Head Rotation Estimation Algorithm for Hand-Free Computer Interaction

A high accuracy rotation angle estimation algorithm based on Local Upsampling Fourier Transform (LUFT) is developed in this paper. The LUFT uses a hierarchical strategy to estimate the rotation, which consists of a transformation of rotation to translation, a fast coarse rotation estimation and a robust refinement stage as well. The coarse rotation is estimated through the conventional Phase Only Correlation (POC), then, it is refined by the resampling technique within a local neighborhood in frequency domain. Furthermore, as will be shown in many experiments, the LUFT can achieve high accuracy rotation estimation, where the accuracy is tunable to some extent. Specially, it is efficient and robust to noise.

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Continuous Distant Measurement of the User’s Heart Rate in Human-Computer Interaction Applications

Sensors ◽

10.3390/s19194205 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4205 ◽

Cited By ~ 1

Author(s):

Przybyło

Keyword(s):

Heart Rate ◽

Human Computer Interaction ◽

Pulse Signal ◽

Estimation Algorithm ◽

Time Domain Analysis ◽

Extraction Methods ◽

Signal Extraction ◽

Power Spectral ◽

The Impact ◽

Computer Interaction

In real world scenarios, the task of estimating heart rate (HR) using video plethysmography (VPG) methods is difficult because many factors could contaminate the pulse signal (i.e. a subjects’ movement, illumination changes). This article presents the evaluation of a VPG system designed for continuous monitoring of the user's heart rate during typical human-computer interaction scenarios. The impact of human activities while working at the computer (i.e. reading and writing text, playing a game) on the accuracy of HR VPG measurements was examined. Three commonly used signal extraction methods were evaluated: green (G), green-red difference (GRD), blind source separation (ICA). A new method based on an excess green (ExG) image representation was proposed. Three algorithms for estimating pulse rate were used: power spectral density (PSD), autoregressive modeling (AR) and time domain analysis (TIME). In summary, depending on the scenario being studied, different combinations of signal extraction methods and the pulse estimation algorithm ensure optimal heart rate detection results. The best results were obtained for the ICA method: average RMSE = 6.1 bpm (beats per minute). The proposed ExG signal representation outperforms other methods except ICA (RMSE = 11.2 bpm compared to 14.4 bpm for G and 13.0 bmp for GRD). ExG also is the best method in terms of proposed success rate metric (sRate).

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