United States

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.

PETRA 2021

CareCam: Concept of a new tool for Corporate Health Management

remote photoplethysmography

stress recognition

affective computing

machine learning

The aim of ESSCIRC and ESSDERC is to provide an annual European forum for the presentation and discussion of recent advances in solid-state devices and circuits.
The level of integration for system-on-chip design is rapidly increasing.

This is made available by advances in semiconductor technology. Therefore, more than ever before, a deeper interaction among technologists, device experts, IC designers, and system designers is necessary. ESSCIRC and ESSDERC are governed by a common Steering Committee and share Plenary Keynote Presentations and Joint Sessions bridging both device and circuit communities.

Highlights:
* 3 joint keynote presentations
* 3 keynote presentations ESSCIRC
* 3 keynote presentations ESSDERC
* Invited papers with overall coverage of all aspects of advanced devices and circuits
* Presentation of IEEE and ESSCIRC/ESSDERC Awards
* Educational Events

JOINT PLENARY talks live broadcast: September 13, 2021 
ESSCIRC and ESSDERC KEYNOTE talks live broadcast: September 15, 2021 
LIVE Q&A INTERACTIVE sessions for all Conference Technical sessions: September 14 – 16 – 17, 2021 
LIVE Q&A INTERACTIVE sessions for all Educational Events: September 20-21-22, 2021 
(on demand videos available from September 6 until November 30, 2021)

**Please see here for the [ESSCIRC Guide](https://drive.google.com/file/d/10dghzuv4aQBty4QfTIY9Q8hXyqsue8Le/view?usp=sharing), [ESSDERC Guide](https://drive.google.com/file/d/10dx9y2tm9cHPDa4upNUYjfXCf44aPry5/view?usp=sharing), and the [Program at a Glance](https://drive.google.com/file/d/10eU0OLTXEpiNzI5hkqv4mSr43T8cedPP/view?usp=sharing)**

To access ESSCIRC ESSDERC 2021, you must pre-register for the conference.  Instructions on how to access the event will be provided directly to attendees and included on the ESSCIRC ESSDERC conference website. [Register now](https://www.esscirc-essderc2021.org/).

Please register for the ESSCIRC conference to join the event.

ESSCIRC ESSDERC 2021

The aim of ESSCIRC and ESSDERC is to provide an annual European forum for the presentation and discussion of recent advances in solid-state devices and circuits.
The level of integration for system-on-chip design is rapidly increasing.

This is made available by advances in semiconductor technology. Therefore, more than ever before, a deeper interaction among technologists, device experts, IC designers, and system designers is necessary. ESSCIRC and ESSDERC are governed by a common Steering Committee and share Plenary Keynote Presentations and Joint Sessions bridging both device and circuit communities.





workshop 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.

The presentation provides a novel method for reducing a multi-class Confusion Matrix into a 2 × 2 version enabling the use of the relevant performance metrics and methods like the Receiver Operating Characteristic and the Area Under the Curve for the assessment of different classification algorithms. The reduction method is based on class grouping and leads to a specific Confusion Matrix format. The developed method is then exploited for the assessment of several state-of-the-art machine learning algorithms applied on a customer experience metric called Net Promoter Score (NPS).

Multi-Class Confusion Matrix Reduction method and its application on Net Promoter Score classification problem

Man overboard incidents in a maritime vessel are serious accidents where, the efficient and rapid detection is crucial in the recovery of the victim. The severity of such accidents, urge the use of intelligent systems that are able to automatically detect a fall and provide relevant alerts. To this end the use of novel deep learning and computer vision algorithms have been tested and proved efficient in problems with similar structure. This paper presents the use of a deep learning framework for automatic detection of man overboard incidents. We investigate the use of simple RGB video streams for extracting specific properties of the scene, such as movement and saliency, and use convolutional spatiotemporal autoencoders to model the normal conditions and identify anomalies. Moreover, in this work we present a dataset that was created to train and test the efficacy of our approach.

Man Overboard: Fall detection using spatiotemporal convolutional autoencoders in maritime environments

Bioinformatics systems for monitoring and mitigating epidemics: The STAMINA Paradigm

