System Identification to a hybrid automaton is a complex and flourishing research field involving expertise in both continuous dynamics and discrete event system modeling. Current methodologies for hybrid automaton Identification predominantly rely on a framework centered around the sequential resolution of local signal processing optimization problems. This paper seeks to enhance the existing framework by introducing a novel step designed to address certain limitations inherent in the non-global optimization aspect of a sequential resolution framework. The proposed approach enables each Identification step not only to address its local optimization challenges but also to contribute to the optimization of a global cost function including model distance, sequential fidelity, and automaton structure.

The COVID-19 pandemic has placed unprecedented pressure on hospitals and requires efficient patient management to optimize bed utilization. This paper introduces a formal modeling tool designed to streamline and update clinical pathways, facilitating evaluation of system performance and resource optimization. Emphasizing user-friendliness for medical personnel, we employ Probabilistic Time Petri Nets to represent clinical pathways. These models are constructed using a novel pattern discovery algorithm that efficiently extracts pertinent data from patient treatment databases. This approach significantly improves the management of hospital workflow, particularly critical during health crises.

In the context of CPS modeling, this paper proposes a new method for online adaptive identification of hybrid systems. The method relies on a Moore machine identification process; A mapping between a hybrid automaton and a discrete event Moore machine is proposed, and a new online adaptive Moore machine identification method is proposed. A case study illustrates the proposed method.

Cyber-Physical Systems (CPS) brought connectivity to factories, and with connectivity comes a risk of cyber attack. CPSs are vulnerable to malicious attacks in which an attacker is inserted between a process and its control unit. Some papers have proposed to model attacked CPSs with discrete event systems (DES), have successfully modeled attacked systems and characterised different types of attacks. Nevertheless, these papers tend to treat attacks after they have become problematic. The objective of this paper is to extend previous works on cyber attack in DES to build models for diagnosis of attacks. We will design a model using Labeled Petri Nets and construct a reachability graph of an attacked net in order to enrich a diagnosis model which will allow the detection of attacks before they completely destabilise the system. This construction is illustrated by an industrial example.

Monitoring Activities of Daily Living (ADL) has become a major occupation to respond to the aging population and prevent frailty. To do this, the scientific community is using Machine Learning (ML) techniques to learn the lifestyle habits of people at home. The most-used formalism to represent the behaviour of the inhabitant is the Hidden Markov Model (HMM) or Probabilistic Finite Automata (PFA), where events streams are considered. A common decomposition to design ADL using a mathematical model is Activities–Actions–Events (AAE). In this paper, we propose mathematical criteria to evaluate a priori the performance of these instrumentations for the goals of ADL recognition. We also present a case study to illustrate the use of these criteria.

Due to population ageing, the number of people requiring monitoring and specific health care increased. Nevertheless, due to social or financial reasons, most of this population prefers to stay at home. Thanks to recent improvement in Ambient Assisted Living (AAL) and smart home technologies, new opportunities emerge to develop Health at Home (HaH) solutions. One of the main objectives of HaH is to offer to smart home inhabitants a life and health quality similar to patients in specialized medical institutions. To achieve this task, it is proposed in this paper an approach to monitor frail people at home to detect behavioral changes which can be symptoms of health problems. The method is model-based, working with Stochastic Timed Automata as it is an efficient tool to model activity duration and ordering habits, which are key features of human behavior. Once a model depicting life habits is built, it is proposed to use normalized likelihood and probabilistic distribution to evaluate the consistency between the regular life habits and newly observed behavior. Appearance of a inconsistent behavior may testify the evolution or apparition of a medical symptom. A case study demonstrates the relevancy of the proposed approach, and several scenarios highlight the possibility to have clear enough information on inhabitant behavior to identify a limited number of responsible medical disorder.

World population ageing causes an important increase of people needing specific health care and monitoring. Dedicated institutions exist, but most of the elderly prefer to keep their autonomy for economic and personal reasons. To ensure a good quality of life and health to this population, Health at Home (HaH) solutions are explored. Many works focus on monitoring smart home inhabitant behavior to detect changes which might be due to health problems. These approaches are efficient to detect accident or short-term diseases such as a cold or influenza but tend to detect too tardily diseases which provoke slow declines in behavior. This is a problem as the elderly are likely to suffer from such troubles and early detection allows for better diagnosis and may help to prevent or reduce future worsening. In this paper, a novel approach for the detection of long-term behavior changes is introduced. It focuses on activity duration as this indicator is influenced by most diseases and give clear information about the inhabitant health status. This paper proposes data forecasting to detect future anomalies to assess existence of evolution in the current behavior. Information is sent to medical staff to refine their prognostic and adapt their treatment or call for a medical appointment. A case study based on a real smart home simulating a worst-case scenario attests for the efficiency of the approach and its resilience.

