## 1 Introduction  
New technologies to support farm management date back to the second half of the last century when the first automated milking systems were installed in intensive livestock systems for dairy cows. Such systems were introduced because of the importance of an early and accurate determination of oestrus events that became crucial to improving farm production. To the authors' knowledge, the first automatic systems for the electronic recording of milk production were developed in the 1970s, while the first attempts at automatic oestrus detection had to wait until the 1980s [1]. Particular attention was paid to the variation of some physiological parameters. For example, the average skin temperature of cows was found to increase during oestrus [2], mainly in some anatomical parts such as the muzzle and vagina [3]. Milk production was also found to be linked to the oestrus cycle, undergoing a decrease in proximity to the oestrus event [4; 5]. Additionally, the milk produced showed variations in both temperature and conductivity [6; 7]. Moreover, during the oestrus event, the motor activity of cows increases by nearly 400% in 93% of the observed cases [8; 9]. This cow behavioral activity suggested the use of electronic devices attached to the cow body, such as pedometers and collars. Usually, pedometers are used as step counters. When the step count exceeds a predetermined threshold defined by the direct observation of the farmer, the cow is considered to be in a state of oestrus. The main variables involved in this process are, therefore, the frequency of the total step count and the multiplier used to set the aforementioned oestrus alert threshold. Over the years, various scientific methods have been proposed to detect oestrus by pedometer: exceeding the average value by 1 or 2 standard deviations [5; 10]; exceeding a scale value [11] and defining a confidence interval [12; 13]. The most popular methods were based on relative increase thresholds compared to previous measurements [14]. These methods were based on a moving average and compared the current value with that obtained by averaging the data coming from a moving window containing a prefixed number of previous observations of the same cow [15; 16]. Alternatively, other methods were based on the analysis of the exponential smoothing, according to which different weights could be assigned to the previous observations [17]. The main investigations in the scientific literature concerning the use of pedometers [18; 19; 20] have found an associated sensitivity (percentage of ovulating cows detected) well below 70%. This is because about 20% of the cows show no noticeable changes in the overall number of steps. Thanks to research projects that are still in progress at the Department of Agriculture, Food and Environment of the University of Catania (Italy), a prototype of a pedometer for monitoring dairy cow oestrus was developed. The pedometer is characterized by an eco-design casing developed taking into account cow foreleg anatomy and an electronic device able to perform non-invasive measurements of cow activity through specific firmware for data acquisition and transferring [21]. Firstly, the research activities for the development of the pedometer prototype [22; 23] were focused on the determination of cow behavioral algorithms based on accelerometer thresholds defined by processing data coming from uniaxial accelerometers. These algorithms are easily implemented within firmware that can be executed by microcontrollers installed on the mainboards of devices worn by animals. This feature is crucial in real-time monitoring of animal behavior and physiological states, such as the estrous event [23], because it allows the use of Low Power Wide Area telecommunications Network (LPWAN), i.e., LoRa, LoRaWAN, and Sigfox. These networks allow for the connection of numerous devices and low energy consumption which guarantees a longer life of the power supply units, reducing over time the costs of replacing and disposing of the batteries.