Validation of a Time-Distributed residual LSTM–CNN and BiLSTM for equine behavior recognition using collar-worn sensors

• Published in: Computers and electronics in agriculture Vol. 231; p. 109999
• Main Authors: Kirsch, Katharina, Strutzke, Saskia, Klitzing, Lara, Pilger, Franziska, Thöne-Reineke, Christa, Hoffmann, Gundula
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2025
• Subjects: accelerometers; agriculture; automation; Behavior classification; data collection; Deep learning; electronics; Equine activity recognition; horses; humans; IMU; Wearable sensors; welfare assessment; Behavior classification; Deep learning; IMU; Wearable sensors; Equine activity recognition

•Deep neural network recognizes equine behavior from collar worn sensor data.•Framework combines local spatiotemporal and long-term temporal feature extraction.•Model achieved > 93 % accuracy in 10-fold and > 85 % in leave-one-out cross-validation.•Performance varied across behaviors and housi...