Recent improvements in connected tools and learning algorithms allow new opportunities in the field of Ambient Assisted Living (AAL). However, smart home inhabitant's life habits are often required to obtain adequate results for energy management, security, Health at Home (HaH), and numerous other applications. In this paper, a model for life routines representation and algorithms for its generation is introduced. Study on the state of the art exposes that activity ordering and duration are key features of human behavior. Consequently, the presented approach focuses on a higher level of semantic by observing activities performed by the inhabitant rather than the sensor logs, which allow for better understanding of his comportment and universality of the model for multiple aims. Stochastic Time Automata (STA) is proposed as it adequately models activity ordering with probability associated to edges and activity duration through probability distribution associated to location delay. Presented approach does not require specific equipment besides sensors required for activity recognition and is versatile enough to be used in various applications. A case study highlights the relevancy of the chosen features and demonstrates that the proposed model is efficient to depict and understand inhabitants' life habits.

Ambient assisted living and smart home technologies are a good way to take care of dependent people whose number will increase in the future. They allow the discovery and the recognition of human's activities of daily living (ADLs) in order to take care of people by keeping them in their home. In order to consider the human behavior nondeterminism, probabilistic approaches are used despite difficulties encountered in model generation and probabilistic indicators computing. In this article, a global method based on probabilistic finite-state automata and the definition of the normalized likelihood and perplexity is proposed to manage ADLs discovery and recognition. In order to reduce the computational complexity, some results about a simplified normalized likelihood computation are proved. A real case study showing the efficiency of the proposed method is discussed. Note to Practitioners-This article is motivated by the problem of the automatic recognition of activities that are daily performed by elderly or disabled people in a smart dwelling. The set of activities to be recognized is defined by a medical staff (e.g., to prepare meal, to do housework, to take leisure, etc.) and correspond to pathologies that have to be monitored by doctors (e.g., loss of memory, loss of mobility, etc.). The proposed method is based on a systematic procedure of offline construction of a model for each activity to be monitored (the activity discovering step). The online recognition of activities actually performed (the activity recognition step) is afterward based on these models of activities. Since the human behavior is nondeterministic, and may even be irrational, probabilistic activity models are built from a learning database. In the same way, probabilistic indicators are used for determining online the most probable activities actually performed. The efficiency of the proposed approach is illustrated through a case study performed in a smart living lab.

This article deals with the problem of discovering a Petri net (PN) model of a discrete-event system, starting from the observation of long-event sequences. Precisely, given an interpreted PN (IPN) system modeling the relations between input and output events of the system (i.e., the reactive/observable behavior), the internal state evolutions of the system (i.e., the unobservable behavior) are first discovered and then modeled. The proposed unobservable discovery takes advantage of the novel concept of interpreted sequences, which better characterize the system and model the behavior by considering both observable markings (outputs) and transition firings (inputs). The unobservable modeling is approached as a net synthesis problem. It relies on an optimization-based procedure that identifies the complementary structure; in particular, places only are added to the original model. Note to Practitioners-Black-box identification procedures process an input-output sequence recorded for a long period of time during the functioning of a closed-loop controlled system, and then return a model of the system. However, even if these models simulate well the recorded sequence, they are not very accurate. Indeed, they simulate also other sequences that, in general, are not admitted by the real system. The method proposed here aims to make more accurate these models by discovering the unobservable behavior of a controlled system, related to evolutions of the internal state (and variables) of the system without changing the capability of simulating the observed behavior.

There has been growing interest in healthcare delivery systems worldwide coupled with a recent influx of funding into the area. Due to rapid development in information and network technology, smartness and interconnectivity have become a central issue in healthcare delivery. Automation is important for healthcare delivery systems engineering. In recent years, the significant changes in healthcare delivery and the rapid development in data analytics, artificial intelligence, robotics, and wearable devices have generated numerous opportunities for innovation in automation for smart and interconnected healthcare delivery systems. In addition, many new challenges have emerged in order to apply and implement these innovations. Such opportunities and challenges have significantly expanded the scopes of traditional automation science and engineering. Therefore, to show the state-of-the-art research and applications in the general area of healthcare delivery systems automation and to address the needs and challenges for the integration of new automation technologies in healthcare delivery, this Special Issue serves as a forum to bring together researchers, clinicians, and healthcare practitioners to present efficient scientific and engineering solutions and to provide visions for future research and development.

