From Smartphone Images to Musculoskeletal Models: Personalized Inertial Parameter Estimation
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Every human body is different, however, current movement analysis does not reflect that, as it heavily relies on generic musculoskeletal models. Usually, these models are scaled to match the participants’ body segment lengths and body weight, but not taking individual body shape into account. This can lead to errors in the estimation of joint forces and torques, which are important to accurately estimate musculoskeletal variables. Thus, we developed a method to estimate body segment inertial parameters based on smartphone pictures. From the pictures, we reconstruct the body hull, estimate the skeletal shape and pose, and then estimate the distribution of bone, lean, and fatty tissues. We then segment the body hull and assign each tissue type a density, which is used to calculate the body segment inertial parameters. These personalizated models were validated with an experiment including gait and magnetic resonance imaging measurements. We found that our method leads to a reduction of residual forces of up to 14.9 % and a reduction of metabolic cost of up to 12.8 % when compared to generic musculoskeletal models. Furthermore, the shape-based inertial parameter personalization method creates participant-specific musculoskeletal models that are closer to the MRI-derived ground truth than generic models. To allow for the use of our method with existing data, we also introduce two new generic musculoskeletal models, which are based on the average standing body shapes and show similar joint moment outcomes, but less reduction of residual forces as the personalized models.
Author summary
Our method adresses the challenge of personalizing inertial properties of musculoskeletal models. The existing state-of-the-art methods involve scaling a generic model to match the participants’ body weight and segment lengths or widths, which does not take individual body shape into account. To overcome these limitations, we developed a method to personalize musculoskeletal models based on smartphone pictures by reconstructing their body hull. We then estimate where tissue types, e.g. fat, muscle, and bone, are located in the body hull estimate inertial parameters for each segment. The personalized models were validated with an experiment including gait and magnetic resonance imaging measurements, and we found that our method leads to reduced residual forces, which is important for accurate musculoskeletal simulations. As our method leads to different joint torques when compared to generic musculoskeletal models, it can influence the interpretation of biomechanical analyses. Our method can be used whenever a participant’s body shape is known, or smartphone pictures can be taken. For retrospective studies, we also introduce two new generic musculoskeletal models, SIPP-generic-female and SIPP-generic-male , which are based on average standing body shapes.
