United States

Currently there is much research in the area of person identification. Mostly it is based on multi-biometric data. In this paper, we aim to leverage soft biometric properties to achieve person reidentification by using unobtrusive sensors, envisioning assistive environments, which recognize their user and thus automatically personalize and adapt. In practice, a car seat recognizes the person
who sits down and greets the person with their own name, enabling various customisation in the car unique to the user, like seat configurations.
We present a system composed of a sensor equipped car seat, which is able to recognize a person from a predefined group. We contribute two classification approaches based on cosine similarity measure and on triplet loss learning. These are thoroughly analysed and evaluated in a user study with nine participants. We achieve the best re-identification performance using a hand-crafted feature approach based on the comparing measure of cosine similarity combined with majority voting. The highest overall precision achieved in re-identifying a person from a group of nine users is 80 %.

PETRA 2021

Person Re-Identification in a Car Seat: Comparison of Cosine Similarity and Triplet Loss based approaches on Capacitive Proximity Sensing data

automotive

capacitive proximity sensing

automatic identification system (ais)

softbiometrics

machine learning

The 9th Brazilian Conference on Intelligent Systems (BRACIS), the 16h Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), and the 12th Concurso de Teses e Dissertações em Inteligência Artificial e Computacional (CTDIAC) was held from 20 to 23 October, 2020.

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence - SBIA (22 editions), and the Brazilian Symposium on Neural Networks - SBRN (13 editions). ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. CTDIAC is a satellite event whose goals are to disseminate and award the best dissertations and theses (MSc and PhDs concluded in Brazilian universities from June 2018 to May 2020) in Artificial and Computational Intelligence areas.

In this year, the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) will be held in conjunction with BRACIS 2020. This sysposium aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences – related to the Data Mining and Machine Learning areas.

BRACIS 2020

technical paper

**About**
 The PErvasive Technologies Related to Assistive Environments (PETRA) conference is a highly interdisciplinary conference that focuses on computational and engineering approaches to improve the quality of life and enhance human performance in a wide range of settings, in the workplace, at home, in public spaces, urban environments, and other. Outcomes of this conference have a broad impact in application areas that include, manufacturing, transportation, healthcare, energy systems, security and safety, robotics, biomedicine, environment and conservation, and many others. PETRA brings together very different types of technologies to also address important social and healthcare issues for sensitive populations, such as the elderly, persons suffering from chronic conditions such as Alzheimer’s, Parkinson’s, Cerebral Palsy, and other disabilities or traumas.

 PETRA (which means “stone” in Greek) addresses important human living needs. From a technological perspective, PETRA motivates and inspires new scientific thrusts, innovations and discoveries to improve living environments, a particularly important issue, in an increasingly crowded and complex world where the need for inclusiveness and connectivity with the rest of the world is key. For the elderly in particular, PETRA addresses the fact that, as people grow older; they will increasingly rely on technology to be able to prolong staying in their homes and enjoy an independent life.

 The PETRA 2021 proceedings will be published in the ACM Digital Library as part of the ACM ICPS program. The ISBN number assigned to PETRA '21 is 978-1-4503-8792-7

The PErvasive Technologies Related to Assistive Environments (PETRA) conference is a highly interdisciplinary conference that focuses on computational and engineering approaches to improve the quality of life and enhance human performance in a wide range of settings, in the workplace, at home, in public spaces, urban environments, and other. Outcomes of this conference have a broad impact in application areas that include, manufacturing, transportation, healthcare, energy systems, security and safety, robotics, biomedicine, environment and conservation, and many others. PETRA brings together very different types of technologies to also address important social and healthcare issues for sensitive populations, such as the elderly, persons suffering from chronic conditions such as Alzheimer’s, Parkinson’s, Cerebral Palsy, and other disabilities or traumas.