Emotion recognition is a field of psychology that involves the process of identifying emotions and treating mental conditions like autism. The advancements in the field of machine learning and deep learning have paved the way for scientists to develop models for evaluating emotions by analyzing facial expressions, speech and text. However, the task of evaluating emotions could be best done by processing the bio-signals and neural imaging of the brain. In that sense, bio-signals such as Electroencephalogram (EEG) are less expensive to use and non-invasive, giving them an edge over traditional methods like Magnetic Resonant Imaging (MRI). However,
not many datasets are publicly available due to privacy issues and their availability is highly limited by the classification task. These constraints, along with the problem of data scarcity, motivates this work as an attempt to enhance the accuracy scores by generating synthetic features that are close to actual data distribution. In this research, we propose a Wasserstein Generative Adversarial Network with gradient penalty (WGAN-GP) based model that can help tackle this problem. The dataset that is investigated is DEAP, one of the benchmark datasets for evaluating emotion recognition algorithms. In the method proposed, nine descriptive features are extracted from the original data and baseline models are evaluated. 
Subsequently, a WGAN-GP is trained on these extracted features and is used to generate a new set of synthetic data features. The synthetic features are then analysed for quality and appended to the original data to expand this dataset. Experiments with different augmentation factors (x2, x3, x4) are investigated to evaluate the impact of the data augmentation procedure. The experimental results
demonstrate that the proposed method gives a considerable enhancement of the classification task’s performance.

GAN-Based Data Augmentation For Improving The Classification Of EEG Signals

A novel deep learning methodology for the assessment of ergonomic risks in any given work-related task with high accuracy and in real-time is presented. The proposed method takes as input a video sequence, extracts 3D skeletal features using VIBE algorithm and then processes these features to predict REBA risk scores for the entire body and individual body parts. Results on two well-known datasets and against other state-of-the-art methods verify the ability of the proposed method in predicting REBA ergonomic risk scores with high accuracy.

Towards Real-time Generalized Ergonomic Risk Assessment for the Prevention of Musculoskeletal Disorders

Physiological data are nowadays frequently used to recognize the
affective state of subjects while performing different tasks. Automatic
recognition of a stressful state as a consequence of a high level
of cognitive load is significant to prevent illnesses like depression,
anxiety and sleep disorders that are often due to excessive workload.
The spread of wearable sensors that are increasingly reliable and
comfortable makes them easy to use in day-life activities. However,
due to the nature of experiments that involve subjects, the cardinality
of the acquired data is often low, making difficult to train
deep learning methods from the scratch. In this paper we consider
the photopletismography (PPG) that measures the blood volume
registered just under the skin, which can be used to obtain the heart
rate of the subject. It is well known that PPG data are particularly
relevant to detect high level of arousal that is activated by stress.
We show that, converting monodimensional photopletismography
(PPG) data into bidimensional signals it is possible to apply a pretrained
CNN, obtaining deep features that outperform handcrafted
ones in classification tasks, especially introducing feature selections
strategies to avoid curse of dimensionality.

A Deep Learning Approach to Recognize Cognitive Load using PPG Signalshttps://dl.acm.org/doi/10.1145/3453892.3461625

As access to new technologies becomes increasingly less expensive and complex, technologies such as virtual reality (VR) are also becoming interesting and relevant for applications outside consumer entertainment. Especially with VR for blind and visually impaired people, VR can overcome the limitations of real-world environments (e.g., hazardous locations and reliance on sighted companionship for training) by virtually exploring its digital model using consumer hardware. In this context, we conducted two within-subject user study with 11 and 5 participants that were asked to learn and re-walk an audio-landmark enriched path in a virtual environment. Our results showed, that the ability to learn and re-walk an audio landmark-enriched environment using an auditive only locomotion interface is possible, but yet difficult. To be able to re-walk even a simple corridor entails a relatively big training effort, but distinctive structures are recognizable again. The results also indicated that the intuitive use of controller based locomotion brings advantages over a keyboard and mouse based approach. Furthermore, we discovered different characteristics of auditive navigation (e.g., walking zigzag, in circles or collision-oriented with regard to the rooms' walls), encountered acoustic rendering problems in acute corners and were able to show a training effect when having trained a path within a virtual environment with respect to re-walking, both inside a virtual environment and outside to a physically re-walked path. This work underlines the feasibility, problems, but also chances of different locomotion implementations in VR and is intended to foster future work towards both technical and methodological optimization in this area.

Preliminary Analysis on Interaction Characteristics with Auditive Navigation in Virtual Environments

Virtual and Augmented Reality technologies are nowadays increasingly adopted as core elements of modern applications for creating unique user experiences. 
Following the capabilities of hardware and its constantly reducing cost, their use is moving from scientific and research projects to everyday life applications such as social media, architectural design and of course games.
In this work, we present a state-of-the-art exergame implementation paradigm, which can facilitate the development of exergames exploiting advanced user interactivity, augmented reality and modern gamification techniques.
The platform has been used for the development of a set of prototype games for Android phones based on the popular AR Core framework. 
The overall approach has been evaluated in real-world scenarios with exceptional usability and performance results.

On the development of augmented reality based exergames for assessing human activity and cognition on mobile devices

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.

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Next from PETRA 2021

Multi-Class Confusion Matrix Reduction method and its application on Net Promoter Score classification problem

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7.5nJ/Inference CMOS Echo State Network for Coronary Heart Disease Prediction

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