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manuscript/20210908_Adriaens_UWB_.tex

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 	\item Implications
 
 
-Precision livestock farming solutions typically aim 
+Precision livestock farming solutions typically aim at supporting monitoring and decision taking by farmers using on-farm sensors measuring a specific aspect of the animals' behavior, performance or, in the case of e.g. dairy cows, product. The resulting raw sensor data are often noisy time series, prone to errors caused not only by sensor failure or the harsh and changing farm environments in which they operate, but also by the animals' specific physiology itself. Consequently, these data have little value without proper (pre-)processing algorithms that translate the raw measures in features interpret-able by the targeted end-users. 
 
-Cow behavior, varying from feeding and drinking, over where and when animals rest, lie down and ruminate, and social interactions potentially offers new paths both for research and commercial decision support systems that can help the farmer manage their herd, optimize production and quickly act upon health and welfare problems. A continuous and essential step to better unlock the potential of cow behavioral analyses is the development of new sensor technologies, and more importantly, the corresponding data-processing algorithms that allow just and timely interpretation of their data.
+In dairy production, precision technologies are vastly deployed and implemented. Individual animals are not only vulnerable because of the physiological stress they endure during lactation, timely and specific interventions can obviate animal suffering and financial losses. As each cow is highly valued and modern dairy farms grew larger over the past decade, investments in sensor technology became increasingly justifiable. Out of the many technologies available, a system monitoring cow position, and derived behavioral features and interactions, not only promises to disclose cow health, but might also reveal welfare and social interactions - aspects that become increasingly important in an ever-changing livestock production landscape. Hence, cow behavior, varying from feeding and drinking over where and when animals rest and ruminate interactions potentially offer new paths both for research and commercial decision support systems that can help the farmer manage their herd, optimize production and quickly act upon health and welfare problems. Lying behavior 
+A continuous and essential step to better unlock the potential of cow behavioral analyses is the development of new sensor technologies, and more importantly, the corresponding data-processing algorithms that allow precise and timely interpretation of their data.
 
-Ultra wide band is technology allows the transmission of high amounts of data over small distances with very low energy. In an indoor positioning system, Radio-Frequency identification (RFID) signals are transmitted across a wide bandwidth and captured by an antenna. The tags worn by the individual cows allow precise localization of the animals with low power usage, even in cluttered indoor environments {\color{b}{1}}. Upon development of appropriate data interpretation algorithms, indoor positioning systems allow studying and monitoring cow behavior, including general activity, resting, feeding, drinking and social interactions with a single sensor system, giving it a relative advantage over e.g. commercially available pedometer systems. Despite its continuous development and high potential for animal monitoring, UWB-based positioning is yet sparingly adopted for livestock applications. A main reason for this is, similar to many new sensor technologies, the lack of proper data-processing algorithms that can translate raw data time series into information valuable to the farmer on which decision support can be based. In the case of indoor positioning systems, data interpretation is complicated by inaccuracy and noise in the time series, missing data, and the heteroscedasticity in the time series. This heteroscedasticity partly results from differences in behavior, but also depends on e.g. the position of the animal in the barn with regard to the antenna and interactions of the signal with metal (e.g. the feeding rack) and water bodies (e.g. other cows). This heteroscedasticity and the unpredictability  of noise impair straightforward interpretation of the positioning data and its derivatives (e.g. distance traveled). Nonetheless, dedicated data processing enables classification of behavior from multiple animals with high accuracy.
+Ultra wide band is technology allows the transmission of high amounts of data over small distances with very low energy. In an indoor positioning system, Radio-Frequency identification (RFID) signals are transmitted across a wide bandwidth and captured by an antenna. The tags worn by the individual cows allow precise localization of the animals with low power usage, even in cluttered indoor environments {\color{b}{1}}. Upon development of appropriate data interpretation algorithms, indoor positioning systems allow studying and monitoring cow behavior, including general activity, resting, feeding, drinking and social interactions with a single sensor system, giving it a relative advantage over e.g. commercially available pedometer systems. Despite its continuous development and high potential for animal monitoring, UWB-based positioning is yet sparingly adopted for livestock applications. As for many new sensor technologies, the main reason for this is the lack of proper data-processing algorithms that translate raw data time series into decision support information valuable to the farmer. In the case of indoor positioning systems, data interpretation is complicated by the inaccuracy and noise in the time series, missing data, and its heteroscedasticity. The latter partly results from differences in behavior, but previous research also highlighted dependency on the position of the animal in the barn with regard to the antenna and interactions of the signal with metal (e.g. the feeding rack) and water bodies (e.g. other cows). These aspects impair straightforward interpretation of the positioning data and its derivatives (e.g. distance traveled). Nonetheless, as dedicated data processing enables classification of behavior from multiple animals with high accuracy has wide application potential, several studies with this topics have been published in the past few years {\color{m} refs}.
 
 In this study, a methodology to identify lying behavior of dairy cows using a UWB-based indoor positioning system was developed and validated against the lying bouts returned by a commercial accelerometer-based system. The methodology relies on the detection of changepoints in the position time series, {\color{r}elegantly avoiding fixed thresholds or severe assumptions on the statistical properties of the data.}