  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.
