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Pain assessment has remained largely unchanged for decades and is currently based on self-reporting. Although there are different versions, these self-reports all have significant drawbacks. For example, they are based solely on the individual’s assessment and are therefore influenced by personal experience and highly subjective, leading to uncertainty in ratings and difficulty in comparability. Thus, medicine could benefit from an automated, continuous and objective measure of pain. One solution is to use automated pain recognition in the form of machine learning. The aim is to train learning algorithms on sensory data so that they can later provide a pain rating. This thesis summarises several approaches to improve the current state of pain recognition systems based on physiological sensor data. First, a novel pain database is introduced that evaluates the use of subjective and objective pain labels in addition to wearable sensor data for the given task. Furthermore, different feature engineering and feature learning approaches are compared using a fair framework to identify the best methods. Finally, different techniques to increase the interpretability of the models are presented. The results show that classical hand-crafted features can compete with and outperform deep neural networks. Furthermore, the underlying features are easily retrieved from electrodermal activity for automated pain recognition, where pain is often associated with an increase in skin conductance.
This Special Issue focused on novel vision-based approaches, mainly related to computer vision and machine learning, for the automatic analysis of human behaviour. We solicited submissions on the following topics: information theory-based pattern classification, biometric recognition, multimodal human analysis, low resolution human activity analysis, face analysis, abnormal behaviour analysis, unsupervised human analysis scenarios, 3D/4D human pose and shape estimation, human analysis in virtual/augmented reality, affective computing, social signal processing, personality computing, activity recognition, human tracking in the wild, and application of information-theoretic concepts for human behaviour analysis. In the end, 15 papers were accepted for this special issue. These papers, that are reviewed in this editorial, analyse human behaviour from the aforementioned perspectives, defining in most of the cases the state of the art in their corresponding field.
The discipline of design studies applies various technologies, from basic theory to application systems, while intelligence engineering encompasses computer-aided industrial design, human-factor design, and greenhouse design, and plays a major part within design science. Intelligence engineering technologies also include topics from theory and application, such as computational technologies, sensing technologies, and video detection. This book presents the proceedings of DSIE2023, the 2023 International Symposium on Design Studies and Intelligence Engineering, held on 28 & 29 October 2023 in Hangzhou, China. The conference provides a platform for professionals and researchers from industry and academia to present and discuss recent advances in the fields of design studies and intelligence engineering. It also fosters cooperation among the organizations and researchers involved in these overlapping fields, and invites internationally renowned professors to further explore these topics in some depth, providing the opportunity for them to discuss the technical presentations with conference participants. In all, 275 submissions were received for the conference, 105 of which were accepted after thorough review by 3 or 4 referees for presentation at the conference and inclusion here. Providing a valuable overview of the latest developments, the book will be of interest to all those working in the fields of design studies and intelligence engineering.
This 2-volume set, LNCS 14469 and 14470, constitutes the proceedings of the 26th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2023, which took place in Coimbra, Portugal, in November 2023. The 61 papers presented were carefully reviewed and selected from 106 submissions. And present research in the fields of pattern recognition, artificial intelligence, and related areas.
This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2022, held in Paris, France, in June 2022. The 15 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 33 submissions. They cover topics such as design, development, deployment, and evaluation of AI for health, smart urban environments, assistive technologies, chronic disease management, and coaching and health telematics systems.
This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits. Discusses efficient implementation of machine learning in embedded, CPS, IoT, and edge computing; Offers comprehensive coverage of hardware design, software design, and hardware/software co-design and co-optimization; Describes real applications to demonstrate how embedded, CPS, IoT, and edge applications benefit from machine learning.
Real-Time Data Acquisition in Human Physiology: Real-Time Acquisition, Processing, and Interpretation—A MATLAB-Based Approach focuses on the design and development of a computer-based system to detect and digitally process human ECG, EMG, and carotid pulse waveforms in real time. The indigenous system developed and described in this book allows for an easy-to-interface, simple hardware arrangement for bio-signal detection. The computational functionality of MATLAB is verified for viewing, digital filtration, and feature extraction of acquired bio-signals. This book demonstrates a method of providing a relatively cost-effective solution to human physiology real-time monitoring, processing, and interpretation that is more realizable and would directly benefit a larger population of patients. - Presents an application-driven, interdisciplinary, and experimental approach to bio-signal processing with a focus on acquiring, processing, and understanding human ECG, EMG, carotid pulse data and HRV. - Covers instrumentation and digital signal processing techniques useful for detecting and interpreting human physiology in real time, including experimental layout and methodology in an easy-to-understand manner. - Discusses development of a computer-based system that is capable of direct interface through the sound port of a PC and does not require proprietary DAQ units and ADC units. - Covers a MATLAB-based algorithm for online noise reduction, features extraction techniques, and infers diagnostic features in real time. - Provides proof of concept of a PC-based twin channel acquisition system for the recognition of multiple physiological parameters. - Establishes the use of Digital Signal Controller to enhance features of acquired human physiology. - Presents the use of carotid pulse waveforms for HRV analysis in critical situations using a very simple hardware/software arrangement.
This book constitutes the refereed proceedings of the 15th International Conference on Pervasive Computing Technologies for Healthcare, Pervasive Health 2021, held in December 2021. Due to COVID-19 pandemic the conference was held virtually. The 28 full and 7 short papers were selected from 74 submissions and are organized in 3 main tracks: hospitality and community care, homecare and medical education. The COVID 19 pandemic was challenging all dimensions of Pervasive Health (PH) and traditional ways of monitoring, diagnosing, treating and communicating changed dramatically.