Technologies | 2022 Issue Cover Collection

1. “Self-Organizing and Self-Explaining Pervasive Environments by Connecting Smart Objects and Applications”
by Börge Kordts, Bennet Gerlach and Andreas Schrader
Technologies 2022, 10, 15; https://doi.org/10.3390/technologies10010015
Available online: https://www.mdpi.com/2227-7080/10/1/15

Highlights:
(1) We have presented a framework to achieve self-explainability in pervasive environments, including smart objects as well as ambient applications (applications that are used within smart environments). The framework is based on an extension of the Smart Object Description Language (SODL) to support ambient applications. By using this system, smart objects and applications can be connected dynamically to control each other.
(2) We have also addressed the ensembling problem and presented an algorithm capable of finding suitable connections between smart objects and ambient applications, enabling our framework to automatically form ensembles.
(3) The framework can be used to achieve self-explaining and self-organizing smart interaction spaces consisting of smart objects as well as an ambient application for various scenarios with minimal effort.

2. “Detection of Physical Strain and Fatigue in Industrial Environments Using Visual and Non-Visual Low-Cost Sensors”
by Konstantinos Papoutsakis, George Papadopoulos, Michail Maniadakis, Thodoris Papadopoulos, Manolis Lourakis, Maria Pateraki and Iraklis Varlamis
Technologies 2022, 10(2), 42; https://doi.org/10.3390/technologies10020042
Available online: https://www.mdpi.com/2227-7080/10/2/42

Highlights:
(1) An unobtrusive and low-cost solution for the detection of physical strain and fatigue during work activities, which is based on the fusion of vision-based extracted information (working postures) and non-visual (heart rate) input, regardless of the activity performed.
(2) A vision-based approach for the classification of ergonomically sub-optimal working postures that cause increased physical strain based on the combination of Graph-based Convolutional Networks and the soft-DTW method for pairwise temporal alignment of 3D skeletal data sequences, which achieves real-time/online runtime performance using continuous streams of data acquired via a single camera.
(3) A predictive model for the early detection of high heart rate incidents, which exploits vision-based extracted information related to the workers’ physical strain to improve heart rate prediction accuracy.
(4) A new multi-modal dataset that comprises synchronized visual information of color and depth image sequences and worker heart rate (HR) data acquired using smartwatches during car assembly activities in an actual manufacturing environment.

3. “Application of 3D Virtual Prototyping Technology to the Integration of Wearable Antennas into Fashion Garments”
by Evridiki Papachristou and Hristos T. Anastassiu
Technologies 2022, 10(3), 62; https://doi.org/10.3390/technologies10030062
Available online: https://www.mdpi.com/2227-7080/10/3/62

Highlights:
(1) Wearable antennas have been a topic of interest for more than a decade, with a very broad scope of applications, including security, health, sports, communications, fashion, etc. Three-dimensional visualization tools for clothing design can assist in addressing some of the challenges of designing wearable antennas.
(2) By using digital prototyping tools, the design of wearable antennas can be developed in a very fast and efficient manner, selecting different materials either from the software’s fabric library or importing the digital representation of a specific material. The following 3D systems are described (in alphabetical order) based on the previous research:

Accumark3D (Gerber);
CLO3D;
Modaris 3D (Lectra);
Optitex3D;
Style3D;
V-Stitcher (Browzwear);
Tukatech.

(3) It is demonstrated how textennas are possible to incorporate into the fabric of various types of garments, by utilizing these software modules, without altering the antenna parameters, and maintaining the elegance and reproducibility of the garment.

4. “Multimodal Semantic Segmentation in Autonomous Driving: A Review of Current Approaches and Future Perspectives”
by Giulia Rizzoli, Francesco Barbato and Pietro Zanuttigh
Technologies 2022, 10(4), 90; https://doi.org/10.3390/technologies10040090
Available online: https://www.mdpi.com/2227-7080/10/4/90

Highlights:
(1) Most autonomous cars exploit a variety of sensors, including color, depth or thermal cameras, LiDARs and RADARs: how to efficiently combine all these sources of information to compute an accurate semantic description of the scene is an open research field.
(2) The paper introduces the acquisition setups and multimodal datasets commonly used within the context of autonomous driving.
(3) It reviews several different deep learning architectures for multimodal semantic segmentation combining different data sources at different stages.

5. “Exploration of Educational Possibilities by Four Metaverse Types in Physical Education“
by Ji-Eun Yu
Technologies 2022, 10(5), 104; https://doi.org/10.3390/technologies10050104
Available online: https://www.mdpi.com/2227-7080/10/5/104

Highlights:
(1) The metaverse environment is still in a rudimentary stage, and its use related to physical education subjects is only at the game level.
(2) Physical education in universities, incorporating metaverse technology, will be possible only when more specialized technology is incorporated into various sports.
(3) This paper will help expand the scope and depth of follow-up research, offering basic data showing the directions of development in metaverse-based physical education.

6. “Modelling the Trust Value for Human Agents Based on Real-Time Human States in Human-Autonomous Teaming Systems”
by Chin-Teng Lin, Hsiu-Yu Fan, Yu-Cheng Chang, Liang Ou, Jia Liu, Yu-Kai Wang and Tzyy-Ping Jung
Technologies 2022, 10(6), 115; https://doi.org/10.3390/technologies10060115
Available online: https://www.mdpi.com/2227-7080/10/6/115

Highlights:
(1) Trust in human society is an essential factor in sustaining cooperation. Inspired by this concept, we have introduced trust modeling for human agents in human-autonomous teaming (HAT) systems to build cooperation between humans and autonomous agents.
(2) Human trust is influenced by changing cognitive states, posing challenges in accurately calibrating trust values. The paper proposes a trust model that estimates the trustworthiness of human agents in HAT systems based on real-time human states such as attention, stress and perception.
(3) The proposed human trust model employs adaptive fusion using fuzzy reinforcement learning, combining data from sources like eye trackers, heart rate monitors and human awareness.
(4) The paper presents the experimental results of robot simulations that demonstrate the effectiveness of the trust model. The simulations show that the trust model can generate reliable human trust values based on real-time cognitive states, leading to improved efficiency in the HAT system.
(5) The proposed human trust model unlocks the potential for advancing HAT systems. This potential enhancement not only underscores the model's current significance in human agent collaborations but also establishes a cornerstone for the development of increasingly sophisticated and responsive HAT systems in the future.