Sensorized insoles provide a tool for gait studies and health monitoring during daily life. For users to accept such insoles they need to be comfortable and lightweight. Previous work has already demonstrated that estimation of ground reaction forces (GRFs) is possible with insoles. However, these are often assemblies of commercial components restricting design freedom and customization. Within this work, we investigate using four 3D-printed soft foam-like sensors to sensorize an insole. These sensors were combined with system identification of Hammerstein-Wiener models to estimate the 3D GRFs, which were compared to values from an instrumented treadmill as the golden standard. It was observed that the four sensors behaved in line with the expected change in pressure distribution during the gait cycle. In addition, the identified (personalized) Hammerstein-Wiener models showed the best estimation performance (on average RMS error 9.3%, R^2=0.85 and mean absolute error (MAE) 7%) of the vertical, mediolateral, and anteroposterior GRFs. Thereby showing that these sensors can estimate the resulting 3D force reasonably well. These results for nine participants were comparable to or outperformed other works that used commercial FSRs with machine learning. The identified models did decrease in estimation performance over time but stayed on average 11.35% RMS and 8.6% MAE after a week with the Hammerstein-Wiener model seeming consistent between days two and seven. These results show promise for using 3D-printed soft piezoresistive foam-like sensors with system identification to be a viable approach for applications that require softness, lightweight, and customization such as wearable (force) sensors.

翻译：传感化鞋垫为步态研究和日常生活健康监测提供了工具。为使用户接受此类鞋垫，其需具备舒适性与轻量化特性。先前研究已证明利用鞋垫估计地面反作用力具有可行性，但现有方案多采用商业组件组装，限制了设计自由度和定制化空间。本研究探索使用四个3D打印柔性泡沫状传感器实现鞋垫传感化。通过结合Hammerstein-Wiener模型的系统辨识方法，对三维地面反作用力进行估计，并以仪器化跑步机测量值作为金标准进行对比。观测发现四个传感器的响应特性符合步态周期中压力分布的预期变化规律。经个性化辨识的Hammerstein-Wiener模型在垂直方向、内外侧方向及前后方向的地面反作用力估计中表现出最优性能（平均均方根误差9.3%、决定系数R^2=0.85、平均绝对误差7%），表明该传感器系统能较好估计三维合力。九名参与者的实验结果与采用商业FSR传感器结合机器学习的研究相比具有可比性或更优性能。虽然辨识模型的估计性能随时间有所衰减，但一周后仍保持平均11.35%均方根误差和8.6%平均绝对误差，且Hammerstein-Wiener模型在第二至第七日间呈现稳定性。这些结果表明，采用3D打印柔性压阻泡沫状传感器结合系统辨识的方法，在需要柔软性、轻量化及定制化的可穿戴（力）传感应用领域具有可行前景。