Comparing sparse inertial sensor setups for sagittal-plane walking and running reconstructions

Abstract

A sensor setup with a small number of sensors, i.e., a sparse sensor setup, could enable a comprehensive unobtrusive motion analysis using inertial motion capture (IMC) with little obtrusion to the user, which is critical for clinical and sports applications, such as assessing disease, providing biofeedback in rehabilitation, or enhancing performance in sports. When sparse sensor setups were used, the motion analysis was either not comprehensive or physical correctness was not considered. Furthermore, the relationship between sensor setup and the resulting accuracy of spatiotemporal, kinematic, or kinetic variables has not been investigated systematically. Therefore, we investigated the accuracy of biomechanical gait reconstructions from different sparse inertial sensor setups, in which the number of sensors is reduced by not placing sensors on all body segments of interest. We created biomechanical reconstructions by solving optimal control problems with a sagittal plane musculoskeletal model. The resulting simulations tracked raw accelerometer and gyroscope data while minimizing muscular effort. We applied this approach to six different sparse sensor setups to investigate the reconstruction quality of spatiotemporal, kinematic, and kinetic variables for each of these setups at three walking and three running speeds. We found that correlations between IMC and optical motion capture (OMC) were large for all sensor setups, but that those setups without a pelvis sensor led to a forward trunk lean that was too high, as well as unrealistic kinetics and kinematics. Spatiotemporal variable estimations generally also benefitted from a pelvis sensor, but not as much as the kinetic and kinematic estimations. We found that for setups including a pelvis sensor, there was no clear benefit for including additional sensors. While walking speed, step length, and joint kinematics generally benefited from using more sensors, the hip moment and knee moment did not. During running, the ankle moment and vertical ground reaction force were even estimated more accurately with smaller sensor setups. Therefore, we conclude that sagittal-plane reconstruction of walking and running kinetics and kinematics using sparse sensor setups can improve usability at the cost of only minor reductions in accuracy. When using a sparse sensor setup for gait reconstructions using optimal control, we advise to always include a pelvis sensor to achieve accurate results.

Where to mount the IMU? Validation of joint angle kinematics and sensor selection for activities of daily living

“…Published literature focused on reducing the sensor count by applying kinematic chain, doublependulum, and statistical shape models (Salarian et al, 2013;Hu et al, 2015;Marcard et al, 2017). Additionally, optimal control approaches were proposed (Dorschky et al, 2023) to investigate sparse IMU sensor sets. However, a systematic quantitative analysis of how adding or removing sensors affects performance in the context of ADLs is missing.…”