Ambulatory Assessment of the Dynamic Margin of Stability Using an Inertial Sensor Network

Abstract

Loss of stability is a precursor to falling and therefore represents a leading cause of injury, especially in fragile people. Thus, dynamic stability during activities of daily living (ADLs) needs to be considered to assess balance control and fall risk. The dynamic margin of stability (MOS) is often used as an indicator of how the body center of mass is located and moves relative to the base of support. In this work, we propose a magneto-inertial measurement unit (MIMU)-based method to assess the MOS of a gait. Six young healthy subjects were asked to walk on a treadmill at different velocities while wearing MIMUs on their lower limbs and pelvis. We then assessed the MOS by computing the lower body displacement with respect to the leading inverse kinematics approach. The results were compared with those obtained using a camera-based system in terms of root mean square deviation (RMSD) and correlation coefficient (ρ). We obtained a RMSD of ≤1.80 cm and ρ ≥ 0.85 for each walking velocity. The findings revealed that our method is comparable to camera-based systems in terms of accuracy, suggesting that it may represent a strategy to assess stability during ADLs in unstructured environments.

Estimation of Human Center of Mass Position through the Inertial Sensors-Based Methods in Postural Tasks: An Accuracy Evaluation

“…Overall, the mean RMSE was always lower than 26 mm for the BM, and 85 mm for the SDI. Considering the first method, our results were comparable to those obtained by Guaitolini et al [ 41 ] during treadmill gait, using an inertial sensor network composed of 7 IMUs, or Najafi et al [ 27 ], using two or three sensors to estimate the trajectory of the CoM. More specifically, our methodology overcomes the limitation of the work of Guaitolini et al [ 41 ], as in our study, the anthropometrical measures were taken by means of a measuring tape, instead of through the OS system.…”

“…Considering the first method, our results were comparable to those obtained by Guaitolini et al [ 41 ] during treadmill gait, using an inertial sensor network composed of 7 IMUs, or Najafi et al [ 27 ], using two or three sensors to estimate the trajectory of the CoM. More specifically, our methodology overcomes the limitation of the work of Guaitolini et al [ 41 ], as in our study, the anthropometrical measures were taken by means of a measuring tape, instead of through the OS system. This, in addition to the use of a functional calibration procedure for the sensor-to-segment rotation estimation, allowed us to present and validate a procedure that is completely performable out of the laboratory setting.…”

“…The first method concerned the development of a Biomechanical Model (BM) composed of nine body segments based on an inertial sensors network of seven MIMU [ 41 ]. Similar to [ 47 , 48 ], a kinematic chain composed of nine inertial body segments was adopted for the BM.…”

“…Recently, a biomechanical model based on the lower limb kinematic chain was developed by Guaitolini et al in 2019 [ 41 ]. In this study, the inertial sensor network approach was used for the ambulatory assessment of human CoM trajectory, with respect to the foot, in the stance phase, during walking tasks.…”

“…Despite the high interest in the use of wearable sensors for the measurements of human CoM dynamics, in most literature cases, the CoM assessment with inertial sensors was computed during dynamic tasks such as walking, running, or skiing, in which the CoM dynamics assumed large fluctuations in the 3D space [ 6 , 34 , 35 , 37 , 38 , 41 , 42 ]. Only a few studies verified the accuracy of IMU-based methods for the measurement of CoM trajectories during standing tasks [ 43 , 44 ].…”