Robust Sensor-Orientation-Independent Feature Selection for Animal Activity Recognition on Collar Tags

Abstract

Fundamental challenges faced by real-time animal activity recognition include variation in motion data due to changing sensor orientations, numerous features, and energy and processing constraints of animal tags. This paper aims at finding small optimal feature sets that are lightweight and robust to the sensor's orientation. Our approach comprises four main steps. First, 3D feature vectors are selected since they are theoretically independent of orientation. Second, the least interesting features are suppressed to speed up computation and increase robustness against overfitting. Third, the features are further selected through an embedded method, which selects features through simultaneous feature selection and classification. Finally, feature sets are optimized through 10-fold cross-validation. We collected real-world data through multiple sensors around the neck of five goats. The results show that activities can be accurately recognized using only accelerometer data and a few lightweight features. Additionally, we show that the performance is robust to sensor orientation and position. A simple Naive Bayes classifier using only a single feature achieved an accuracy of 94 % with our empirical dataset. Moreover, our optimal feature set yielded an average of 94 % accuracy when applied with six other classifiers. This work supports embedded, real-time, energy-efficient, and robust activity recognition for animals.

Geospatial Analysis and the Internet of Things

“…The majority of studied papers, especially those where mobile phones were used, also harnessed GPS sensing for acquiring exact user location. Moreover, ultrasonic water sensors [14], pollution sensors [10] infrared (IR) cameras [15], temperature, humidity and pressure sensors [16], geo-cubes, meteorological and hydrological sensors [17], buoy, pressure and water column heights sensors [18], as well as 3D accelerometer and gyroscope sensors and animal collar tags [19], [20] have been recorded.…”

“…Custom-made IoT sensors made use of the IEEE802.11 wireless transmission standard, such as ultrasonic water sensors [14] or a combination of GPRS/Wi-Fi at an air quality sensory system [16]. Moreover, a low power wide area network was observed in [19], for recording animal activity and in [15], for video transmission from IR cameras. In particular for wildlife animal monitoring [20], a combination of Bluetooth low energy (BLE) and LoRa was proposed.…”

Horsing Around—A Dataset Comprising Horse Movement

“…Activities from animals can be recognized from motion data [1,2]. The advent of small, lightweight, and low-power electronics has propelled research in Animal Activity Recognition (AAR).…”

“…After training, the classifier can classify unlabeled raw data-samples into the learned activity categories. Recently, various studies utilized IMUs for AAR regarding: wildlife [3][4][5][6][7][8][9], livestock [1,2,[10][11][12][13][14][15][16][17][18], and pets [19][20][21].…”

Synchronization between Sensors and Cameras in Movement Data Labeling Frameworks

“…In order to label movement data of sheep [6], goats [7], and horses [8,9], a labeling framework was developed using a Matlab GUI [12]. The code of the framework is publicly available [5].…”