Engineering Emergence: A Survey on Control in the World of Complex Networks

Complex networks make an enticing research topic that has been increasingly attracting researchers from control systems and various other domains over the last two decades. The aim of this paper was to survey the interest in control related to complex networks research over time since 2000 and to identify recent trends that may generate new research directions. The survey was performed for Web of Science, Scopus, and IEEEXplore publications related to complex networks. Based on our findings, we raised several questions and highlighted ongoing interests in the control of complex networks.publishedVersio

Predictive Agent-Based Crowd Model Design Using Decentralized Control Systems

As a complex system, crowd dynamics emerge bottom-up from the local interactions between pedestrians as component subsystems. This article proposes a predictive agent-based crowd simulation model to analyze the outcomes of emergency evacuation scenarios taking into account collisions between pedestrians, smoke, fire sprinklers, and exit indicators. The crowd model is based on a decentralized control system structure, where each pedestrian agent is governed through a deliberative-reactive control architecture. The simulation model for evacuation includes a routing-based control system for dynamic-guided evacuation. A design case illustrates the modeling process. Results show that the crowd simulation model based on agent autonomy and local interactions is able to generate higher level crowd dynamics through emergence.publishedVersio

INTELLIGENT DECISION MAKING TOOL FOR AIDING THE SETTING OF OPTIMAL INTENSITY IN AEROBIC ENDURANCE TRAINING

Aerobic endurance training plays a significant part in the field of physical activity due to the fact that it has a medium intensity value and it depends primarily on aerobic energy generating processes. By engaging the cardiovascular and respiratory systems of the body this type of training is used to improve the overall endurance and fitness. The adaptation of these structures to the physical effort results in improving the performance output, by pushing the limits of cardiovascular and respiratory system’s functions. In order to obtain the desired results it is necessary to ensure the operating points that are often hard to reach by classical training methods. The human heart rate variability with respect to the velocity of walking or running presents inherent nonlinearities that are required to be taken into account when designing computer integrated training aids. This paper implements a training aiding tool for setting the optimal intensity of the training, using an intelligent decision making system for the human cardiovascular fitness. This software application was tested and validated by using pre-recorded data from various subjects and was compared to the classical way of analysing the heart rate variability of a subject. The designed intelligent decision making tool ensures the optimal desired intensity profiles, finding its usefulness in supporting the training configuration process by specialized professionals. Instrument decizional inteligent pentru determinarea intensității optime în antrenamentul de rezistență aerobă. Antrenamentul pentru rezistența aerobă poartă un rol important în aria activităților fizice datorită valorii medii ale intensității care activează, în principal, procesele aerobe energogene. Prin angrenarea sistemelor cardiovascular și respirator, acest tip de antrenament este utilizat pentru dezvoltarea anduranței generale. Adaptarea acestor sisteme la efortul fizic, care are ca rezultat creșterea performanței, este realizată prin continua forțare a funcțiilor sistemelor cardiovascular și respirator. Pentru a se obține rezultatele dorite este necesar să se asigure puncte operative obiective din punct de vedere fiziologic, lucru care este mai greu de realizat prin metodele de antrenament clasice. Variabilitatea frecvenței cardiace prezintă nonliniarități inerente, în ceea ce privește viteza mersului sau a alergării, care trebuie luate în considerare în conceperea instrumentelor pentru antrenamentele asistate de calculator. Această lucrare implementează un instrument decizional de asistare pentru setarea intensității optime a antrenamentului, utilizând un sistem inteligent de luare a deciziilor. Testarea și validarea acestei aplicații au fost realizate prin utilizarea unor date înregistrate anterior; totodată s-a efectuat și compararea acestui instrument cu metoda clasică de analiza a variabilității frecvenței cardiace. Instrumentul decizional inteligent asigură stabilirea profilelor optime dorite pentru intensitatea antrenamentelor, găsindu-și utilitatea în suportul acordat procesului de configurare a antrenamentului de către profesioniști. Cuvinte cheie: intensitate, rezistență aerobă, antrenament, sistem inteligen

Digital phenotyping by wearable-driven artificial intelligence in older adults and people with Parkinson's disease: Protocol of the mixed method, cyclic ActiveAgeing study

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Background: Active ageing is described as the process of optimizing health, empowerment, and security to enhance the quality of life in the rapidly growing population of older adults. Meanwhile, multimorbidity and neurological disorders, such as Parkinson’s disease (PD), lead to global public health and resource limitations. We introduce a novel user-centered paradigm of ageing based on wearable-driven artificial intelligence (AI) that may harness the autonomy and independence that accompany functional limitation or disability, and possibly elevate life expectancy in older adults and people with PD. Methods: ActiveAgeing is a 4-year, multicentre, mixed method, cyclic study that combines digital phenotyping via commercial devices (Empatica E4, Fitbit Sense, and Oura Ring) with traditional evaluation (clinical assessment scales, in-depth interviews, and clinical consultations) and includes four types of participants: (1) people with PD and (2) their informal caregiver; (3) healthy older adults from the Helgetun living environment in Norway, and (4) people on the Helgetun waiting list. For the first study, each group will be represented by N = 15 participants to test the data acquisition and to determine the sample size for the second study. To suggest lifestyle changes, modules for human expert-based advice, machine-generated advice, and self-generated advice from accessible data visualization will be designed. Quantitative analysis of physiological data will rely on digital signal processing (DSP) and AI techniques. The clinical assessment scales are the Unified Parkinson’s Disease Rating Scale (UPDRS), Montreal Cognitive Assessment (MoCA), Geriatric Depression Scale (GDS), Geriatric Anxiety Inventory (GAI), Apathy Evaluation Scale (AES), and the REM Sleep Behaviour Disorder Screening Questionnaire (RBDSQ). A qualitative inquiry will be carried out with individual and focus group interviews and analysed using a hermeneutic approach including narrative and thematic analysis techniques. Discussion: We hypothesise that digital phenotyping is feasible to explore the ageing process from clinical and lifestyle perspectives including older adults and people with PD. Data is used for clinical decision-making by symptom tracking, predicting symptom evolution, and discovering new outcome measures for clinical trials.publishedVersio