Koji Oguri

Estimating heart rate using wrist-type Photoplethysmography and acceleration sensor while running

This study provides Heart Rate (HR) Estimation using wrist-type Photoplethysmogpraphy (PPG) sensor while the subject is running. We propose the algorithm to estimate heart rate for the wrist-type PPG sensor. Since body motion artifacts easily affect the arm portion, our method in this study also uses accelerometer built in the wrist-type sensor to improve the accuracy of heart rate estimation. Our method has two components. One is rejecting artifacts with the power spectrums difference between PPG and acceleration obtained by frequency analysis. The other is the reliability of heart rate estimation, defined by the acceleration. Experimental results while our test subjects were running came closer to the holter Electrocardiogram (ECG) in high accuracy (r = 0.98, SD = 8.7 bpm). We, therefore, report the heart rate estimation method which has a higher degree of usability compared to existing methods using ECG.

Driver's cognitive distraction detection using physiological features by the adaboost

Effects of drivers states adaptive driving support systems is highly expected for the prevention of traffic accidents. In order to create this constituent technology, detecting drivers psychosomatic states which occurs just before a traffic accident is essential. Therefore drivers distraction is thought as one of important factors. This study focused on detecting drivers cognitive distraction, a state which can easily lead to a traffic accident. We reproduced the cognitive distraction by imposing conversation or arithmetic loads to the subjects on a driving simulator. A stereo camera system were used as the means to track a subjects eyes, and head movements, which were set as classification features for pattern recognition on the Support Vector Machine (hereafter, SVM) basis used in the previous study of the AIDE project, a part of EU 6th Framework Programme. Diameter of pupil as well as the interval between heart R-waves (hereafter, heart rate RRI) from an ECG (electrocardiogram) were added for classification features to further improve the accuracy of drivers cognitive distraction detection. Based on this study, we established the methodology for more precise and faster drivers cognitive detection by using the AdaBoost.

A modeling method for predicting driving behavior concerning with driver’s past movements

Recently, studies of predicting driving behavior based on behavioral models have been done for constructing Driving Safety Support Systems (DSSS) responding to driverpsilas intention. Although traditional behavioral models predict future behavior by analyzing instantaneous velocity and pedal strokes, past movements should be concerned for accurate prediction since humanpsilas behavior is strongly related to past actions. This study proposed a method of modeling driving behavior concerned with certain period of past movements by using AR-HMM (Auto-Regressive Hidden Markov Model) in order to predict stop probability. As results of comparison with a conventional method, our algorithm is effective for predicting driving behavior accurately.

Driver’s cognitive distraction detection using AdaBoost on pattern recognition basis

Detecting the mental and physical states which occur in a driver immediately before a traffic accident and then providing information to or warning the driver is an effective means of reducing traffic accidents. This study is focused on driver distraction, a state which can easily lead to traffic accidents, and reproduced this distraction in a driving simulator by providing conversation or arithmetic tasks to the subjects. Stereo cameras were used as the means to track subjectspsila eye and head movements. These movements were tracked and their standard deviations were set as classification features of pattern recognition, and the AdaBoost method was used to detect subject distraction. The interval between heart R-waves was also added as a classifier feature, in order to improve cognitive distraction detection performance. The results were then compared with the SVM method from the AIDE Project, which was carried out as part of the EU 6th Framework Programme.

Estimation of drowsiness level based on eyelid closure and heart rate variability

This paper presents a novel method that uses eyelid closure and heart rate variability to estimate the drivers drowsiness level. Laboratory experiments were conducted by using a proprietary driving simulator, which induced drowsiness among the test drivers. The purposes of these experiments were to obtain the electrocardiogram (ECG) and the eye-blink video sequences. Also the drivers were monitored through a video camera. The changes in facial expression of the drivers were used as a standard index of drowsiness level. Error-Correcting Output Coding (ECOC) was employed as a multi-class classifier to estimate the drowsiness level. We extended the ordinary ECOC using a loss function for decoding procedure to obtain class tendencies of each drowsiness level. We used the Loss-based Decoding ECOC (LD-ECOC) to classify the drowsiness level. As a result, we obtained an extraordinarily high accuracy for estimation of drowsiness level.

