## 3 Animal Health Monitoring
### Mental Health Monitoring
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Sensor** & **Advantages** & **Disadvantages** \\ \hline Electrocardiograph & Heart rate monitoring is a likely trustworthy sign of positive affect. & Motion artifacts cause deployment difficulties; It’s not feasible to monitor in real-time or on-site. \\ \hline \multirow{3}{*}{Global Positioning System} & Global Positioning System (GPS) Noninvasive, long-lasting system Expensive at first, poor battery life, and other concerns & Costly at first, battery life, accuracy difficulties, and noise \\ \hline Electroencephalography & Measurement of brain activity that is accurate regardless of subject movement & EEG states and emotional valences are not linked; real-time non-invasive sensors are not yet accessible. \\ \hline Thermal Infrared Imaging & Accurate temperature gauge. & External heat sources may cause interference. \\ \hline \multirow{3}{*}{Electromyogram} & Particularly beneficial for diagnostic purposes and animals with particular breathing habits. & It’s difficult to put into practice, because it’s influenced by a variety of things, including mobility. \\ \hline Olfactory and chemical sensors & Deeply associated with feelings & Do not utilize the animal’s data directly; There are no verified benchmarks for indirect measuring. \\ \hline \end{tabular}
\end{table} 
Table 2: Sensors for animal health [55]. Facial expression technology is already widely employed in human applications [56], and it has the potential to be used in animal emotion studies. Even though the facial traits of farm animals are not fully understood or linked to emotional states, this is an emerging subject of research. Each minor movement in a farm animal's eyes, ears, nose, cheeks, and jaws could indicate a different feeling. Changes in ear position, in addition to changes in eye size, may be symptomatic of certain emotions in farm animals. Pain management is crucial for improving animal wellbeing, and facial expressions are now thought to be a viable method for assessing pain in farm animals. The current grimace scale scoring systems, which analyze many sorts of so-called facial action units in determining pain levels, incorporated body function, physiological reactions, and behavior observation [57]. In addition, in recent years, facial action coding systems (FACS) for many animals have been developed, providing an objective approach for identifying all potential facial emotions in an animal and doing species comparisons [57]. Correlations between physiological factors such as heart rate variability, skin conductance (electrodermal activity), skin temperature fluctuations (infrared data), and facial-emotional reactions described using multi-sensor complex data would necessitate a substantial amount of computational capacity [58]. Distinguishing expressive qualities of animals from sensor data based on a plethora of physiological processes, as well as the dynamic changes in their emotional states over time, necessitates the ability to systematically extract information from huge data. Data collected from sensors is solely one component of the process of interpreting animal emotions and another aspect of the process is algorithms that can be used to examine the collected data [59]. In general, these methods rely on modern computer ML capabilities. ML refers to the ability of computer algorithms to learn and change as more data is introduced into the system. The systems are all capable of taking in all available information (facial expressions, temperature readings, vocalizations, etc.) and establishing standards, then comparing these benchmarks to fresh information to sort out discordant results. ML approaches are required to forecast and frequently estimate an individual's affective states based on physiological functions and behavior. ML algorithms are helpful because of the vast volume of data and in the feature selection and optimization of emotional factors such as valence, duration, and activation [58].