Editorial

5 pages, 183 KiB

Open AccessEditorial

Artificial Intelligence and Ambient Intelligence

by Matjaz Gams and Martin Gjoreski

Electronics 2021, 10(8), 941; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10080941 - 15 Apr 2021

Cited by 4 | Viewed by 1584

Artificial intelligence (AI) and its sister ambient intelligence (AmI) have in recent years become one of the main contributors to the progress of digital society and human civilization [...] Full article

(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence)

Research

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19 pages, 1278 KiB

Open AccessFeature PaperArticle

Ultra-Short Window Length and Feature Importance Analysis for Cognitive Load Detection from Wearable Sensors

by Jaakko Tervonen, Kati Pettersson and Jani Mäntyjärvi

Electronics 2021, 10(5), 613; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10050613 - 06 Mar 2021

Cited by 15 | Viewed by 2665

Abstract

Human cognitive capabilities are under constant pressure in the modern information society. Cognitive load detection would be beneficial in several applications of human–computer interaction, including attention management and user interface adaptation. However, current research into accurate and real-time biosignal-based cognitive load detection lacks [...] Read more.

Human cognitive capabilities are under constant pressure in the modern information society. Cognitive load detection would be beneficial in several applications of human–computer interaction, including attention management and user interface adaptation. However, current research into accurate and real-time biosignal-based cognitive load detection lacks understanding of the optimal and minimal window length in data segmentation which would allow for more timely, continuous state detection. This study presents a comparative analysis of ultra-short (30 s or less) window lengths in cognitive load detection with a wearable device. Heart rate, heart rate variability, galvanic skin response, and skin temperature features are extracted at six different window lengths and used to train an Extreme Gradient Boosting classifier to detect between cognitive load and rest. A 25 s window showed the highest accury (67.6%), which is similar to earlier studies using the same dataset. Overall, model accuracy tended to decrease as the window length decreased, and lowest performance (60.0%) was observed with a 5 s window. The contribution of different physiological features to the classification performance and the most useful features that react in short windows are also discussed. The analysis provides a promising basis for future real-time applications with wearable sensors. Full article

(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence)

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21 pages, 1764 KiB

Open AccessArticle

A One-Dimensional Non-Intrusive and Privacy-Preserving Identification System for Households

by Tomaž Kompara, Janez Perš, David Susič and Matjaž Gams

Electronics 2021, 10(5), 559; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10050559 - 27 Feb 2021

Cited by 2 | Viewed by 1691

Abstract

In many ambient-intelligence applications, including intelligent homes and cities, awareness of an inhabitant’s presence and identity is of great importance. Such an identification system should be non-intrusive and therefore seamless for the user, especially if our goal is ubiquitous and pervasive surveillance. However, [...] Read more.

In many ambient-intelligence applications, including intelligent homes and cities, awareness of an inhabitant’s presence and identity is of great importance. Such an identification system should be non-intrusive and therefore seamless for the user, especially if our goal is ubiquitous and pervasive surveillance. However, due to privacy concerns and regulatory restrictions, such a system should also strive to preserve the user’s privacy as much as possible. In this paper, a novel identification system is presented based on a network of laser sensors, each attached on top of the room entry. Its sensor modality, a one-dimensional depth sensor, was chosen with privacy in mind. Each sensor is mounted on the top of a doorway, facing towards the entrance, at an angle. This position allows acquiring the user’s body shape while the user is crossing the doorway, and the classification is performed by classical machine learning methods. The system is non-intrusive, non-intrusive and preserves privacy—it omits specific user-sensitive information such as activity, facial expression or clothing. No video or audio data are required. The feasibility of such a system was tested on a nearly 4000-person, publicly available database of anthropometric measurements to analyze the relationships among accuracy, measured data and number of residents, while the evaluation of the system was conducted in a real-world scenario on 18 subjects. The evaluation was performed on a closed dataset with a 10-fold cross validation and showed 98.4% accuracy for all subjects. The accuracy for groups of five subjects averaged 99.1%. These results indicate that a network of one-dimensional depth sensors is suitable for the identification task with purposes such as surveillance and intelligent ambience. Full article

(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence)

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25 pages, 1087 KiB

Open AccessArticle

Device-Free Crowd Counting Using Multi-Link Wi-Fi CSI Descriptors in Doppler Spectrum

by Ramon F. Brena, Edgar Escudero, Cesar Vargas-Rosales, Carlos E. Galvan-Tejada and David Munoz

Electronics 2021, 10(3), 315; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10030315 - 29 Jan 2021

Cited by 11 | Viewed by 3135

Abstract

Measuring the quantity of people in a given space has many applications, ranging from marketing to safety. A family of novel approaches to measuring crowd size relies on inexpensive Wi-Fi equipment, taking advantage of the fact that Wi-Fi signals get distorted by people’s [...] Read more.

