Research on Six-Axis Sensor-Based Step-Counting Algorithm for Grazing Sheep

Research on Six-Axis Sensor-Based Step-Counting Algorithm for Grazing Sheep

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Step counting is an effective method to assess the activity level of grazing sheep.However, existing step-counting algorithms have limited adaptability to sheep walking patterns and fail to eliminate false step counts caused by abnormal behaviors.Therefore, this study proposed a step-counting Tick Sprays algorithm based on behavior classification designed explicitly for grazing sheep.The algorithm utilized regional peak detection and peak-to-valley difference detection to identify running and leg-shaking behaviors in sheep.

It distinguished leg shaking from brisk walking behaviors through variance feature analysis.Based on the recognition results, different step-counting strategies were employed.When running behavior was detected, the algorithm divided the sampling window by the baseline step frequency and multiplied it by a scaling factor to accurately calculate the number of steps for running.No step counting was performed for leg-shaking behavior.

For other behaviors, such as slow and brisk walking, a window peak detection algorithm was used for step counting.Experimental results demonstrate a significant improvement in the accuracy of the proposed algorithm compared to the peak detection-based method.In addition, the Tank Tops experimental results demonstrated that the average calculation error of the proposed algorithm in this study was 6.244%, while the average error of the peak detection-based step-counting algorithm was 17.

556%.This indicates a significant improvement in the accuracy of the proposed algorithm compared to the peak detection method.