Table 5.

Sensor fusion for animal monitoring and assessment (Decision/High level).

Author	Hou et al. [33]	Feng et al. [13]	Bocaj et al. [29]	Schmeling et al. [40]	Dziak et al. [32]	Sturm et al. [41]	Rahman et al. [37]	Corcoran et al. [31]
Year	2021	2021	2020	2021	2022	2020	2018	2019
Qualsyst Score	83%	71%	70%	96%	75%	100%	86%	67%
Problem addressed	Welfare	Wildlife monitoring	Welfare	Welfare	Wildlife monitoring	Increase production	Welfare	Wildlife monitoring
Fusion application	Health detection	Activity detection	Activity detection	Activity detection	Individuals recognition	Health detection	Activity detection	Individuals recognition
Target species	Pigs	Felines	- Horses - Goats	Cows	- Felines - Birds	Cows	Cows	Koalas
Animal interface	-	-	Collar	Collar	-	Ear-tag	-Collar - Ear-tag - Halter	Collar
Postures	-	Standing	-	- Standing - Lying	- Standing - Lying on the side	- Walking - Running - Flying postures	- Stationary - Non-Stationary - Lying on belly	Standing
Activities	-	- Walking - Running	- Galloping - Walking - Running - Trotting - Feeding/eating - Grassing - Walking (with rider)	- Normal - Walking - Feeding/eating - Resting	- Walking - Running - Trotting - Flying	Ruminating	- Grassing - Ruminating	-
Participants	10	-	11	7–11	-	671	-	48
Sensors/Sampling rate (Hz, FPS)	Visual spectrum cameras	Visual spectrum cameras (30 FPS)	- Accelerometer (100 Hz) - Magnetometer (12 Hz) - Gyroscope (100 Hz)	- Accelerometer - Magnetometer - Gyroscope - Visual spectrum cameras (60 FPS)	- Visual spectrum cameras - Infrared thermal camera	Accelerometer (10 Hz)	Accelerometer (30 Hz)	Infrared thermal camera (9 Hz)
Feature extraction	- Histogram-oriented gradients - Local binary patterns - ML models (LSTM-models, CNN-models, etc.)	ML models (LSTM-models, CNN-models, etc.)	ML models (LSTM-models, CNN-models, etc.)	ML models (LSTM-models, CNN-models, etc.)	ML models (LSTM-models, CNN-models, etc.)	ML models (LSTM-models, CNN-models, etc.)	-	ML models (LSTM-models, CNN-models, etc.)
Feature type	Spatial	Spatiotemporal	-	- Spatiotemporal - Motion	- Spatiotemporal - Motion	- Spatial - Statistical	- Statistical - Frequency domain	-
Data alignment	Same datasize	Same datasize	Same datasize	-	Downsampling	Timestamps	Timestamps	Same datasize
Machine learning algorithms	- Convolutional Neural Networks - Bayesian-CNN	- VGG - LSTMs	Convolutional Neural Networks	- SVM - Naive Bayes - Random forest	- YOLO - FASTER RCNN	- Naive Bayes - Nearest centroid classification	Random forest	- Convolutional Neural Networks - YOLO - FASTER RCNN
Sensor fusion type	Two or more classifiers	Single fusion algorithm	Single fusion algorithm	Multimodal switching	Single fusion algorithm	Feature level fusion	Mixing	Two or more classifiers
Performance metrics	- Accuracy ($98.12$%) - Precision	Accuracy (92%)	- Accuracy ($97.42$%) - F1-score ($+84$%)	Accuracy ($87.3$%)	Accuracy (94%)	- Accuracy ($72.58$%) - Sensibility/Recall ($66.98$%) - Precision ($32.27$%) - F1-score ($43.56$%) - Mattews correlation ($31.55$%) - Youdens index ($40.61$%)	F1-score ($93.2$%)	Probability of detection (87%) Precision (49%)