TY - JOUR
T1 - Sow's activity classification device using acceleration data - a resource constrained approach
AU - Marchioro, Gilberto Fernandes
AU - Cornou, Cecile
AU - Kristensen, Anders Ringgaard
AU - Madsen, Jan
PY - 2011/6
Y1 - 2011/6
N2 - This paper discusses the main architectural alternatives and design decisions in order to implement a sows' activity classification model on electronic devices. The different possibilities are analyzed in practical and technical aspects, focusing on the implementation metrics, like cost, performance, complexity and reliability. The target architectures are divided into: server based, where the main processing element is a central computer; and embedded based, where the processing is distributed on devices attached to the animals. The initial classification model identifies the activities performed by the sows using a multi-process Kalman filter having, as input, 3-axes data from accelerometers. However, the power demanding hardware resources to run the filters require frequent battery recharges, making its use unsuitable in the current state-of-the-art. It motivated the development of a heuristic classification approach, focusing on the resource constrained characteristics of embedded systems. The new approach classifies the activities performed by the sows with accuracy close to 90%. It was implemented as a hardware module that can easily be instantiated to provide preprocessed information to models in order to detect important situations in the sows' life, e.g. the onset of farrowing.
AB - This paper discusses the main architectural alternatives and design decisions in order to implement a sows' activity classification model on electronic devices. The different possibilities are analyzed in practical and technical aspects, focusing on the implementation metrics, like cost, performance, complexity and reliability. The target architectures are divided into: server based, where the main processing element is a central computer; and embedded based, where the processing is distributed on devices attached to the animals. The initial classification model identifies the activities performed by the sows using a multi-process Kalman filter having, as input, 3-axes data from accelerometers. However, the power demanding hardware resources to run the filters require frequent battery recharges, making its use unsuitable in the current state-of-the-art. It motivated the development of a heuristic classification approach, focusing on the resource constrained characteristics of embedded systems. The new approach classifies the activities performed by the sows with accuracy close to 90%. It was implemented as a hardware module that can easily be instantiated to provide preprocessed information to models in order to detect important situations in the sows' life, e.g. the onset of farrowing.
U2 - 10.1016/j.compag.2011.04.004
DO - 10.1016/j.compag.2011.04.004
M3 - Journal article
SN - 0168-1699
VL - 77
SP - 110
EP - 117
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
IS - 1
ER -