50 pretrained for a similar task, activity recognition, where large labeled video datasets are available. Audio information is processed using a Bidirectional Long Short-Term Memory (Bi-LSTM) network receiving extracted features. A multimodal approach fuses audio and video information at the score level using a support vector machine classifier. Evaluation with data from the EmotiW 2020 AV Group-Level Emotion sub-challenge shows a final test accuracy of 65.74% for the multimodal approach, approximately 18% higher than the official baseline. The results show that using activity recognition pretraining offers performance advantages for group-emotion recognition and that audio is essential to improve the accuracy and robustness of video-based recognition. © 2020 IEEE.

Abstract
Despite the growing popularity of 802.11 wireless networks, users often suffer from connectivity problems and performance issues due to unstable radio conditions and dynamic user behavior, among other reasons. Anomaly detection and distinction are in the thick of major challenges that network managers encounter. The difficulty of monitoring broad and complex Wireless Local Area Networks, that often requires heavy instrumentation of the user devices, makes anomaly detection analysis even harder. In this paper we exploit 802.11 access point usage data and propose an anomaly detection technique based on Hidden Markov Model (HMM) and Universal Background Model (UBM) on data that is inexpensive to obtain. We then generate a number of network anomalous scenarios in OMNeT++/INET network simulator and compare the detection outcomes with those in baseline approaches-RawData and Principal Component Analysis. The experimental results show the superiority of HMM and HMM-UBM models in detection precision and sensitivity.

Abstract
Magnetic resonance imaging (MRI) is the most commonly used exam for diagnosis and follow-up of neurodegenerative diseases, such as multiple sclerosis (MS). MS is a neuroinflammatory and neurodegenerative disease characterized by demyelination of neuron axon. This demyelination process causes lesions in white matter that can be observed in vivo by MRI. Such lesions may provide quantitative assessments of the inflammatory activity of the disease. Quantitative measures based on various features of lesions have been shown to be useful in clinical trials for evaluating therapies. Although manual segmentations are considered as the gold standard, this process is time consuming and error prone. Therefore, automated lesion identification and quantification of the MRI are active areas in MS research. The purpose of this study was to perform the brain lesions volumetric quantification in MS patients, after segmentation via a convolutional neural network (CNN) model. Initially, MRI was rigidly registered, skullstripped and bias corrected. After, we use the CNN for brain lesions segmentation, which used training data to identify lesions within new test subjects. Finally, volume quantification was performed with a count of segmented voxels and represented by mm(3). We did not observe a statistical difference between the volume of brain lesion automatically identified and the volume manually segmented. The use of deep learning techniques in health is constantly developing. We observed that the use of these computational method for segmentation and quantification of brain lesions can be applied to aid in diagnosis and follow-up of MS.

Abstract
With the appearance of Shared Autonomous Vehicles there will no longer be a driver responsible for maintaining the car interior and well-being of passengers. To counter this, it is imperative to have a system that is able to detect any abnormal behaviors, more specifically, violence between passengers. Traditional action recognition algorithms build models around known interactions but activities can be so diverse, that having a dataset that incorporates most use cases is unattainable. While action recognition models are normally trained on all the defined activities and directly output a score that classifies the likelihood of violence, video anomaly detection algorithms present themselves as an alternative approach to build a good discriminative model since usually only non-violent examples are needed. This work focuses on anomaly detection and action recognition algorithms trained, validated and tested on a subset of human behavior video sequences from Bosch's internal datasets. The anomaly detection network architecture defines how to properly reconstruct normal frame sequences so that during testing, each sequence can be classified as normal or abnormal based on its reconstruction error. With these errors, regularity scores are inferred showing the predicted regularity of each frame. The resulting framework is a viable addition to traditional action recognition algorithms since it can work as a tool for detecting unknown actions, strange/violent behaviors and aid in understanding the meaning of such human interactions.