# Development of a Device and Algorithm Research for Akhal-Teke Activity Level Analysis  
[PERSON]  
1 School of Software, Shanxi Agricultural University, Jinzhong 030801, China; [EMAIL_ADDRESS] (X.C.); [EMAIL_ADDRESS] (F.L.); [EMAIL_ADDRESS] (Q.F.)  
2 Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing 100081, China; [EMAIL_ADDRESS] (3) College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China (4) School of Engineering and Technology, CQUniversity Brisbane, 160 Ann St., Brisbane City, QLD 4000, Australia; [EMAIL_ADDRESS] (5) Institute of Animal Husbandy Quality Standards, Xinjiang Academy of Animal Science, Urumqi 830011, China (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (10) (13) (15) (16) (18) (19) (17) (19) (18) (18) (19) (19  
to quantify equine motion data, trainers can specify training durations based on the horse's activity levels, thereby avoiding training errors and injuries caused by excessive fatigue.  
Activity levels are differentiated based on the horse's gaits. Three-axis accelerometer data have been proven to detect animal gaits [2]. For instance, [PERSON] et al. [3] collected accelerometer data using a smartwatch application and employed novel outlier detection, feature extraction, and machine learning methods to analyze horse motion states through monitoring devices on the saddle and the rider's wrist. [PERSON] et al. [4] created a system for recognizing cattle behaviors, including walking, feeding, lying, and standing, using accelerometers attached to both the leg and collar. [PERSON] et al. [5] explored interspecies comparisons of daily total locomotor activity under various management conditions for sheep and horses. [PERSON] et al. [6] validated an automated recording system for evaluating behavioral activity levels in sheep. Current research on gait recognition algorithms primarily focuses on analyzing behavioral patterns of sheep [7; 8; 9] and cattle [10; 11; 12]. This study has drawn on their research method, utilizing gait analysis to measure activity levels.  
During horse training, the duration of training for different activity levels directly impacts the effectiveness of the training [13]. Trainers can finely adjust their strategies by analyzing the horse's activity levels. For example, in the early stages of training, medium activity contributes to building trust and foundational skills, while horses needing improvements in speed and strength benefit from higher activity level exercises [14]. To accurately determine each activity level, precise gait recognition methods are crucial. Current research tends to apply deep learning technology to analyze horse gaits [15; 16; 17; 18] precisely. Methods based on deep learning are challenging to implement directly on resource-constrained wearable devices. This study proposes a horse activity level evaluation model based on acceleration threshold analysis to address this issue. This approach is well suited for low-power and embedded devices such as the STMicroelectronics 32-bit Arm Cortex-M Microcontroller (STM32) series hardware devices, which have been used in this study [19]. The proposed approach enables wearable devices to parse and record the time spent in different gaits accurately.  
The focus of this study is to provide a new evaluation model for horse activity levels and run the model on hardware devices to evaluate horse activity, providing a more convenient and comprehensive solution for horse activity monitoring and management. Additionally, this study employs non-linear segmentation methods for the preliminary classification of horse activity levels, laying a foundation for the detailed classification of complex activity levels using the acceleration interval counting method. Building upon the preliminary classification, combined with inter-segment variance analysis and multi-level threshold processing, this study further achieves fine-grained division and precise identification of horse activity levels.