Hochreiter Dominik, Schmermbeck Katharina, Vazquez-Pufleau Miguel, Ferscha Alois
Institute of Pervasive Computing, Johannes Kepler University, 4040 Linz, Austria.
Chair of Production Technology, Institute of Mechatronics, University of Innsbruck, 6020 Innsbruck, Austria.
Sensors (Basel). 2025 Aug 22;25(17):5225. doi: 10.3390/s25175225.
Intention prediction is essential for enabling intuitive and adaptive control in upper-limb exoskeletons, especially in dynamic industrial environments. However, the suitability of different cues, sensors, and computational models for real-world industrial applications remains unclear. This systematic review, conducted according to PRISMA guidelines, analyzes 29 studies published between 2007 and 2024 that investigate intention prediction in active exoskeletons. Most studies rely on motion capture (14) and electromyography (14) to estimate joint torque or trajectories, predicting from 450 ms before to 660 ms after motion onset. Approaches include model-based and model-free regression, as well as classification methods, but vary significantly in complexity, sensor setups, and evaluation procedures. Only a subset evaluates usability or support effectiveness, often under laboratory conditions with small, non-representative participant groups. Based on these insights, we outline recommendations for robust and adaptable intention prediction tailored to industrial task requirements. We propose four generalized support modes to guide sensor selection and control strategies in practical applications. Future research should leverage wearable sensors, integrate cognitive and contextual cues, and adopt transfer learning, federated learning, or LLM-based feedback mechanisms. Additionally, studies should prioritize real-world validation, diverse participant samples, and comprehensive evaluation metrics to support scalable, acceptable deployment of exoskeletons in industrial settings.
意图预测对于实现上肢外骨骼的直观和自适应控制至关重要，尤其是在动态工业环境中。然而，不同的线索、传感器和计算模型在实际工业应用中的适用性仍不明确。本系统综述根据PRISMA指南进行，分析了2007年至2024年间发表的29项研究，这些研究探讨了主动外骨骼中的意图预测。大多数研究依赖于运动捕捉（14项）和肌电图（14项）来估计关节扭矩或轨迹，预测从运动开始前450毫秒到运动开始后660毫秒。方法包括基于模型和无模型的回归以及分类方法，但在复杂性、传感器设置和评估程序方面差异很大。只有一小部分研究评估了可用性或支持效果，而且通常是在实验室条件下，针对规模较小、缺乏代表性的参与者群体进行的。基于这些见解，我们概述了针对工业任务要求的稳健且适应性强的意图预测建议。我们提出了四种通用的支持模式，以指导实际应用中的传感器选择和控制策略。未来的研究应利用可穿戴传感器，整合认知和情境线索，并采用迁移学习、联邦学习或基于大语言模型的反馈机制。此外，研究应优先进行实际验证、多样化的参与者样本和综合评估指标，以支持外骨骼在工业环境中的可扩展、可接受的部署。
