Adaptive Assistance with an Active and Soft Back-Support Exosuit to Unknown External Loads via Model-Based Estimates of Internal Lumbosacral Moments

State of the art controllers for back exoskeletons largely rely on body kinematics. This results in control strategies which cannot provide adaptive support under unknown external loads. We developed a neuromechanical model-based controller (NMBC) for a soft back exosuit, wherein assistive forces were proportional to the active component of lumbosacral joint moments, derived from real-time electromyography-driven models. The exosuit provided adaptive assistance forces with no a priori information on the external loading conditions. Across 10 participants, who stoop-lifted 5 and 15 kg boxes, our NMBC was compared to a non-adaptive virtual spring-based control(VSBC), in which exosuit forces were proportional to trunk inclination. Peak cable assistive forces were modulated across weight conditions for NMBC (5kg: 2.13 N/kg; 15kg: 2.82 N/kg) but not for VSBC (5kg: 1.92 N/kg; 15kg: 2.00 N/kg). The proposed NMBC strategy resulted in larger reduction of cumulative compression forces for 5 kg (NMBC: 18.2%; VSBC: 10.7%) and 15 kg conditions (NMBC: 21.3%; VSBC: 10.2%). Our proposed methodology may facilitate the adoption of non-hindering wearable robotics in real-life scenarios.

3D Printed Graded Porous Sensors for Soft Sensorized Insoles with Gait Phase & Ground Reaction Forces Estimation

Sensorized insoles provide a tool to perform gait studies and health monitoring during daily life. These sensorized insoles need to be comfortable and lightweight to be accepted. Previous work has already demonstrated that sensorized insoles are possible and can estimate both ground reaction force and gait cycle. However, these are often assemblies of commercial components restricting design freedom and flexibility. Within this work, we investigate the feasibility of using four 3D-printed porous (foam-like) piezoresistive sensors embedded in a commercial insole. These sensors were evaluated using 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, Hammerstein-Wiener models were identified that were capable of estimating the vertical and mediolateral ground reaction forces (GRFs). Their NRMSE fits were on average 82% and 73%, respectively. Similarly, for the averaged gait cycle the R^2 values were 0.98 and 0.99 with normalized RMS errors overall below 6%. These values were comparable with other insoles based on commercial force sensing resistors but at a significantly lower cost (over four times cheaper). Thereby indicating that our 3D-printed sensors can be an interesting option for sensorized insoles. The advantage of 3D printing these sensors is that it allows for significantly more design freedom, reduces assembly, and is cheaper. However, further research is needed to exploit this design freedom for complex sensors, estimate the anteroposterior GRF, and fully 3D print the entire insole.