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A 4\(\mu\)W Low-Power Audio Processor System for Real-Time Jaw Movements Recognition in Grazing Cattle

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Abstract

Precision livestock farming consists of technological tools and techniques to improve livestock management. Proper detection and classification of jaw movement (JM) events are indispensable for the estimation of dry matter intake, detection of health problems, and flag the onset of estrus, among other information. The analysis of acoustic signals is one of the most accepted ways to monitor the feeding behavior of free-grazing cattle. Different acoustic methods have been developed for recognizing JM-events in recent years. However, their operation is limited to off-line analysis on a personal computer. The lack of on-line acoustic monitoring systems is associated with the challenging operation requirements (low-power consumption, autonomy, portability, robustness and non-intrusive on the animal). In this paper, a fixed-point variant of the chew-bite energy-based algorithm is presented. This algorithm is implemented on a new low-power audio processor system for real-time recognition of JM-events. The system includes a Nanocontroller processor, which is always-on and detects JM-events; and a second transport-triggered architecture (TTA) based processor, which is mainly in power-down and classifies JM-events. The results demonstrate that the proposed fixed-point JM-events recognizer achieves a recognition rate of 91.4% and 90.2% in noiseless and noisy conditions, respectively. The recognition rate increases by 6.1% regarding a previous reference on-line system. Moreover, the proposed audio processor system chip consumes 4 μW on average, i.e., only 2.3% of the power of an always-on TTA-based processor system for the same audio sequence. An exemplary implementation of the proposed system in a 65 nm low-leakage CMOS technology is given.

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Availability of Data and Material

The authors declare that all relevant evaluation data are available in the figures and tables within the article. The generated and analyzed datasets are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank KBS Robotic Dairy Farm staff and Campo Experimental J. Villarino Dairy Farm staff involved in the data gathering, curation and labelling. A special thanks to Julio Galli from Facultad de Ciencias Agrarias, Universidad Nacional de Rosario and Santiago Utsumi from the Department of Animal and Range Science, New Mexico State University, United States for their constant support, help and assistance during the completation of this work. This work has been granted by the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) and the Deutscher Akademischer Austauschdienst (DAAD) (ID: 57507441).

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Author notes

  1. Luciano S. Martinez-Rau, Moritz Weißbrich and Guillermo Payá-Vayá contribute equally to this work.

Authors and Affiliations

  1. Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL/CONICET, Ciudad Universitaria UNL, 4to piso FICH, Santa Fe, S3000, Santa Fe, Argentina

    Luciano S. Martinez-Rau

  2. Chair for Chip Design for Embedded Computing, Technische Universität Braunschweig, Mühlenpfordtstraße 23, 38106, Braunschweig, Germany

    Moritz Weißbrich & Guillermo Payá-Vayá

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  1. Luciano S. Martinez-Rau
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Martinez-Rau, L.S., Weißbrich, M. & Payá-Vayá, G. A 4\(\mu\)W Low-Power Audio Processor System for Real-Time Jaw Movements Recognition in Grazing Cattle. J Sign Process Syst 95, 407–424 (2023). https://doi.org/10.1007/s11265-022-01822-y

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