In the control of lower-limb exoskeletons with feet, the phase in the gait cycle can be identified by monitoring the weight distribution at the feet. This phase information can be used in the exoskeleton's controller to compensate the dynamics of the exoskeleton and to assign impedance parameters. Typically the weight distribution is calculated using data from sensors such as treadmill force plates or insole force sensors. However, these solutions increase both the setup complexity and cost. For this reason, we propose a deep-learning approach that uses a short time window of joint kinematics to predict the weight distribution of an exoskeleton in real time. The model was trained on treadmill walking data from six users wearing a four-degree-of-freedom exoskeleton and tested in real time on three different users wearing the same device. This test set includes two users not present in the training set to demonstrate the model's ability to generalize across individuals. Results show that the proposed method is able to fit the actual weight distribution with R2=0.9 and is suitable for real-time control with prediction times less than 1 ms. Experiments in closed-loop exoskeleton control show that deep-learning-based weight distribution estimation can be used to replace force sensors in overground and treadmill walking.

翻译：在配备足部传感器的下肢外骨骼控制中，步态周期相位可通过监测足底负重分布进行识别。该相位信息可用于外骨骼控制器，以补偿外骨骼动力学特性并配置阻抗参数。传统方法通常通过跑步机测力台或鞋垫式力传感器数据计算负重分布，但这些方案会增加系统复杂度与成本。为此，我们提出一种深度学习方法，利用关节运动学的短时窗口数据实时预测外骨骼负重分布。该模型基于六名穿戴四自由度外骨骼受试者的跑步机行走数据进行训练，并在三名穿戴相同设备的不同受试者身上进行实时测试。测试集包含两名未参与训练的用户，以验证模型跨个体泛化能力。结果表明：所提方法能够以R2=0.9的精度拟合实际负重分布，且预测时间小于1毫秒，满足实时控制需求。闭环外骨骼控制实验表明，基于深度学习的负重分布估计可替代力传感器，适用于地面行走与跑步机行走场景。