Increasing digitization in healthcare promises easier exchange and more efficient use of medical information for patients, institutions and research. 
The number of sharing options for medical data increases, e.g., through personal health records, as well as the volume of data.
To use this data in medical research patients' consent is important.
As more and more data access is regulated by consent forms and their complexity also increases.
For the patient, it becomes less comprehensible which information could be gained from his or her disclosed data. 
This becomes more important when presumably anonymized data has the risk of potential re-identification of an individual.
In this paper we introduce a consent-privacy-impact-quantification (CPIQ) as a risk model of consent forms for the release of personal medical data for use within research projects.
CPIQ evaluates how reasonable a consent decision is for the patient. 
It takes relevant factors such as the patient’s preferences into account, the circumstances and benefits of the research project, and the potential risk to the patient.
The model can be parameterized so that different aspects such as the benefit of the research project, the risk of a data leak or the risk of a patient’s confidential data becoming known can be represented.
We present the feasibility of this model by including it in an existing consent management system.

CPIQ - A Privacy Impact Quantification for Digital Medical Consent

In a pandemic crisis such as the one that the world has been experiencing since January 2020, utilizing remote mobile health monitoring can help monitor the cardiac health of healthy individuals as well as chronically ill patients. Apple Watch is a fitness tracker that measures heart rate and can facilitate this need due to its availability worldwide and relative affordability. Hence, it has the potential of being useful in assessing and monitoring one’s cardiac health. This paper reports the preliminary results from an ongoing study aimed at assessing the accuracy of the Apple Watch in measuring heart rate variability. To achieve the goal of the study, concurrent measurements of electrocardiogram (ECG), as a gold standard, and instantaneous Apple Watch heartrate values were made in six (4 male) age 26.4±3.2 years old volunteer healthy subjects. Both time and frequency domain measures of heartrate variability (HRV) were obtained from the recorded ECG and estimated from the Apple Watch instantaneous heartrate values. The results indicate that it may be possible to obtain a reasonable estimate of HRV time-domain and frequency-domain metrics using Apple Watch in healthy subjects.

Estimation of Heart Rate Variability Measures Using Apple Watch and Evaluating Their Accuracy

Corporate health management is an important tool for preventing work-related absenteeism, increasing overall employee satisfaction and reducing the costs of absenteeism or presenteeism in the long term. Today, corporate health management is even more important because many employees work from home. Often, there is a lack of workspace or even a workplace that meets minimum occupational health and safety guidelines. Overwork, noise pollution, incorrect sitting posture and unstructured work breaks contribute negatively to the daily work routine, as does the general problem of separating work and leisure. Under these conditions, daily work is made even more difficult, which can lead to increased mental and physical stress. A concept for unobtrusive monitoring to increase long-term health, improve working conditions or at least to show the necessary adjustments to the new work situation can help to solve these problems. This paper presents a concept that shows how a simple webcam can be used to record essential vital signs during working hours, evaluate them using machine learning, and offer intervention recommendations based on these data to reduce psychological and physical stress. Work on continuous stress measurement and the challenges associated with it will be presented. This work serves as a starting point for the development of a camera-based tool for mental and physical stress measurement in the workplace. Our approach demonstrates that the required parameters can be captured using a simple webcam and that interventions can be used to achieve long-term reductions in work-related mental and physical stress, provided that the proposed interventions are followed. The prototypical implementation shows that such a solution can work well in the workplace, but that data protection and technical limitations must be considered in the future in order to establish camera-based methods in the toolbox of workplace health management.

CareCam: Concept of a new tool for Corporate Health Management

The analysis of large data sets for several purposes is a growing requirement for many professions. Tactile charts are suitable to enable people with visual impairment and blindness performing data analysis tasks. However, few approaches focus on the development of tactile charts for data analysis purposes. Concepts are needed to enable the representation of a sufficient amount of data with tactile charts and to address arising challenges, such as information overload. In this paper, we first discuss and analyse the scalability of data represented by tactile charts, using the example of tactile scatterplots. We further address the data size limitations and propose a workflow for identifying critical, tactile representation with limited readability respecting the analysis task. Moreover, we propose concepts to increase the represented amount of data in tactile scatterplots. Based on our analysis results, we introduce tactile heatmaps as an innovative and new concept to represent data
for the haptic sense. Therefore, we developed a design for tactile heatmaps by utilising different elevation levels. We evaluated the concept as well as the feasibility of different elevation levels with 11 blind and visually impaired people. Four design conditions for embossed tactile heatmaps were compared. We further evaluated
the suitability of 3D-printed heatmaps. The results show that tactile heatmaps are suitable for representing more data than previously known tactile representation methods. They support obtaining an overview of a high amount of data and can be applied for data analysis purposes.

