We in it. The third level, integration of information,

We are living in a
digital world, where digital devices are increasingly affecting our

lives, by handling
different tasks and making our lives easier. Wearables have also been a part of
the digitalization trend and process in the last years, not only for humans but
for animals as well. The automation of processes can especially help dairy
farmers to reduce physical labour and labour costs, considering the increase of farm
sizes and the need of higher-yielding cattle. (de Koning, 2010) This review delivers an organised overview of the
wearable sensors developed to assist with three different health issues of
dairy animals like mastitis, fertility and locomotion.

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Before starting with the
overview and comparison of the sensor systems developed for each health issue,
it is useful to describe the framework with the four levels of a sensor system,
where the comparison is based on. The first level is technique, which describes the different types of sensors that
have already been developed. In the second level named data interpretation, is shown if there is an existing algorithm in the system that processes the data gathered
from the sensor and produces information by determining the changes in it. The
third level, integration of information,  presents a decision support model which
combines the information produced by the system with external data coming for
example from the advisor or farmer and advises
how to act upon the detected event. The fourth and last level, decision making
determines if the decision is taken by the farmer or autonomously by the
advanced system sensor. (Rutten, 2013)

Starting with the first
disease, mastitis is inflammation of the mammary gland in the breast due to
bacterial infection. It is the most common and most costly disease of dairy
cattle.(McGill, 2003) For sensor systems detecting mastitis, 31 publications
were found where 37 sensor systems are described. 48% of the sensors found use
electrical conductivity, 23 % combine electrical conductivity and milk colour sensors – inline and 16 % are biosensors
which detect different enzymes. 92 % of the sensors found interpret the gathered data, but they do not
integrate external information for the decision making part. 43 % of them give a mastitis alert to the farmer, 32 % show
the probability of the disease as well, whereas only 5 % degree and classify the disease. (Rutten, 2013)

The second health issue that
is being contemplated is fertility. 61% of the sensors study the activity of
the cow, 15% of them are biosensors and measure the progesterone levels in milk
and another 15% consider their mounting behaviour. 75% of the sensor systems
developed include data interpretation, but there is no integration of external
information for decision making. 60 % of them give an estrus alert, but only
15% of them show the probability of the estrus alert as well. (Rutten, 2013) The
objective of sensors developed for fertility
issues is to prevent missing cases of true estrus caused by using visual
observation. (Firk et al., 2002) The oldest and most studied sensor is the
pedometer. (Williams et al., 1981) A pedometer counts each step the animal
takes by detecting the motion of its hips and legs. Pedometers can detect
estrus accurately and appear to be a promising tool for prediction of ovulation
and hence could be a tool for improving fertilization rates. (Roelofs,

Lameness is considered to be the
third most influential health issue, after mastitis and fertility issues. Researchers
have observed the frequency of this disease in 21 farms and the physical
effects caused by it on 2183 cows. The final result showed that a lame cow
causes a loss of 110 € per foot per year. (Enting, 1997) Pedometers & 3D
accelerometers are the most studied sensors. (Mol, 2009) Accelerometers measure
the overall dynamic body acceleration. (Wilson, 2006) 45 % of the sensors
developed to detect lameness study the weight distribution between the legs, 42
% study their walking behaviour and 13 %
study their walking activity. However, only half of the systems can interpret
the data gathered from the sensors, whereas the rest shows only raw and unclear
results. (Rutten, 2013)

conclusion, the development of wearables for dairy animals is still in its
early stages. There have been a lot of research and development done on the
technique and data interpretation levels, especially for mastitis and fertility
issues, whereas sensors helping with locomotion detection are still in the technique development phase. Therefore, the alerts
coming from the wearables for mastitis and estrus are more informative than the
locomotion alerts. (Rutten, 2013) It is also still unclear if the locomotion sensors can detect lameness at an
early stage. (Juarez et al., 2003). Based on the findings of
this literature review, we can observe that this topic is still quite
innovative and there is still a lot of room for improvements and new developments.