OBJECTIVETo describe the epidemiological characteristics of nonfatal child pedestrian injuries and provide information to help understand an important public-health problem.

METHODSThis was a school-based, cross-sectional questionnaire survey. The sample (42 750 children) was obtained from two urban cities of Guangdong Province, China, using multi-stage randomized sampling. Information was collected by the respondents self-reporting in the classroom.

RESULTSThe incidence rate of nonfatal child pedestrian injuries in the cities was 2.0%. Boys had a higher incidence rate (2.6%) than girls (1.4%). Compared to other children, those aged 10 years are at the highest risk. The primary places of occurrence were sidewalks, residential roads, and crosswalks. High-risk behavior of the children immediately prior to injury included mid-block crossings, playing on roads, and crossing on red lights. The major vehicles that caused pedestrian injuries were bicycles, car or vans, and motorcycles. Bruises, fractures, and injuries to the internal organs were the top three types of injuries. Almost 40% of victims were hospitalized, and nearly 30% of the victims suffered long-term disabilities.

CONCLUSIONThis study shows that nonfatal child pedestrian injuries are a very serious public-health problem in the urban cities of Guangdong. Based on the epidemiological characteristics, prevention strategies and further research should be carried out to reduce the occurrence of injuries.

Accident Prevention ; methods ; Accidents, Traffic ; prevention & control ; statistics & numerical data ; Animals ; Child ; China ; epidemiology ; Cross-Sectional Studies ; Data Collection ; Female ; Humans ; Incidence ; Male ; Motor Vehicles ; Surveys and Questionnaires ; Urban Population ; statistics & numerical data ; Wounds and Injuries ; epidemiology

Photoplethysmography (PPG) is often affected by interference, which could lead to incorrect judgment of physiological information. Therefore, performing a quality assessment before extracting physiological information is crucial. This paper proposed a new PPG signal quality assessment by fusing multi-class features with multi-scale series information to address the problems of traditional machine learning methods with low accuracy and deep learning methods requiring a large number of samples for training. The multi-class features were extracted to reduce the dependence on the number of samples, and the multi-scale series information was extracted by a multi-scale convolutional neural network and bidirectional long short-term memory to improve the accuracy. The proposed method obtained the highest accuracy of 94.21%. It showed the best performance in all sensitivity, specificity, precision, and F1-score metrics, compared with 6 quality assessment methods on 14 700 samples from 7 experiments. This paper provides a new method for quality assessment in small samples of PPG signals and quality information mining, which is expected to be used for accurate extraction and monitoring of clinical and daily PPG physiological information.
Photoplethysmography ; Machine Learning ; Neural Networks, Computer

The extraction of pulse rate variability(PRV) in daily life is often affected by exercise and blood perfusion. Therefore, this paper proposes a method of detecting pulse signal and extracting PRV in post-ear, which could improve the accuracy and stability of PRV in daily life. First, the post-ear pulse signal detection system suitable for daily use was developed, which can transmit data to an Android phone by Bluetooth for daily PRV extraction. Then, according to the state of daily life, nine experiments were designed under the situation of static, motion, chewing, and talking states, respectively. Based on the results of these experiments, synchronous data acquisition of the single-lead electrocardiogram (ECG) signal and the pulse signal collected by the commercial pulse sensor on the finger were compared with the post-auricular pulse signal. According to the results of signal wave, amplitude and frequency-amplitude characteristic, the post-ear pulse signal was significantly steady and had more information than finger pulse signal in the traditional way. The PRV extracted from post-ear pulse signal has high accuracy, and the accuracy of the nine experiments is higher than 98.000%. The method of PRV extraction from post-ear has the characteristics of high accuracy, good stability and easy use in daily life, which can provide new ideas and ways for accurate extraction of PRV under unsupervised conditions.
Ear ; Electrocardiography, Ambulatory ; Fingers ; Heart Rate ; Humans ; Monitoring, Ambulatory ; Motion ; Pulse