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

Abstract

AbstractA 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.Author summaryWe aim to analyse walking and running in an environment that is as normal as possible. To do so, we should use sensor setups that can be worn in any environment, and that are the least bothersome to the wearer, and that ensure their privacy. Inertial sensors are small sensors that measure accelerations and velocities during movements that can be used in any environment, while also they do not record privacy-sensitive information as video cameras would. To also ensure that they are the least bothersome, while still being accurate, we investigated the accuracy of walking and running variables of different sparse sensor setups, i.e., sensor setups that do not have sensors on each body part of interest. We compared these setups to a full sensor setup, with sensors on all body parts of interest. All sensor setups included sensors measuring the feet, because those can be attached to the shoes instead of on the body, as would be required for sensors measuring the thighs or shanks. We show that movement analyses can be as accurate as with the full sensor setup using only three sensors, two on the feet and one on the pelvis. We also found that any sensor setup with no pelvis sensor led to inaccurate analysis of the trunk.