Measuring the quantity of people in a given space has many applications, ranging from marketing to safety. A family of novel approaches to measuring crowd size relies on inexpensive Wi-Fi equipment, taking advantage of the fact that Wi-Fi signals get distorted by people’s presence, so by identifying these distortion patterns, we can estimate the number of people in such a given space. In this work, we refine methods that leverage Channel State Information (CSI), which is used to train a classifier that estimates the number of people placed between a Wi-Fi transmitter and a receiver, and we show that the available multi-link information allows us to achieve substantially better results than state-of-the-art single link or averaging approaches, that is, those that take the average of the information of all channels instead of taking them individually. We show experimentally how the addition of each of the multiple links information helps to improve the accuracy of the prediction from 44% with one link to 99% with 6 links. Full article

(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence)

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19 pages, 2803 KiB

Open AccessArticle

Constructing Emotional Machines: A Case of a Smartphone-Based Emotion System

by Hao-Chiang Koong Lin, Yu-Chun Ma and Min Lee

Electronics 2021, 10(3), 306; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10030306 - 27 Jan 2021

Cited by 3 | Viewed by 2055

Abstract

In this study, an emotion system was developed and installed on smartphones to enable them to exhibit emotions. The objective of this study was to explore factors that developers should focus on when developing emotional machines. This study also examined user attitudes and [...] Read more.

In this study, an emotion system was developed and installed on smartphones to enable them to exhibit emotions. The objective of this study was to explore factors that developers should focus on when developing emotional machines. This study also examined user attitudes and emotions toward emotional messages sent by machines and the effects of emotion systems on user behavior. According to the results of this study, the degree of attention paid to emotional messages determines the quality of the emotion system, and an emotion system triggers certain behaviors in users. This study recruited 124 individuals with more than one year of smartphone use experience. The experiment lasted for two weeks, during which time participants were allowed to operate the system freely and interact with the system agent. The majority of the participants took interest in emotional messages, were influenced by emotional messages and were convinced that the developed system enabled their smartphone to exhibit emotions. The smartphones generated 11,264 crucial notifications in total, among which 76% were viewed by the participants and 68.1% enabled the participants to resolve unfavorable smartphone conditions in a timely manner and allowed the system agent to provide users with positive emotional feedback. Full article

(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence)

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15 pages, 3615 KiB

Open AccessArticle

Gaining a Sense of Touch Object Stiffness Estimation Using a Soft Gripper and Neural Networks

by Michal Bednarek, Piotr Kicki, Jakub Bednarek and Krzysztof Walas

Electronics 2021, 10(1), 96; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10010096 - 05 Jan 2021

Cited by 11 | Viewed by 3714

Abstract

Soft grippers are gaining significant attention in the manipulation of elastic objects, where it is required to handle soft and unstructured objects, which are vulnerable to deformations. The crucial problem is to estimate the physical parameters of a squeezed object to adjust the [...] Read more.

Soft grippers are gaining significant attention in the manipulation of elastic objects, where it is required to handle soft and unstructured objects, which are vulnerable to deformations. The crucial problem is to estimate the physical parameters of a squeezed object to adjust the manipulation procedure, which poses a significant challenge. The research on physical parameters estimation using deep learning algorithms on measurements from direct interaction with objects using robotic grippers is scarce. In our work, we proposed a trainable system which performs the regression of an object stiffness coefficient from the signals registered during the interaction of the gripper with the object. First, using the physics simulation environment, we performed extensive experiments to validate our approach. Afterwards, we prepared a system that works in a real-world scenario with real data. Our learned system can reliably estimate the stiffness of an object, using the Yale OpenHand soft gripper, based on readings from Inertial Measurement Units (IMUs) attached to the fingers of the gripper. Additionally, during the experiments, we prepared three datasets of IMU readings gathered while squeezing the objects—two created in the simulation environment and one composed of real data. The dataset is the contribution to the community providing the way for developing and validating new approaches in the growing field of soft manipulation. Full article

(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence)

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17 pages, 1224 KiB

Open AccessEditor’s ChoiceArticle

On Robustness of Multi-Modal Fusion—Robotics Perspective

by Michal Bednarek, Piotr Kicki and Krzysztof Walas

Electronics 2020, 9(7), 1152; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9071152 - 16 Jul 2020

Cited by 13 | Viewed by 3752

Abstract

The efficient multi-modal fusion of data streams from different sensors is a crucial ability that a robotic perception system should exhibit to ensure robustness against disturbances. However, as the volume and dimensionality of sensory-feedback increase it might be difficult to manually design a [...] Read more.

