Comparison of Surface Models and Skeletal Models for Inertial Sensor Data Synthesis

We present a modelling and simulation framework to synthesise body-worn inertial sensor data based on personalised human body surface and biomechanical models. Anthropometric data and reference images were used to create personalised body surface mesh models. The mesh armature was aligned using motion capture reference pose and afterwards mesh and armature were parented. In addition, skeletal models were created using an established musculoskeletal dynamic modelling framework. Four activities of daily living (ADL), including upper and lower limbs were simulated with surface and skeletal models using motion capture data as stimuli. Acceleration and angular velocity data were simulated for 12 body areas of surface models and 8 body areas of skeletal models. We compared simulated inertial sensor data of both models against physical IMU measurements that were obtained simultaneously with video motion capture. Results showed average errors of 27 degrees/s vs. 31 degrees/s and 1:7 m/s(2) vs. 3:3 m/s(2) for surface and skeletal models, respectively. Mean correlation coefficients of body surface models ranged between 0:2 - 0:9 for simulated angular velocity and between 0:1 - 0:8 for simulated acceleration when compared to physical IMU data. The proposed surface modelling consistently showed similar or lower error compared to established skeletal modelling across ADLs and study participants. Body surface models can offer a more realistic representation compared to skeletal models for simulation-based analysis and optimisation of wearable inertial sensor systems. Index Terms-Accelerometer, gyroscope,

Automated summary

This study presents a framework for synthesising body-worn inertial sensor data based on personalised body surface and biomechanical models. By aligning mesh armatures and creating skeletal models, acceleration and angular velocity data were simulated for daily activities and compared against physical IMU measurements, showing lower errors in surface models.