Title: “Filtering Motion Capture Data for Real-Time Applications”
Abstract:
This paper presents custom-designed filters for real-time motion capture applications, specifically motion controllers for musical interaction. The authors previously developed methods for designing nearly optimal filters for real-time applications but needed to establish the typical frequency content of motion capture data to design suitable filters for their application. An experiment was conducted recording hand motion of 20 subjects to determine cutoff frequencies. Three cutoff frequencies (5, 10, and 15 Hz) were proposed for different scenarios, representing heavy, medium, and light filtering. The paper also proposes a range of real-time filters for motion controllers, focusing on low-pass filters and low-pass differentiators.
Background:
Motion capture (MoCap) and sensor technologies are widely used in real-time interactive musical applications. However, many MoCap technologies produce noisy results, necessitating noise removal filters. The paper discusses the challenges in designing digital filters for real-time applications, emphasizing the need for low latency and minimal delay.
Methods:
The authors designed custom filters for real-time motion capture applications, emphasizing hand motion for musical interaction. The methodology involved determining the frequency content of motion capture data through an experiment recording hand motions of 20 subjects. The experiment used an optical infrared marker-based MoCap system, and subjects performed two types of hand motions. The frequency spectra of these motions were analyzed to determine appropriate cutoff frequencies for the filters.
Results:
The experiment revealed that the main frequency content for controlled hand motion (Take 2) reached up to about 5-10 Hz, while rapid hand motion (Take 1) had a wider frequency distribution. Based on these results, the paper proposes cutoff frequencies of 5, 10, and 15 Hz for different filtering needs. These cutoffs correspond to heavy, medium, and light filtering, respectively.
Discussions:
The discussion focuses on the application of the proposed filters in real-time scenarios, particularly in musical interactions. The authors emphasize the balance between noise attenuation and signal distortion, and the importance of minimizing filter delay for real-time applications. They also discuss the application-specific nature of filter design and the need for a careful selection of cutoff frequencies based on the intended use.
Limitations:
The paper acknowledges that the experiment involved general and open tasks for the subjects, which might have resulted in a range of different motions. This variability could influence the determination of generic frequency properties of hand motion. Additionally, the paper notes that the optimal filter choice heavily depends on application-specific details.
Possible Applications:
The research has direct applications in designing motion controllers for musical interaction, where real-time processing with minimal delay is crucial. The proposed filters are particularly useful in scenarios requiring intimate control of music or sound through motion capture data. The findings could also be applied in other areas where real-time processing of motion capture data is essential, such as gaming or virtual reality.