The efficient multi-modal fusion of data streams from different sensors is a crucial ability that a robotic perception system should exhibit to ensure robustness against disturbances. However, as the volume and dimensionality of sensory-feedback increase it might be difficult to manually design a multimodal-data fusion system that can handle heterogeneous data. Nowadays, multi-modal machine learning is an emerging field with research focused mainly on analyzing vision and audio information. Although, from the robotics perspective, haptic sensations experienced from interaction with an environment are essential to successfully execute useful tasks. In our work, we compared four learning-based multi-modal fusion methods on three publicly available datasets containing haptic signals, images, and robots’ poses. During tests, we considered three tasks involving such data, namely grasp outcome classification, texture recognition, and—most challenging—multi-label classification of haptic adjectives based on haptic and visual data. Conducted experiments were focused not only on the verification of the performance of each method but mainly on their robustness against data degradation. We focused on this aspect of multi-modal fusion, as it was rarely considered in the research papers, and such degradation of sensory feedback might occur during robot interaction with its environment. Additionally, we verified the usefulness of data augmentation to increase the robustness of the aforementioned data fusion methods. Full article

(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence)

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26 pages, 11606 KiB

Open AccessArticle

PUT-Hand—Hybrid Industrial and Biomimetic Gripper for Elastic Object Manipulation

by Tomasz Mańkowski, Jakub Tomczyński, Krzysztof Walas and Dominik Belter

Electronics 2020, 9(7), 1147; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9071147 - 16 Jul 2020

Cited by 15 | Viewed by 5434

Abstract

In this article, the design of a five-fingered anthropomorphic gripper is presented specifically designed for the manipulation of elastic objects. The manipulator features a hybrid design, being equipped with three fully actuated fingers for precise manipulation, and two underactuated, tendon-driven digits for secure [...] Read more.

In this article, the design of a five-fingered anthropomorphic gripper is presented specifically designed for the manipulation of elastic objects. The manipulator features a hybrid design, being equipped with three fully actuated fingers for precise manipulation, and two underactuated, tendon-driven digits for secure power grasping. For ease of reproducibility, the design uses as many off-the-shelf and 3D-printed components as possible. The on-board controller circuit and firmware are also presented. The design includes resistive position and angle sensors in each joint, resulting in full joint observability. The controller has a position-based controller integrated, along with USB communication protocol, enabling gripper state reporting and direct motor control from a PC. A high-level driver operating as a Robot Operating System node is also provided. All drives and circuitry of the PUT-Hand are integrated within the hand itself. The sensory system of the hand includes tri-axial optical force sensors placed on fully actuated fingers’ fingertips for reaction force measurement. A set of experiments is provided to present the motion and perception capabilities of the gripper. All design files and source codes are available online under CC BY-NC 4.0 and MIT licenses. Full article

(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence)

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Review

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16 pages, 2662 KiB

Open AccessEditor’s ChoiceReview

Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules

by Matjaž Gams and Tine Kolenik

Electronics 2021, 10(4), 514; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10040514 - 22 Feb 2021

Cited by 14 | Viewed by 4764

Abstract

This paper presents relations between information society (IS), electronics and artificial intelligence (AI) mainly through twenty-four IS laws. The laws not only make up a novel collection, currently non-existing in the literature, but they also highlight the core boosting mechanism for the progress [...] Read more.

This paper presents relations between information society (IS), electronics and artificial intelligence (AI) mainly through twenty-four IS laws. The laws not only make up a novel collection, currently non-existing in the literature, but they also highlight the core boosting mechanism for the progress of what is called the information society and AI. The laws mainly describe the exponential growth in a particular field, be it the processing, storage or transmission capabilities of electronic devices. Other rules describe the relations to production prices and human interaction. Overall, the IS laws illustrate the most recent and most vibrant part of human history based on the unprecedented growth of device capabilities spurred by human innovation and ingenuity. Although there are signs of stalling, at the same time there are still many ways to prolong the fascinating progress of electronics that stimulates the field of artificial intelligence. There are constant leaps in new areas, such as the perception of real-world signals, where AI is already occasionally exceeding human capabilities and will do so even more in the future. In some areas where AI is presumed to be incapable of performing even at a modest level, such as the production of art or programming software, AI is making progress that can sometimes reflect true human skills. Maybe it is time for AI to boost the progress of electronics in return. Full article

(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence)

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