One of the most relevant physiological parameters in dogs is respiratory rate (RR). The aim of this paper is to present a novel wearable system that allows to accurately estimate RR in dogs, and to compare it to a gold standard in static conditions. Data from 12 dogs were acquired while the animals were anesthetized and attached to a vital signs monitor. The experimental setup consisted of three Inertial Measurement Units (IMUs) applied on the dog, and a video camera filming the RR value shown on the monitor. The range of RR values analyzed in the study is 0 to 29 breaths per minute, read by the vital signs monitor. The mean RMSE for the data acquisitions is 1.68 breaths per minute. The values of the filtering parameters that allow to obtain the best performance depend on the specific acquisition. This result demonstrates that adaptive filtering is a viable method for the application. Future developments include tests on a larger dataset, and trials on dogs in unconstrained environments and during movement.

Validation of a Wearable System for Respiratory Rate Monitoring in Dogs

Birettoni, Francesco
Conceptualization
;
Bufalari, Antonello
Investigation
;
2024

Abstract

One of the most relevant physiological parameters in dogs is respiratory rate (RR). The aim of this paper is to present a novel wearable system that allows to accurately estimate RR in dogs, and to compare it to a gold standard in static conditions. Data from 12 dogs were acquired while the animals were anesthetized and attached to a vital signs monitor. The experimental setup consisted of three Inertial Measurement Units (IMUs) applied on the dog, and a video camera filming the RR value shown on the monitor. The range of RR values analyzed in the study is 0 to 29 breaths per minute, read by the vital signs monitor. The mean RMSE for the data acquisitions is 1.68 breaths per minute. The values of the filtering parameters that allow to obtain the best performance depend on the specific acquisition. This result demonstrates that adaptive filtering is a viable method for the application. Future developments include tests on a larger dataset, and trials on dogs in unconstrained environments and during movement.
2024
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/1577714
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