Accurate estimation of a user's biological joint moment from wearable sensor data is vital for improving exoskeleton control during real-world locomotor tasks. However, most state-of-the-art methods rely on deep learning techniques that necessitate extensive in-lab data collection, posing challenges in acquiring sufficient data to develop robust models. To address this challenge, we introduce a locomotor task set optimization strategy designed to identify a minimal, yet representative, set of tasks that preserves model performance while significantly reducing the data collection burden. In this optimization, we performed a cluster analysis on the dimensionally reduced biomechanical features of various cyclic and non-cyclic tasks. We identified the minimal viable clusters (i.e., tasks) to train a neural network for estimating hip joint moments and evaluated its performance. Our cross-validation analysis across subjects showed that the optimized task set-based model achieved a root mean squared error of 0.30$\pm$0.05 Nm/kg. This performance was significantly better than using only cyclic tasks (p<0.05) and was comparable to using the full set of tasks. Our results demonstrate the ability to maintain model accuracy while significantly reducing the cost associated with data collection and model training. This highlights the potential for future exoskeleton designers to leverage this strategy to minimize the data requirements for deep learning-based models in wearable robot control.

翻译：从可穿戴传感器数据中准确估计用户的生物关节力矩，对于提升外骨骼在真实世界步态任务中的控制性能至关重要。然而，大多数先进方法依赖于深度学习技术，这些技术需要进行大量的实验室数据采集，这给获取足够数据以开发鲁棒模型带来了挑战。为应对这一挑战，我们提出了一种步态任务集优化策略，旨在识别一个最小化但具有代表性的任务集合，该集合能在保持模型性能的同时，显著减轻数据采集负担。在此优化过程中，我们对各种周期性和非周期性任务的降维生物力学特征进行了聚类分析。我们识别出用于训练神经网络以估计髋关节力矩的最小可行聚类（即任务），并评估了其性能。我们的跨受试者交叉验证分析表明，基于优化任务集的模型实现了0.30$\pm$0.05 Nm/kg的均方根误差。该性能显著优于仅使用周期性任务（p<0.05），且与使用完整任务集的表现相当。我们的结果表明，能够在显著降低数据采集和模型训练相关成本的同时，保持模型的准确性。这突显了未来外骨骼设计者利用此策略来最小化基于深度学习的可穿戴机器人控制模型数据需求的潜力。