This study aims to improve the accuracy of motor rotational speed estimation in Mini 4WD AI using acoustic signals. Conventional frequency interpolation methods based on a Hanning window can enhance estimation accuracy; however, their performance degrades under certain spectral conditions due to spectral leakage. To address this issue, we first clarify the conditions under which estimation accuracy deteriorates. We then propose a method that combines dual-side frequency interpolation with an adaptive selection mechanism based on a regression model. Extensive simulations and real-time experiments demonstrate that the proposed method improves estimation accuracy across a wide range of measurement conditions, particularly for sampling frequencies above 11050 Hz.

In human motion analysis, variability across participants and trials, as well as observational noise, are significant factors that impede the accurate extraction of motion features. Traditional techniques, such as simple time normalization or the direct application of Dynamic Time Warping (DTW) in the observation space, struggle to effectively handle these uncertainties, leading to reduced analysis accuracy. To mitigate these challenges, we present a robust framework that applies DTW to trajectories in a low-dimensional latent space produced by a Gaussian Process Dynamical Model (GPDM). By leveraging the GPDM’s probabilistic framework and mean-prediction capability, it becomes possible to extract and synchronize essential motion dynamics in the latent space while eliminating the effects of trial-to-trial variance and noise. Validation through lifting tasks demonstrated that the proposed method preserves the original motion trajectories and achieves stable temporal alignment even under conditions with intense additive noise, effectively suppressing the matching disruptions typically observed when applying DTW in the observation space. Joint-angle synergy analysis of aligned trajectories further confirmed that essential synergies can be consistently extracted, regardless of trial uncertainties.