To control the lower-limb exoskeleton robot effectively, it is essential to accurately recognize user status and environmental conditions. Previous studies have typically addressed these recognition challenges through independent models for each task, resulting in an inefficient model development process. In this study, we propose a Multitask learning approach that can address multiple recognition challenges simultaneously. This approach can enhance data efficiency by enabling knowledge sharing between each recognition model. We demonstrate the effectiveness of this approach using Gait phase recognition (GPR) and Terrain classification (TC) as examples, the most conventional recognition tasks in lower-limb exoskeleton robots. We first created a high-performing GPR model that achieved a Root mean square error (RMSE) value of 2.345 $\pm$ 0.08 and then utilized its knowledge-sharing backbone feature network to learn a TC model with an extremely limited dataset. Using a limited dataset for the TC model allows us to validate the data efficiency of our proposed Multitask learning approach. We compared the accuracy of the proposed TC model against other TC baseline models. The proposed model achieved 99.5 $\pm$ 0.044% accuracy with a limited dataset, outperforming other baseline models, demonstrating its effectiveness in terms of data efficiency. Future research will focus on extending the Multitask learning framework to encompass additional recognition tasks.

翻译：为有效控制下肢外骨骼机器人，准确识别用户状态与环境条件至关重要。以往研究通常针对每项识别任务采用独立模型，导致模型开发效率低下。本研究提出一种多任务学习方法，可同时处理多项识别挑战。该方法通过促进各识别模型间的知识共享，提升数据利用效率。我们以下肢外骨骼机器人中最传统的两项识别任务——步态相位识别（GPR）与地形分类（TC）为例，验证该方法的有效性。首先构建高性能GPR模型，其均方根误差（RMSE）达到2.345 ± 0.08；随后利用其知识共享骨干特征网络，在极有限数据集上训练TC模型。通过限制TC模型的数据量，验证了所提多任务学习方法的数据效率。我们将所提TC模型的准确率与其他基线TC模型进行对比，结果显示：在有限数据集下，所提模型准确率达99.5 ± 0.044%，优于其他基线模型，充分证明了其在数据效率方面的有效性。未来研究将聚焦于扩展多任务学习框架以涵盖更多识别任务。