Ground reaction forces and moments (GRF&M) are important measures used as input in biomechanical analysis to estimate joint kinetics, which often are used to infer information for many musculoskeletal diseases. Their assessment is conventionally achieved using laboratory-based equipment that cannot be applied in daily life monitoring. In this study, we propose a method to predict GRF&M during walking, using exclusively kinematic information from fully-ambulatory inertial motion capture (IMC). From the equations of motion, we derive the total external forces and moments. Then, we solve the indeterminacy problem during double stance using a distribution algorithm based on a smooth transition assumption. The agreement between the IMC-predicted and reference GRF&M was categorized over normal walking speed as excellent for the vertical (ρ = 0.992, rRMSE = 5.3%), anterior (ρ = 0.965, rRMSE = 9.4%) and sagittal (ρ = 0.933, rRMSE = 12.4%) GRF&M components and as strong for the lateral (ρ = 0.862, rRMSE = 13.1%), frontal (ρ = 0.710, rRMSE = 29.6%), and transverse GRF&M (ρ = 0.826, rRMSE = 18.2%). Sensitivity analysis was performed on the effect of the cut-off frequency used in the filtering of the input kinematics, as well as the threshold velocities for the gait event detection algorithm. This study was the first to use only inertial motion capture to estimate 3D GRF&M during gait, providing comparable accuracy with optical motion capture prediction. This approach enables applications that require estimation of the kinetics during walking outside the gait laboratory.

Ground reaction force (GRF) measurement is important in the analysis of human body movements. The main drawback of the existing measurement systems is the restriction to a laboratory environment. This paper proposes an ambulatory system for assessing the dynamics of ankle and foot, which integrates the measurement of the GRF with the measurement of human body movement. The GRF and the center of pressure (CoP) are measured using two six-degrees-of-freedom force sensors mounted beneath the shoe. The movement of foot and lower leg is measured using three miniature inertial sensors, two rigidly attached to the shoe and one on the lower leg. The proposed system is validated using a force plate and an optical position measurement system as a reference. The results show good correspondence between both measurement systems, except for the ankle power estimation. The root mean square (RMS) difference of the magnitude of the GRF over 10 evaluated trials was (0.012 0.001) N/N (mean standard deviation), being (1.1 0.1)% of the maximal GRF magnitude. It should be noted that the forces, moments, and powers are normalized with respect to body weight. The CoP estimation using both methods shows good correspondence, as indicated by the RMS difference of (5.1 0.7) mm, corresponding to (1.7 0.3)% of the length of the shoe. The RMS difference between the magnitudes of the heel position estimates was calculated as (18 6) mm, being (1.4 0.5)% of the maximal magnitude. The ankle moment RMS difference was (0.004 0.001) Nm/N, being (2.3 0.5)% of the maximal magnitude. Finally, the RMS difference of the estimated power at the ankle was (0.02 0.005) W/N, being (14 5)% of the maximal power. This power difference is caused by an inaccurate estimation of the angular velocities using the optical reference measurement system, which is due to considering the foot as a single segment. The ambulatory system considers separate heel and forefoot segments, thus allowing an additional foot moment and power to be estimated. Based on the results of this research, it is concluded that the combination of the instrumented shoe and inertial sensing is a promising tool for the assessment of the dynamics of foot and ankle in an ambulatory setting.

The center of mass (CoM) and the center of pressure (CoP) are two variables that are crucial in assessing energy expenditure and stability of human walking. The purpose of this study is to estimate the CoM displacement continuously using an ambulatory measurement system. The measurement system consists of instrumented shoes with 6 DOF force/moment sensors beneath the heels and the forefeet. Moreover, two inertial sensors are rigidly attached to the force/moment sensors for the estimation of position and orientation. The estimation of CoM displacement is achieved by fusing low-pass filtered CoP data with high-pass filtered double integrated CoM acceleration, both estimated using the instrumented shoes. Optimal cutoff frequencies for the low-pass and high-pass filters appeared to be 0.2 Hz for the horizontal direction and 0.5 Hz for the vertical direction. The CoM estimation using this ambulatory measurement system was compared to CoM estimation using an optical reference system based on the segmental kinematics method. The rms difference of each component of the CoM displacement averaged over a hundred trials obtained from seven stroke patients was ( 0.020 +/-0.007 ) m (mean +/- standard deviation) for the forward x-direction, ( 0.013 +/-0.005) m for the lateral y-direction, and ( 0.007 +/-0.001) m for the upward z-direction. Based on the results presented in this study, it is concluded that the instrumented shoe concept allows accurate and continuous estimation of CoM displacement under ambulatory conditions.

Over the last years, inertial sensing has proven to be a suitable ambulatory alternative to traditional human motion tracking based on optical position measurement systems, which are generally restricted to a laboratory environment. Besides many advantages, a major drawback is the inherent drift caused by integration of acceleration and angular velocity to obtain position and orientation. In addition, inertial sensing cannot be used to estimate relative positions and orientations of sensors with respect to each other. In order to overcome these drawbacks, this study presents an Extended Kalman Filter for fusion of inertial and magnetic sensing that is used to estimate relative positions and orientations. In between magnetic updates, change of position and orientation are estimated using inertial sensors. The system decides to perform a magnetic update only if the estimated uncertainty associated with the relative position and orientation exceeds a predefined threshold. The filter is able to provide a stable and accurate estimation of relative position and orientation for several types of movements, as indicated by the average rms error being 0.033 m for the position and 3.6 degrees for the orientation.