Lower extremity fractures pose challenges due to prolonged healing times and limited assessment methods. Integrating wearable sensors with machine learning can help overcome these challenges by providing objective assessment and predicting fracture healing. In this retrospective study, data from a gait monitoring insole on 25 patients with closed lower extremity fractures were analyzed. Continuous underfoot loading data were processed to isolate steps, extract metrics, and feed them into three white-box machine learning models. Decision tree and Lasso regression aided feature selection, while a logistic regression classifier predicted days until fracture healing within a 30-day range. Evaluations via 10-fold cross-validation and leave-one-out validation yielded stable metrics, with the model achieving a mean accuracy, precision, recall, and F1-score of approximately 76%. Feature selection revealed the importance of underfoot loading distribution patterns, particularly on the medial surface. Our research facilitates data-driven decisions, enabling early complication detection, potentially shortening recovery times, and offering accurate rehabilitation timeline predictions.

Partial weight bearing prescriptions for tibial fracture care are made with little data to support their efficacy. To provide long term tibial load data that can be used to study, guide, and monitor partial weight bearing, we developed a novel load sensor that can record the load placed on an injured limb over a two-week period of time. The prototype load sensor demonstrated high linearity, low hysteresis and low static and dynamic drift. Preliminary human testing on the load sensor demonstrated the ability of the load sensor to capture load profile during underfoot recording. Presented are the bench testing and human testing data on the prototype load sensor verifying the design of an economic and durable system that allows for 2 weeks of recording of normal loads experienced by the tibia. This design motivates a next generation approach for use in clinical trials that will enable clinicians and researchers to improve current partial weight bearing prescriptions, thus improving tibial fracture outcomes.