Indirect measurement of anterior-posterior ground reaction forces using a minimal set of wearable inertial sensors: From healthy to hemiparetic walking

Abstract

<jats:title>Abstract</jats:title> <jats:p>Background: The anterior-posterior ground reaction force (AP-GRF) and propulsion and braking metrics derived from the AP-GRF time series are indicators of locomotor function across healthy and neurological diagnostic groups. In this paper, we describe the use of a minimal set of wearable inertial measurement units (IMUs) to indirectly measure the AP-GRFs generated during healthy and hemiparetic walking. Methods: Ten healthy individuals and five individuals with chronic post-stroke hemiparesis completed a 6-minute walk test over a walking track instrumented with six forceplates while wearing three IMUs securely attached to the pelvis, thigh, and shank. Subject-specific models driven by IMU-measured thigh and shank angles and an estimate of body acceleration provided by the pelvis IMU were used to generate indirect estimates of the AP-GRF time series. Propulsion and braking point metrics (i.e., peaks, peak timings, and impulses) were extracted from the IMU-generated time series. Peaks and impulses were expressed as % bodyweight (%bw) and peak timing was expressed as % stance phase (%sp). A 75%-25% split of 6-minute walk test data was used to train and validate the models. Indirect estimates of the AP-GRF time series and point metrics were compared to direct measurements of the same made by the reference standard forceplates. Results: Indirect measurements of the AP-GRF time series strongly approximated the direct measurements made by forceplates, with low error and high consistency in both the healthy (RMSE = 4.5 %bw; R2 = 0.93) and post-stroke (RMSE = 2.65 %bw; R2 = 0.90) cohorts. In the healthy cohort, the average errors between indirect and direct measurements of the peak propulsion magnitude, peak propulsion timing, and propulsion impulse point estimates were 2.37 %bw, 0.67 %sp, and 0.43 %bw. In the post-stroke cohort, the average errors for these same point estimates were 1.07 %bw, 1.27 %sp, and 0.31 %bw. Average errors for the braking-related point estimates were higher, but comparable. Conclusions: Accurate estimates of the AP-GRF time series and key propulsion and braking point metrics can be generated using three strategically mounted IMUs and subject-specific calibrations. This study is a foundational step toward the development of point-of-care diagnostic systems that can catalyze the routine assessment and management of propulsion and braking locomotor deficits during rehabilitation.</jats:p>

Moving on: measuring movement remotely after stroke

“…The advantage of this approach is that it allows us to extract data from sensors that are not directly measured by them. For instance, using machine learning models applied to sparse IMUs, we can extract 3D ground reaction forces (Ancillao et al, 2018;Revi et al, 2020;Wouda et al, 2018a), full body joint kinematics (Weygers et al, 2020;Wouda et al, 2018a), CoM velocity (Sabatini and Mannini, 2016), Centre of Pressure (CoP) trajectory (Podobnik et al, 2020) etc. A drawback of this method is that the machine model cannot be related to an underlying physical model of motion.…”

“…Note that here, the GRF are the sum of all forces acting on the body, which is the sum of reactive forces at both feet, provided no additional contact with the environment. As Inertial Measurement Units (IMUs) measure accelerations of the segment they are attached to, the GRF acting on the body can be estimated either using a biomechanical model (Chapter VI) (Karatsidis et al, 2016) or machine learning techniques (Ancillao et al, 2018;Komaris et al, 2019;Revi et al, 2020). Ancillao and colleagues (Ancillao et al, 2018) summarize several of these methods and find that they either estimate only the vertical GRF using a minimal setup or estimate the shear forces using machine learning methods or an array of several IMUs.…”