United States

Difficulties with appropriate access to public or semi-public toilets for older or disabled people lead to less participation in social activities within this group. We present a smart AI-based toilet concept that uses 3D depth data to automatically pre-configure the height and tilt of a motorized toilet seat to support standing-up and sitting down without the necessity of user interaction. This has the potential to lead to increased autonomy and quality of life.

PETRA2021

Smart toilet seat configuration for more autonomy using an AI-based 3D depth sensor

pose detection

depth data

privacy protection

smart toilet

artificial intelligence

**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.

 **RTSS 2021** has successfully concluded. Hope you have enjoyed it, and look forward to seeing you next year in person.
    Announcement for the Awards:
 * Best Paper Award goes to: Monte Carlo Response-Time Analysis, Sergey Bozhko, Georg von der Brüggen, and Björn Brandenburg
 * Best Student Paper Award goes to: Automatic Energy-Hotspot Detection and Elimination in Real-Time Deeply Embedded Systems, Mohsen Shekarisaz, Lothar Thiele, and Mehdi Kargahi.
 * Best Solution Award for Industry Session goes to: Toward Real-Time Guaranteed Scheduling for Autonomous Driving Systems. Jinghao Sun, Tianyi Wang, Kailu Duan, Bin Lu, Jiankang Ren, Zhishan Guo, and Guozhen Tan.



IEEE RTSS 2021

The IEEE Real-Time Systems Symposium (RTSS) is the premier conference in the field of real-time systems, and is a venue for researchers and practitioners to showcase innovations covering all aspects of real-time systems, including theory, design, analysis, implementation, evaluation, and experience. 

demo

In this paper we present early results of a novel approach for human activity recognition, focusing on activities of daily living. Our approach is multimodal, i.e., it combines RGB, depth and skeletal data measurements. Moreover, it is based on a novel hybrid deep neural network architecture that combines a convolutional neural network, a long short term memory network and it is evaluated using a publicly available dataset.

A Deep Hybrid Architecture for Human Activity Recognitionhttps://dl.acm.org/doi/10.1145/3453892.3461006

The simulated environment presented aims to provide a method for preparing a training set for most of this deep network adaptation. The goal is to use a simulation-generated training set for most or all of the data needed to achieve the transfer of knowledge from the original network to the platform-specific

A Simulated Environment For Traversability Estimation Experiments In Field Robotic Applications

Virtual Reality (VR) is attracting more and more attention from academic research and practical application with the current availability of low-cost and end-user friendly devices. In terms of haptic (rather than visual) interaction, however, this technology is still in its infancy and there are few devices that are inexpensive and technologically simple to operate, respectively to procure. In this context, we present a concept of how haptic interaction with VR data gloves can also succeed by means of a commercially available web cam, sophisticated tracking software, and homemade low-cost hardware. All hardware and software components are to be obtained inexpensively or are open-source in order to achieve the greatest possible dissemination potential. With this work, we intend to provide an important trigger for future improvements and dissemination in terms of both technology and areas of application.

Build-and-Touch: A Low-Cost, DIY, Open-Source Approach Towards Touchable Virtual Reality

Sign language (SL) technologies including various deaf accessibility tools, dynamic sign language synthesis systems, machine learning based signing video recognition algorithms and sign language machine translation, are only some of the technologies requiring the existence of sign language annotated resources. SL resources are equally critical for the development of various end-user tools and services, which support everyday communication needs via SL.
The poster demonstrates how annotated SL lexical and corpus resources, created and maintained at ILSP/ATHENA RC, serve the needs of both Sign Language Technologies and Greek Sign Language (GSL) signers’ accessibility.


The Landscape of Accessibility Tools Requiring SL Resources

FutureMe: Negotiating Learning Goals with your Future Learning-Self Avatar

Nurses have to carry out a myriad of tasks, which is one of the main reasons for burnout. In this paper, a robotic Multitasking Intelligent Nurse Aid (MINA) is proposed to assist nurses with everyday tasks and tackle nurse burnout. MINA uses Simultaneous Localization And Mapping (SLAM) to map shelves in a storage room and pointcloud using an RGB-Depth camera. The barcode of the item is first detected using zbar library, and then the pointcloud is generated in ROS and saved accordingly. Preliminary experiments with respect to the creation of a pointcloud map with barcode detection are presented. The preliminary results reveal that the system shows a 100% barcode detection rate, and with each barcode detection a corresponding pointcloud map is generated and saved automatically without any intervention.

MINA: A Multitasking Intelligent Nurse Aid Robot

Assessment and prevention of work-related musculo-skeletal disorders (WMSD) and fatigue are considered common and critical tasks towards occupational safety and well-being in working environments. Indicators/risk factors for WMSD and physical/mental fatigue in the manufacturing industry include labor-intensive assembly works, repetitive tasks, prolonged, sub-optimal working postures and more. We briefly present our work on automatic, non-obtrusive detection of worker physical strain using visual data acquired by cameras and on automatic detection of worker fatigue based on heart rate (HR) data acquired by smartwatches.


Detection of physical strain and fatigue in industrial environments using visual and non-visual sensors

We develop deep learning-based classifiers for Audio Event Detec-tion (AED), attacking them next with some white noise disturbances. We show that an attacker can use such simple disturbances to poten-tially fully avoid detection by AED systems. Prior work has shown that attackers can mislead image classification tasks, however this work focuses on attacks against AED systems, by tampering the audio and not image. This work brings awareness to the designers and manufacturers of AED systems and devices, as these solutions are becoming more ubiquitous by the day.

Attacking Audio Event Detection Deep Learning Classifiers with White Noise

Manifolk is a tool to visualize the output of dimensionality reduction algorithms like t-SNE, PCA etc. One of this tool's main uses is that it de-clutters graphs by plotting data points pertaining to a subset of labels. The subset of labels to be plotted can be selected using the provided checkboxes. A case study on data from a publicly available action recognition dataset like UCF101 shows how this tool can help find outliers. With the rise in self-supervised methods for training deep neural networks, this tool helps researchers better visualize the embeddings learned by the model.

Manifolk: A 3D TSNE Visualizer

"This paper showcases a developed end-user framework for a human-robot collaborative system for common tasks, such as pick and
place. The system is designed for semi-automated pick and place
tasks as well as manual operation making it flexible for multiple
use-case scenarios. The goal of the system is to make the robot
multi-functional, easy to use with a graphical user interface and
to perform common tasks with the help of a human teammate.
Integration with object recognition neural network (YoloV3) and
an RGB-Depth camera helps automate pick and place tasks with a
wide variety of objects"

Smart toilet seat configuration for more autonomy using an AI-based 3D depth sensor

Downloads

Next from PETRA2021

A Deep Hybrid Architecture for Human Activity Recognitionhttps://dl.acm.org/doi/10.1145/3453892.3461006

Similar lecture

AI Meets Real-Time: Addressing Real-World Complexities in Graph Response-Time Analysis

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES