Public articles linked to the same research event.
发表出处待核验 This thesis explores how sensor signals combined with AI models can quantify equine locomotion and respiration, finding that a single limb sensor can accurately classify terrain type even at low sampling rates, that a combination of head, withers, and pelvis sensors can discriminate between sound and lame strides with performance varying by front or hind limb lameness, that vertical ground forces are better estimated from upper-body sensors than limb sensors, that dynamic respiratory rate can be computed from downsampled audio signals, and that explainable AI confirms models rely on kinematic landmarks similar to those used by veterinarians, while also presenting a data collection platform for harness racing horses and publishing an audio dataset.
This thesis explores how sensor signals combined with AI models can quantify equine locomotion and respiration, finding that a single limb sensor can accurately classify terrain type even at low sampling rates, that a combination of head, withers, and pelvis sensors can discriminate between sound and lame strides with performance varying by front or hind limb lameness, that vertical ground forces are better estimated from upper-body sensors than limb sensors, that dynamic respiratory rate can be computed from downsampled audio signals, and that explainable AI confirms models rely on kinematic landmarks similar to those used by veterinarians, while also presenting a data collection platform for harness racing horses and publishing an audio dataset.
This thesis explores how sensor signals combined with AI models can quantify equine locomotion and respiration, finding that a single limb sensor can accurately classify terrain type even at low sampling rates, that a combination of head, withers, and pelvis sensors can discriminate between sound and lame strides with performance varying by front or hind limb lameness, that vertical ground forces are better estimated from upper-body sensors than limb sensors, that dynamic respiratory rate can be computed from downsampled audio signals, and that explainable AI confirms models rely on kinematic landmarks similar to those used by veterinarians, while also presenting a data collection platform for harness racing horses and publishing an audio dataset.
This thesis explores how sensor signals combined with AI models can quantify equine locomotion and respiration, finding that a single limb sensor can accurately classify terrain type even at low sampling rates, that a combination of head, withers, and pelvis sensors can discriminate between sound and lame strides with performance varying by front or hind limb lameness, that vertical ground forces are better estimated from upper-body sensors than limb sensors, that dynamic respiratory rate can be computed from downsampled audio signals, and that explainable AI confirms models rely on kinematic landmarks similar to those used by veterinarians, while also presenting a data collection platform for harness racing horses and publishing an audio dataset.