A Novel Simplified System to Estimate Lower-Limb Joint Moments during Sit-to-Stand

Abstract

To provide effective diagnosis and rehabilitation, the evaluation of joint moments during sit-to-stand is essential. The conventional systems for the evaluation, which use motion capture cameras, are quite accurate. However, the systems are not widely used in clinics due to their high cost, inconvenience, and the fact they require lots of space. To solve these problems, some studies have attempted to use inertial sensors only, but they were still inconvenient and inaccurate with asymmetric weight-bearing. We propose a novel joint moment estimation system that can evaluate both symmetric and asymmetric sit-to-stands. To make a simplified system, the proposal is based on a kinematic model that estimates segment angles using a single inertial sensor attached to the shank and a force plate. The system was evaluated with 16 healthy people through symmetric and asymmetric weight-bearing sit-to-stand. The results showed that the proposed system (1) has good accuracy in estimating joint moments (root mean square error < 0.110 Nm/kg) with high correlation (correlation coefficient > 0.99) and (2) is clinically relevant due to its simplicity and applicability of asymmetric sit-to-stand.

Inertial Motion Capture-Based Wearable Systems for Estimation of Joint Kinetics: A Systematic Review

“…Among them, two studies additionally performed running turns [ 57 , 60 ]. Other activities analyzed included lifting and carrying loads [ 61 , 62 , 63 , 64 , 65 , 66 , 67 ], sit-to-stand [ 55 , 58 , 68 , 69 , 70 , 71 ], stair ascent and descent [ 55 , 58 , 70 , 72 ], standing [ 58 , 73 , 74 ], and trunk bending [ 75 ]. In addition, four studies conducted a kinetic analysis of winter sports, including snowboarding [ 76 ], ski jumping, [ 77 ], skiing [ 78 ], and skating [ 79 ].…”

“…In 24 studies, joint kinetics were estimated using bottom-up inverse dynamics, which propagates force and moment upward from the foot, the farthest extremity of the lower limb, and the external forces (i.e., GRF and GRM) required for this were measured directly through force sensors or predicted from IMU data. Faber et al [ 61 ] and Hwang et al [ 71 ] measured GRF from FPs instrumented on the floor to estimate the moment of the L5/S1 or lower body joint, in a lifting task and a sit-to-stand task, respectively. Eight studies measured the GRF and GRM by attaching MFPs developed or commercially available to the outsole of a shoe to estimate the joint kinetics in different motions, including walking [ 32 , 33 , 35 , 36 , 37 , 55 , 65 , 76 ].…”

Extended Application of Inertial Measurement Units in Biomechanics: From Activity Recognition to Force Estimation

“…Numerical methods utilized the acceleration and angular velocity collected from IMUs to calculate the orientation, velocity, and position of human limbs [78,79]. Biomechanics models were then implemented to estimate high-level parameters for HAR and musculoskeletal force estimation [80][81][82].…”

“…To determine joint forces and moments with IMUs, various biomechanical models, including segmented 2D/3D models, were established for kinematics and kinetics calculations [80,81,[116][117][118]. However, most of the biomechanical model-based methods require the placement of IMUs onto each segment of the lower limb, making them too cumbersome for application in daily continuous monitoring.…”