Analysis of driver behavior based on traffic incidents for driver monitor systems

Research is being conducted into a large number of on-board driver monitor systems as a means of reducing traffic accidents. In order to improve the effectiveness of these systems, it is necessary to detect the driver behavior and mental and physical state immediately before an accident, and to inform or warn the driver of the danger, or else to send an intervention signal to the pre-crash safety system and other advanced vehicle safety systems. Previous research has been conducted for conditions of apparent risk, and has used drive recorders to analyze the causes of accidents and to investigate and analyze driver behavior and other factors which are present immediately before an accident. This study involved an investigation of near-miss accidents (hereafter referred to as ldquoincidentsrdquo) by means of interviews in order to determine the driver behavior and mental and physical state immediately before the incident, when there was the potential risk of an accident. The purpose of this study is to contribute to research concerning advanced vehicle safety systems. We will here provide an analysis of the results and propose direction for future research concerning driver monitor systems.

Detecting Motion Artifact ECG Noise During Sleeping by Means of a Tri-axis Accelerometer

The electrocardiogram (ECG) monitoring has become a helpful tool for detecting a number of heart diseases. High quality ECG is utilized by the physicians for interpretation and identification of physiological and pathological phenomena. ECG recordings, however, are often corrupted by motion artifacts even when patient is sleeping due to his or her positional change. In this paper, we proposed a new method of detecting motion artifact ECG noise during sleeping based on acceleration data. The experimental recordings of ECG and acceleration signal were collected from 8 subjects. The result in this study demonstrated that the proposed approach is effective for detecting corruption of ECG signal.

Effect of pattern recognition features on detection for driver's cognitive distraction

Constituent technology of a driver monitor system using information of a drivers psychosomatic states is expected to create drivers states adaptive drive supporting system for the reduction of traffic accidents. In this study we identified a drivers distraction as one of major psychosomatic states which may result in a traffic accident by using Internet based survey on a questionnaire basis. Then we aimed at creating a methodology in use for detecting drivers cognitive distraction by means of using the AdaBoost which is capable of rapid and accurate classification. Furthermore we verified an effect of pattern recognition features such as interval between heart R-waves (hereafter, heart rate RRI) from an ECG (electrocardiogram), pupil diameter, and, gaze angle and head rotation angle.

Estimation of driver's mental workload using visual information and heart rate variability

Most of the traffic accidents have been caused by drivers inattention. Therefore, driver monitoring is one of the most important challenges in order to prevent traffic accidents. Some studies for evaluating mental workload during driving have been reported; however drivers mental state should be estimated quantitatively for the future. This paper proposes an estimation method of drivers mental workload using physiological information. We defined mental workload as NASA-TLX score, and used the multiple linear regression to quantify drivers mental workload. As a result, higher accuracy was achieved by considering drivers aggression.

Cuffless blood pressure estimation by error-correcting output coding method based on an aggregation of AdaBoost with a photoplethysmograph sensor

This paper presented a novel cuffless and non-invasive technique of Blood Pressure (BP) estimation with a pattern recognition method by using a Photoplethysmograph (PPG) sensor instead of a cuff. Error-Correcting Output Coding (ECOC) method was adopted as a multi-classifier machine based on an aggregation of general binary classifiers. AdaBoost was applied as binary classifier machine. 368 volunteers participated in the experiment. The estimated Systolic Blood Pressure (SBP) was calculated from their individual information and several features of their Pulse Wave (PW). As a result of the comparison between measured SBP, estimated SBP, MD = 3D-1.2 [mmHg] and SD = 3D11.7 [mmHg] were obtained. Hence, this technique would be helpful to the advance the development of continuous BP monitoring system, since the only one device to monitor BP is smaller than traditional measurements.