Abstract:We hypothesize that our fingertip image-based heart rate detection methods using smartphone reliably detect the heart rhythm and rate of subjects. We propose fingertip curve line movement-based and fingertip image intensity-based detection methods, which both use the movement of successive fingertip images obtained from smartphone cameras. To investigate the performance of the proposed methods, heart rhythm and rate of the proposed methods are compared to those of the conventional method, which is based on average image pixel intensity. Using a smartphone, we collected 120 s pulsatile time series data from each recruited subject. The results show that the proposed fingertip curve line movement-based method detects heart rate with a maximum deviation of 0.0832 Hz and 0.124 Hz using time-and frequency-domain based estimation, respectively, compared to the conventional method. Moreover, another proposed fingertip image intensity-based method detects heart rate with a maximum deviation of 0.125 Hz and 0.03 Hz using time-and frequency-based estimation, respectively.
The advent of smartphones has advanced the use of embedded sensors to acquire various physiological information. For example, smartphone camera sensors and accelerometers can provide heart rhythm signals to the subjects, while microphones can give respiratory signals. However, the acquired smartphone-based physiological signals are more vulnerable to motion and noise artifacts (MNAs) compared to using medical devices, since subjects need to hold the smartphone with proper contact to the smartphone camera and lens stably and tightly for a duration of time without any movement in the hand or finger. This results in more MNA than traditional methods, such as placing a finger inside a tightly enclosed pulse oximeter to get PPG signals, which provides stable contact between the sensor and the subject’s finger. Moreover, a smartphone lens does not block ambient light in an effective way, while pulse oximeters are designed to block the ambient light effectively. In this paper, we propose a novel diversity method for smartphone signals that reduces the effect of MNAs during heart rhythm signal detection by 1) acquiring two heterogeneous signals from a color intensity signal and a fingertip movement signal, and 2) selecting the less MNA-corrupted signal of the two signals. The proposed method has advantages in that 1) diversity gain can be obtained from the two heterogeneous signals when one signal is clean while the other signal is corrupted, and 2) acquisition of the two heterogeneous signals does not double the acquisition procedure but maintains a single acquisition procedure, since two heterogeneous signals can be obtained from a single smartphone camera recording. In our diversity method, we propose to choose the better signal based on the signal quality indices (SQIs), i.e., standard deviation of instantaneous heart rate ( STD – HR ), root mean square of the successive differences of peak-to-peak time intervals ( RMSSD – T ), and standard deviation of peak values ( STD – PV ). As a performance metric evaluating the proposed diversity method, the ratio of usable period is considered. Experimental results show that our diversity method increases the usable period 19.53% and 6.25% compared to the color intensity or the fingertip movement signals only, respectively.
This paper examines the performance of an HDP-type (adaptive critic design (ACD) of the game Go. The game Go is an ideal problem domain for exploring machine learning; it has simple rules but requires complex strategies to play well. All current commercial Go 2programs are knowledge based implementations; they utilize input feature and pattern matching along with minimax type search techniques. But the extremely high branching factor puts a limit on their capabilities, and they are very weak compared to the relative strengths of other game program like chess. In this paper, the Goplaying ACD consists of a critic nerwork and an action network. The HDP type critic network learns to predict cumulative utility function of the current board position ,from training games, and, the action network chooses a next move which maximizes critics' next step cost-to-go. After about 6000 different training games against a public domain program, WALLY 111, the network (playing WHITE) began to win in some of the games and showed slow but steady improvements on test games.
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