This letter presents a transfer learning method to enhance locomotion intent prediction in novel transfemoral amputee subjects, particularly in data-sparse scenarios. Transfer learning is done with three pre-trained models trained on separate datasets: transfemoral amputees, able-bodied individuals, and a mixed dataset of both groups. Each model is subsequently fine-tuned using data from a new transfemoral amputee subject. While subject-dependent models, trained and tested using individual user data, can achieve the least error rate, they require extensive training datasets. In contrast, our transfer learning approach yields comparable error rates while requiring significantly less data. This highlights the benefit of using pre-existing, pre-trained features when data is scarce. As anticipated, the performance of transfer learning improves as more data from the subject is made available. We also explore the performance of the intent prediction system under various sensor configurations. We identify that a combination of a thigh inertial measurement unit and load cell offers a practical and efficient choice for sensor setup. These findings underscore the potential of transfer learning as a powerful tool for enhancing intent prediction accuracy for new transfemoral amputee subjects, even under data-limited conditions.

中文翻译：

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下肢假肢中有效意图预测的迁移学习：有限数据集的策略

这封信提出了一种迁移学习方法，用于增强新型经股截肢者受试者的运动意图预测，特别是在数据稀疏的情况下。迁移学习是通过在不同数据集上训练的三个预训练模型来完成的：股骨截肢者、健全人以及两组的混合数据集。随后使用来自新的经股骨截肢者受试者的数据对每个模型进行微调。虽然使用个人用户数据进行训练和测试的主题相关模型可以实现最低的错误率，但它们需要大量的训练数据集。相比之下，我们的迁移学习方法产生的错误率相当，同时需要的数据少得多。这凸显了在数据稀缺时使用预先存在的、预先训练的特征的好处。正如预期的那样，随着来自主题的更多数据可用，迁移学习的性能会提高。我们还探讨了意图预测系统在各种传感器配置下的性能。我们发现大腿惯性测量单元和称重传感器的组合为传感器设置提供了实用且高效的选择。这些发现强调了迁移学习作为一种强大工具的潜力，即使在数据有限的条件下，也可以提高新的经股截肢者受试者意图预测的准确性。