4.6 Article

Temporal Analysis and Classification of Sensor Signals

期刊

SENSORS
卷 23, 期 6, 页码 -

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MDPI
DOI: 10.3390/s23063017

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sensors; Duration Calculus; temporal logic; signal and data processing; heart rate monitoring; interval logic

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Understanding sensor behavior and specifications is complex, with variables such as application domain, sensor usage, and architectures. Various models and technologies have been developed to address this, including a new interval logic called Duration Calculus for Functions (DC4F), which precisely specifies signals from sensors used in heart rhythm monitoring. DC4F is an extension of Duration Calculus, suitable for describing complex interval-dependent behaviors and allows for the specification of desired behavior. This is advantageous over machine learning algorithms, as it allows for hypothesis formulation and user-specified behavior.
Understanding the behaviour of sensors, and in particular, the specifications of multisensor systems, are complex problems. The variables that need to be taken into consideration include, inter alia, the application domain, the way sensors are used, and their architectures. Various models, algorithms, and technologies have been designed to achieve this goal. In this paper, a new interval logic, referred to as Duration Calculus for Functions (DC4F), is applied to precisely specify signals originating from sensors, in particular sensors and devices used in heart rhythm monitoring procedures, such as electrocardiograms. Precision is the key issue in case of safety critical system specification. DC4F is a natural extension of the well-known Duration Calculus, an interval temporal logic used for specifying the duration of a process. It is suitable for describing complex, interval-dependent behaviours. Said approach allows one to specify temporal series, describe complex interval-dependent behaviours, and evaluate the corresponding data within a unifying logical framework. The use of DC4F allows one, on the one hand, to precisely specify the behaviour of functions modelling signals generated by different sensors and devices. Such specifications can be used for classifying signals, functions, and diagrams; and for identifying normal and abnormal behaviours. On the other hand, it allows one to formulate and frame a hypothesis. This is a significant advantage over machine learning algorithms, since the latter are capable of learning different patterns but fail to allow the user to specify the behaviour of interest.

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