A Wearable Sensor System for Lameness Detection in Dairy Cattle

• Main Authors: Juan Haladjian, Johannes Haug, Stefan Nüske, Bernd Bruegge
• Format: Article
• Language: English
• Published: MDPI AG 2018-05-01
• Series: Multimodal Technologies and Interaction
• Subjects: gait analysis; anomaly detection; unsupervised machine learning
• Online Access: http://www.mdpi.com/2414-4088/2/2/27

Cow lameness is a common manifestation in dairy cattle that causes severe health and life quality issues to cows, including pain and a reduction in their life expectancy. In our previous work, we introduced an algorithmic approach to automatically detect anomalies in the walking pattern of cows using a wearable motion sensor. In this article, we provide further insights into a system for automatic lameness detection, including the decisions we made when designing the system, the requirements that drove these decisions and provide further insight into the algorithmic approach. Results from a controlled experiment we conducted indicate that our approach can detect deviations in cows’ gait with an accuracy of 91.1%. The information provided by our system can be useful to spot lameness-related diseases automatically and alarm veterinarians.