The deployment of autonomous virtual avatars (in extended reality) and robots in human group activities -- such as rehabilitation therapy, sports, and manufacturing -- is expected to increase as these technologies become more pervasive. Designing cognitive architectures and control strategies to drive these agents requires realistic models of human motion. Furthermore, recent research has shown that each person exhibits a unique velocity signature, highlighting how individual motor behaviors are both rich in variability and internally consistent. However, existing models only provide simplified descriptions of human motor behavior, hindering the development of effective cognitive architectures. In this work, we first show that motion amplitude provides a valid and complementary characterization of individual motor signatures. Then, we propose a fully data-driven approach, based on long short-term memory neural networks, to generate original motion that captures the unique features of specific individuals. We validate the architecture using real human data from participants performing spontaneous oscillatory motion. Extensive analyses show that state-of-the-art Kuramoto-like models fail to replicate individual motor signatures, whereas our model accurately reproduces the velocity distribution and amplitude envelopes of the individual it was trained on, while remaining distinct from others.

翻译：随着自主虚拟化身（在扩展现实中）和机器人在人类群体活动（如康复治疗、体育和制造业）中的部署日益普及，驱动这些智能体的认知架构与控制策略设计亟需真实的人类运动模型。此外，近期研究表明，每个人展现出独特的速度特征，这凸显了个体运动行为既具有丰富的变异性又保持内在一致性。然而，现有模型仅提供对人类运动行为的简化描述，阻碍了有效认知架构的发展。本研究首先证明运动幅度为个体运动特征提供了有效且互补的表征维度。随后，我们提出一种基于长短期记忆神经网络的完全数据驱动方法，用于生成能捕捉特定个体独有特征的原生运动轨迹。我们利用参与者执行自发振荡运动的真实人类数据对该架构进行验证。大量分析表明，当前最先进的类Kuramoto模型无法复现个体运动特征，而我们的模型能准确再现训练个体的速度分布与幅度包络特征，同时保持与其他个体的差异性。