40 research outputs found

    Virtual friend: tracking and generating natural interactive behaviours in real video

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    The aim of our research is to create a “virtual friend” i.e., a virtual character capable of responding to actions obtained from observing a real person in video in a realistic and sensible manner. In this paper, we present a novel approach for generating a variety of complex behavioural responses for a fully articulated “virtual friend” in three dimensional (3D) space. Our approach is model-based. First of all, we train a collection of dual Hidden Markov Models (HMMs) on 3D motion capture (MoCap) data representing a number of interactions between two people. Secondly, we track 3D articulated motion of a single person in ordinary 2D video. Finally, using the dual HMM, we generate a moving “virtual friend” reacting to the motion of the tracked person and place it in the original video footage. In this paper, we describe our approach in depth as well as present the results of experiments, which show that the produced behaviours are very close to those of real people

    Toward Capturing Momentary Changes of Heart Rate Variability by a Dynamic Analysis Method

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    <div><p>The analysis of heart rate variability (HRV) has been performed on long-term electrocardiography (ECG) recordings (12~24 hours) and short-term recordings (2~5 minutes), which may not capture momentary change of HRV. In this study, we present a new method to analyze the momentary HRV (mHRV). The ECG recordings were segmented into a series of overlapped HRV analysis windows with a window length of 5 minutes and different time increments. The performance of the proposed method in delineating the dynamics of momentary HRV measurement was evaluated with four commonly used time courses of HRV measures on both synthetic time series and real ECG recordings from human subjects and dogs. Our results showed that a smaller time increment could capture more dynamical information on transient changes. Considering a too short increment such as 10 s would cause the indented time courses of the four measures, a 1-min time increment (4-min overlapping) was suggested in the analysis of mHRV in the study. ECG recordings from human subjects and dogs were used to further assess the effectiveness of the proposed method. The pilot study demonstrated that the proposed analysis of mHRV could provide more accurate assessment of the dynamical changes in cardiac activity than the conventional measures of HRV (without time overlapping). The proposed method may provide an efficient means in delineating the dynamics of momentary HRV and it would be worthy performing more investigations.</p></div

    Time course of the SPLF on human RR-interval series with1-min increment (red solid line) and 5-min increment (green dotted line).

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    <p>M and N labelled on the signal represent Meditation and Non-meditation state, respectively. It can be seen that 1-min-increment-based HRV analysis measure gives an earlier responses to the changes of meditation states than 5-min-increment-based measure.</p

    Toward Capturing Momentary Changes of Heart Rate Variability by a Dynamic Analysis Method

    No full text
    <div><p>The analysis of heart rate variability (HRV) has been performed on long-term electrocardiography (ECG) recordings (12~24 hours) and short-term recordings (2~5 minutes), which may not capture momentary change of HRV. In this study, we present a new method to analyze the momentary HRV (mHRV). The ECG recordings were segmented into a series of overlapped HRV analysis windows with a window length of 5 minutes and different time increments. The performance of the proposed method in delineating the dynamics of momentary HRV measurement was evaluated with four commonly used time courses of HRV measures on both synthetic time series and real ECG recordings from human subjects and dogs. Our results showed that a smaller time increment could capture more dynamical information on transient changes. Considering a too short increment such as 10 s would cause the indented time courses of the four measures, a 1-min time increment (4-min overlapping) was suggested in the analysis of mHRV in the study. ECG recordings from human subjects and dogs were used to further assess the effectiveness of the proposed method. The pilot study demonstrated that the proposed analysis of mHRV could provide more accurate assessment of the dynamical changes in cardiac activity than the conventional measures of HRV (without time overlapping). The proposed method may provide an efficient means in delineating the dynamics of momentary HRV and it would be worthy performing more investigations.</p></div

    Time course of SPLF components derived from signal segments created by sliding window with six time increments.

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    <p>With the time increment increasing, the time courses of the measure become smoother, resulting in losing the dynamical information. When increasing the time increment to 5 min, the measure could not provide any useful information about the temporal dynamics in the representative synthetic signal.</p

    Time courses of measures of HRV analysis on ECG data from a dog with1-min increment (red solid line) and 5-min increment (green dotted line).

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    <p>Four rows from top to bottom correspond to the SDNN (standard deviation of the normal-to-normal intervals), RMSSD (square root of the mean squared differences of successive intervals), SPLF (power spectrum in low frequency), and SPHF (power spectrum in high frequency) components. Following the application of atropine, all the four measures of HRV analysis with 1-min increment instantly decreased and reached their minimum values within about seven minutes, and then gradually increased. With a 5-min increment, the four measures began to decrease about three minutes later after giving atropine.</p

    Schematic diagram of the difference between the traditional method and the proposed method with a 1 min time increment.

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    <p>(a) Traditional method of monitoring temporal dynamics of HRV: HRV analysis was performed in each neighbor analysis window, which means the time interval between two analyzed results is 5min, i.e. we can only observe the status changes taking place in the second window at the 10min time point. (b) The proposed overlapping window method of monitoring temporal dynamics of HRV, HRV analysis is performed in each overlapped analysis window.</p

    Time course of RMSSD calculated from signal segments created by sliding window with six time increments.

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    <p>With the time increment increasing, the time courses of the measure become smoother, resulting in losing the dynamical information. When increasing the time increment to 5min, the measure could not provide any information about the temporal dynamics in the representative synthetic signal.</p

    Time course of the SPLF on human RR-interval series with1-min increment (red solid line) and 5-min increment (green dotted line).

    No full text
    <p>M and N labelled on the signal represent Meditation and Non-meditation state, respectively. It can be seen that a 5-min-increment-based measure was hardly to capture the happenings of 2-min non-meditation state among the meditation states, 1-min-increment-based measure could clearly catch the short-time changes of meditation state.</p
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