Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA.
Department of Orthopaedics, University of Utah, Salt Lake City, UT 84112, USA.
Sensors (Basel). 2024 Aug 17;24(16):5321. doi: 10.3390/s24165321.
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.
下肢骨折由于愈合时间长和评估方法有限而带来挑战。将可穿戴传感器与机器学习相结合，可以通过提供客观评估和预测骨折愈合来克服这些挑战。在这项回顾性研究中，分析了 25 例闭合性下肢骨折患者的步态监测鞋垫数据。对连续足底负荷数据进行处理，以分离步骤、提取指标，并将其输入到三个白盒机器学习模型中。决策树和套索回归辅助特征选择，而逻辑回归分类器在 30 天范围内预测骨折愈合的天数。通过 10 倍交叉验证和留一法验证得出了稳定的指标，模型的平均准确率、精度、召回率和 F1 得分为约 76%。特征选择揭示了足底负荷分布模式的重要性，特别是在内侧表面。我们的研究促进了数据驱动的决策，能够早期发现并发症，潜在地缩短康复时间，并提供准确的康复时间线预测。
