Gait Instability Detection with Temporal Convolutional Networks for Orthopedic Patients

Gait instability is a significant risk for postoperative orthopedic patients, frequently leading to falls and related accidents, hence extending recovery time and escalating healthcare expenses. This research introduces an innovative method to improve patient safety by utilizing Temporal Convolutional Networks (TCNs) for the detection of gait instability. TCNs are engineered to identify temporal patterns in sequential data, rendering them appropriate for the analysis of gait sequences. Gait data were acquired from orthopedic patients using wearable sensors, and the TCN model was developed to differentiate between stable and unstable gait patterns. Experimental findings indicate that the proposed system attains a detection accuracy of 90%, surpassing traditional machine learning methods. Moreover, the approach delivers real-time notifications to healthcare professionals, allowing prompt actions to avert falls. This system enhances patient safety and assists physicians in objectively tracking rehabilitation progress. The results underscore the promise of TCN-based systems for incorporation into clinical orthopedic practice, providing data-driven approaches to improve patient outcomes and facilitate informed decision-making throughout recovery.