A sensor interface is configured to receive a sensor signal. A transmitter generates a transmit signal. A receiver receives the signal corresponding to the transmit signal. Further, a monitor interface is configured to communicate a waveform to the monitor so that measurements derived by the monitor from the waveform are generally equivalent to measurements derivable from the sensor signal.

Physiological measurement communications adapter

IEEE International Conference on Robotics and Automation (ICRA) におけるフルードパワー技術の研究動向

Radar is considered an important technology for health monitoring and fall detection in elderly assisted living due to a number of attributes not shared by other sensing modalities. In this article, we describe the signal processing algorithms and techniques involved in elderly fall detection using radar. A human?s radar signal returns differ in their Doppler characteristics, depending on the nature of the human gross motor activities. These signals are nonstationary in nature, inviting time-frequency analysis in both its linear and bilinear aspects, to play a fundamental role in motion identification, including fall features determination and classification. This article employs real fall data to demonstrate the success of existing detection algorithms as well as to report on some of the challenges facing technology developments for fall detection.

Radar Signal Processing for Elderly Fall Detection: The future for in-home monitoring

In this paper we present an efficient approach for the fault detection of discrete event systems using Petri nets. We assume that some of the transitions of the net are unobservable, including all those transitions that model faulty behaviors. We prove that the set of all possible firing sequences corresponding to a given observation can be described as follows. First a set of basis markings corresponding to the observation are computed together with the minimal set of transitions firings that justify them. Any other marking consistent with the observation must be reachable from a basis marking by firing only unobservable transitions. For the computation of the set of basis markings we propose a simple tabular algorithm and use it to determine a basis reachability tree that can be used as a diagnoser.

/pdf/fault-detection-for-discrete-event-systems-using-petri-nets-2b1pfist6s.pdf

Fault detection for discrete event systems using Petri nets with unobservable transitions

Overview of fault diagnosis methods for Discrete Event Systems

This paper studies online fault detection and isolation of modular dynamic systems modeled as sets of place-bordered Petri nets. The common places among the set of Petri nets modeling a system capture coupling of various system components. The transitions are labeled by events, some of which are unobservable (i.e., not directly recorded by the sensors attached to the system). The events whose occurrence must be diagnosed have unobservable transition labels. These events model faults or other significant changes in the system state. The existing theory of diagnosis of discrete-event systems is extended in the context of the above model. The modular structure of the system is exploited by a distributed algorithm for fault diagnosis. A Petri net diagnoser is associated with every Petri net and the diagnosers communicate in real time during the diagnostic process when the token count of common places changes. A merge function is defined to combine the individual diagnoser states and recover the complete diagnoser state that would be obtained under a monolithic approach. Strategies that reduce the communication overhead are presented. The software implementation of the distributed algorithm is discussed. Note to Practitioners-In the last decade, monitoring, fault detection, and diagnosis methodologies based on the use of discrete-event models have been successfully used in a variety of technological systems ranging from document processing systems to intelligent transportation systems. This paper was motivated by the problem of fault diagnosis for modular (distributed) dynamic discrete-event systems (DES). As a DES modeling formalism, Petri nets offer potential advantages in terms of the distributed representation of the system and the ability to represent coupling of the system components. The systems studied in this paper are sets of modules coupled with each other through various system components and modeled using Petri nets. We present a distributed fault diagnosis algorithm which allows each module in the distributed system to diagnose its faults independently unless completion of a task requires the use of coupled components. In the case of coupling, modules communicate with each other to accurately diagnose the fault. The distributed fault diagnosis algorithm recovers the monolithic diagnosis information at the cost of communication and growing communication overhead. To mitigate that problem, we present an improved version of the algorithm that significantly reduces the communication overhead. Finally, we introduce the software toolbox (written in Matlab and integrated with AT&T Graphviz) and we present a case study of an example of a heating, ventilation, and air-conditioning system where we use the software tool for modeling and analyzing the system

https://www.eecs.umich.edu/umdes/publication_files/sgtase07.pdf

Distributed Diagnosis of Place-Bordered Petri Nets

Predictability of event occurrences in partially-observed discrete-event systems

The problem of detecting and isolating fault events in dynamic systems modeled as discrete-event systems is considered. The modeling formalism adopted is that of Petri nets with labeled transitions, where some of the transitions are labeled by different types of unobservable fault events. The Diagnoser Approach for discrete-event systems modeled by automata developed in earlier work is adapted and extended to on-line fault diagnosis of systems modeled by Petri nets, resulting in a centralized diagnosis algorithm based on the notion of "Petri net diagnosers". A distributed version of this centralized algorithm is also presented. This distributed version assumes that the Petri net model of the system can be decomposed into two place-bordered Petri nets satisfying certain conditions and that the two resulting Petri net diagnosers can exchange messages upon the occurrence of observable events. It is shown that this distributed algorithm is correct in the sense that it recovers the same diagnostic information as the centralized algorithm. The distributed algorithm provides an approach for tackling fault diagnosis of large complex systems.

Distributed diagnosis of discrete-event systems using Petri nets

https://hal.inria.fr/inria-00420497/document

Predictability of Sequence Patterns in Discrete Event Systems

A method for managing alarm events in a physiological monitoring system is described. The method includes validating the accuracy of alarm events by checking if the alarm events are noise events. The method further includes identifying a pattern in alarm sequence or an alarm rate of at least one alarm type associated with the alarm events. The alarm rate is the frequency of the occurrence of alarm events for the particular alarm type. Based on the identified pattern in the alarm sequence and the alarm rate and patient data, an alarm level associated with the alarm type is adjusted. The hospital staff is notified depending on the criticality of the adjusted alarm level. Further, the alarm signals are suppressed when either a patient intervention or a pause signal is detected by the physiological monitoring system.

Sahika Genc

Papers

Distributed Diagnosis of Place-Bordered Petri Nets

Predictability of event occurrences in partially-observed discrete-event systems

Distributed diagnosis of discrete-event systems using Petri nets

Predictability of Sequence Patterns in Discrete Event Systems

Method for managing alarms in a physiological monitoring system