This paper focuses on the problem of discovering a Petri Net model from long event sequences generated by a discrete event system. Precisely, it is assumed that the relations between input and output events (i.e. the observable behaviour of the system) are already modelled by a set of Interpreted Petri Net fragments while the behaviour of the internal state evolutions (i.e. the unobservable behaviour) must be discovered. An approach inspired to net synthesis is proposed. It relies on an optimization-based procedure for the identification of the unobservable net structure and marking.

Numerous theoretical results have been obtained in the field of conformance testing, a very promising formal technique to improve dependability of critical systems. Nevertheless, developing on this basis programmable logic controller (PLC) test techniques that produce correct conformance verdicts requires to take into account the real technological features of PLC. This paper proposes conformance relations that meet this objective. Examples illustrate the benefits of the contribution.

This correspondence paper deals with the sensor placement optimization problem in the context of indoor multiple inhabitants location tracking to solve ambient assisted living problems. Binary sensors, like passive infrared (PIR) sensors, are used to guaranty specific coverage requirements and allow privacy respecting. Moreover, within real home environments, different kinds of obstacles (like walls, high furniture, etc.) can affect the detection capacity of PIR sensors. This paper proposes an integrated framework devoted to optimize the placement of sensors and PIR sensors in smart homes by taking into account physical topologies and coverage precision constraints. An integer linear programming problem is formalized and a case study illustrates the applicability of the proposed approach and the scalability of the optimization method.

This paper deals with the smart placement of motion sensors in smart homes for Ambient Assisted Living, by considering the sensor technology and cost and respecting specific coverage requirements. The core of the proposed methodology is a decision module that can optimize the sensors placement according to different objectives. More precisely, the main objective is the minimization of costs of the deployed sensors. Moreover, the second objective can be the maximization of the overlapping in order to find a robust solution or the minimization of the overlapping of the detection areas in order to improve the inhabitant localization. A case study demonstrates the effectiveness of the proposed strategy on sensors placement in a domestic environment.

The aim of behavioral identification of discreteevent systems is to build, from a sequence of observed inputs/outputs events, an understandable model that exhibits both the direct relations between inputs and outputs events (i.e., the observable behavior of the system) and the internal state evolutions (i.e., the unobservable behavior). Since parallelism hinders the construction of monolithic models, distributed identification builds instead the models of subsystems. This paper proposes an automated partitioning of the system and optimal regarding the readability of the identified distributed models, thus fitting reverse-engineering purposes. To solve the optimization problem, a first solution is extracted from the observable behavior; then additional solutions are computed by agglomerative clustering. The approach is applied to a benchmark, resulting in an adequate functional partition.

Smart Home technologies may improve the comfort and the safety of frail people into their home. To achieve this goal, models of Activities of Daily Living (ADL) are often used to detect dangerous situations or behavioral changes in the habits of these persons. In this paper, an approach is proposed to build a model of ADLs, under the form of Hidden Markov Models (HMMs), from a training database of observed events emitted by binary sensors. The main advantage of our approach is that no knowledge of actions really performed during the learning period is required. Finally, we apply our approach to a real case study and we discuss the quality of the results obtained.

The aim of the paper is to provide an optimal placement of sensors for inhabitant location tracking in smart homes, by using only motion detectors. In particular, motion detectors are binary sensors largely used in ambient assisted living applications because they are low cost, non-intrusive and privacy sensors. An approach to optimize the placement of motion detectors in a real home environment by adopting a two-dimensional grid is presented. In this context, the real coverage area of a sensor is computed by considering the obstacles and respecting the specified coverage performance requirements. The optimization problem is formalized and solved as an Integer Linear Programming problem and a case study is presented to show the efficacy of the proposed approach.

The aim of this paper is to improve the autonomy of medically monitored patients in a smart home instrumented only with binary sensors; overwatching the disease evolution, that can be characterized by behavior changes, is helped by detecting the activities the inhabitant performs. Two contributions are presented. On one hand, using sequence mining methods in the flow of sensor events, the most frequent patterns mirroring activities of the inhabitant are discovered; these activities are then modeled by an extended finite automaton, which can then be used for activity recognition and generate activity events. On the other hand, given the set of activities that can be recognized, another automaton is built to model requirements from the medical staff supervising the inhabitant; it accepts activity events, and residuals are defined to detect any behavior deviation. The whole method is applied to the dataset of Domus, an instrumented smart home.