Tactile Heatmaps: A Novel Visualisation Technique for Data Analysis with Tactile Charts

Retinopathy of Prematurity (ROP) is a disease affecting premature infants and may lead to childhood blindness. Due to lack of trained ophthalmologists, developing a fully automated ROP diagnostic system can significantly benefit the infants affected by ROP. Based on manually segmented features, previous work produces severity scores for ROP with excellent prediction accuracy. However, when automated segmentation employed, a significant accuracy drop is observed. In this paper, we show that U-Net segmentation, which is automated, comes at no accuracy loss.

Automated ROP Diagnostic System based on Comparisons and U-Net Segmentation

In pandemic times, public facilities, such as gymnasiums and other sports facilities are closed. Hence, keeping oneself physically fit becomes particularly difficult as well as getting help from a fitness trainer or instructor is almost impossible. We propose an alternative solution, GymSoles++, a virtual trainer supporting the user with their exercise. This allows users to maintain the correct body posture to avoid injuries when performing exercises such as squats. We combine Google Glass with an unobtrusive sensing approach, an insole-based wearable system that provides feedback on the user's centre of pressure (CoP) via vibrotactile and visual aids. Previous research has shown that solely visualizing the CoP can significantly improve body posture and thus effectively assist users when performing squats and dead-lifts. In this research, we explored different feedback modalities and conclude that a vibrotactile insole is a practical and effective solution.

Combining Google Glass with Smart Insoles to Improve Body Posture when Performing Squats

Due to the rapid development of information technology, health monitoring research demands are growing tremendously. People prefer to use wearable devices that consist of sensors, which help in monitoring the health of individuals. This study aims to develop a system that remotely monitors the physiological aspects of individuals, such as blood oxygen saturation and pulse measurement, using wearable sensors and a new cloud-based fuzzy logic system, and generates necessary alarms. Monitoring health remotely based on wearable and non-invasive sensors provides a cost-effective solution, which in turn permits the elderly people to continue to live in his/her home environment as opposed to spending on expensive healthcare facilities. The developed system shall permit smooth physiological monitoring of an individual’s health by healthcare personnel in real-time. The developed system will not only assess health conditions but also provide feedback from distant facilities.
Moreover, the developed rule-based fuzzy system can infer an individual’s health condition. The study uses pulse rate and oxygen saturation data, which are processed by a fuzzy system on the cloud, and the acquired results are displayed on a mobile application.

A cloud-based pervasive application for monitoring oxygen saturation and heart rate using fuzzy-as-a-service

Detection of fake signatures is a hard task. In this paper, we present a novel method for detecting trained forgeries using features extracted from sliding windows with different overlaps on a public available dataset of static images of signatures. Using a linear machine learning model named Extreme Learning Machine (ELM), our methodology achieves, in average, an Equal Error Rates (EER) of 2.31% for an overlap of 90%. In line with the state-of-the-art results available in the scientific literature.

Handwriting features based detection of fake signatures

In domains such as Human-Robot Collaboration artificial agents must be able to support mutual adaptation and learning. Towards this direction, we use a discrete Soft Actor-Critic agent on a real-time collaborative game with humans. We examine how different allocations of on-line and off-line gradient updates impact the game performance and the total training time. Our results suggest that early allocation of a high number of off-line g/u can accelerate learning while shortening training duration.

Accelerating Human-Agent Collaborative Reinforcement Learning

In this presentation, we propose Structurally Guided Channel Attention Networks (SGCA-Net), a principled way to guide the channel at- tention of CNNs. Convolution operator constructs features maps by using both channel and spatial information within the receptive fields of its filters. Prior research has investigated the impact of strengthening the representational power of CNNs using channel attention modules. In this work, we guide the channel attention of networks using feature vectors that contain clinically relevant information. We do so by attaching guided attention modules into a state-of-the-art network architecture, and guiding these attention modules with domain knowledge using feature vectors. Experi- ments on a dataset of 5512 posterior retinal images, taken using a wide angle fundus camera, show that SGCA-Net achieves 0.983 and 0.948 AUC to predict plus and normal categories, respectively. SGCA-Net achieves higher performance than CNNs without atten- tion modules and CNNs with unguided attention modules.

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