WIMUSim: simulating realistic variabilities in wearable IMUs for human activity recognition

Physics simulation has emerged as a promising approach to generate virtual Inertial Measurement Unit (IMU) data, offering a solution to reduce the extensive cost and effort of real-world data collection. However, the fidelity of virtual IMU depends heavily on the quality of the source motion data, which varies with motion capture setups. We hypothesize that improving virtual IMU fidelity is crucial to fully harness the potential of physics simulation for virtual IMU data generation in training Human Activity Recognition (HAR) models. To investigate this, we introduce WIMUSim, a 6-axis wearable IMU simulation framework designed to accurately parameterize real IMU properties when deployed on people. WIMUSim models IMUs in wearable sensing using four key parameters: Body (skeletal model), Dynamics (movement patterns), Placement (device positioning), and Hardware (IMU characteristics). Using these parameters, WIMUSim simulates virtual IMU through differentiable vector manipulations and quaternion rotations. A key novelty enabled by this approach is the identification of WIMUSim parameters using recorded real IMU data through gradient descent-based optimization, starting from an initial estimate. This process enhances the fidelity of the virtual IMU by optimizing the parameters to closely mimic the recorded IMU data. Adjusting these identified parameters allows us to introduce physically plausible variabilities. Our fidelity assessment demonstrates that WIMUSim accurately replicates real IMU data with optimized parameters and realistically simulates changes in sensor placement. Evaluations using exercise and locomotion activity datasets confirm that models trained with optimized virtual IMU data perform comparably to those trained with real IMU data. Moreover, we demonstrate the use of WIMUSim for data augmentation through two approaches: Comprehensive Parameter Mixing, which enhances data diversity by varying parameter combinations across subjects, outperforming models trained with real and non-optimized virtual IMU data by 4 to 10 percentage points (pp); and Personalized Dataset Generation, which customizes augmented datasets to individual user profiles, resulting in average accuracy improvements of 4 pp, with gains exceeding 10 pp for certain subjects. These results underscore the benefit of high-fidelity virtual IMU data and WIMUSim's utility in developing effective data generation strategies, alleviating the challenge of data scarcity in sensor-based HAR.