Air pollution is a social problem, because the harmful suspended materials can cause diseases and deaths to humans. Specifically, particulate matters (PM), a form of air pollution, can contribute to cardiovascular morbidity and lung diseases. Nowadays, humans are exposed to PM pollution everywhere because it occurs in both indoor and outdoor environments. To purify or ventilate polluted air, one need to accurately monitor the ambient air quality. Therefore, this study proposed a practical particulate matter sensing and accurate calibration system using low-cost commercial sensors. The proposed system basically uses noisy and inaccurate PM sensors to measure the ambient air pollution. This paper mainly deals with three types of error caused in the light scattering method: short-term noise, part-to-part variation, and temperature and humidity interferences. We propose a simple short-term noise reduction method to correct measurement errors, an auto-fitting calibration for part-to-part repeatability to pinpoint the baseline of the signal that affects the performance of the system, and a temperature and humidity compensation method. This paper also contains the experiment setup and performance evaluation to prove the superiority of the proposed methods. Based on the evaluation of the performance of the proposed system, part-to-part repeatability was less than 2 μg/m³ and the standard deviation was approximately 1.1 μg/m³ in the air. When the proposed approaches are used for other optical sensors, it can result in better performance.

Speed estimation and gait classification are widely used in gait analysis for various applications such as medical diagnostics, rehabilitation, and human activity tracking. Smart insoles provide a simple and good infra to measure steps and plantar pressure used for gait analysis. This paper proposes a speed estimation and gait classification using plantar pressure and on-device deep learning. First, this paper designs and implements a smart insole and Android application to collect plantar pressure data of human being and then proposes the accurate speed estimation method. Second, I collect various gait dataset from the proposed smart insoles and then build lightweight deep learning models running on the Smartphone and Table PC in real time. In addition, this paper evaluated performance of the proposed speed estimation and on-device deep learning model. As a result, the accuracy of speed estimation was nearly 97% and that of gait classification was also approximately 97%.

Anemia is a condition in which red blood cells or the hemoglobin in the blood is lower than in healthy people. Red blood cells transport and supply oxygen needed to various organs in the human body. Anemia is caused by hypoxemia due to the lack of red blood cells and causes other serious health problems, such as heart problems, pregnancy complications, severe fatigue, or death. There are many causes of anemia, and it can be diagnosed by measuring hematocrit or hemoglobin levels in the blood. Even though there are various diagnostic devices on the market, these devices are inconvenient because their systems are bulky, heavy, expensive, or inaccurate. This study proposed a new anemia diagnostic system based on the impedance measurement of red blood cells. The proposed system consists of a test strip that collects a blood sample from the finger and a hemoglobin meter that measures the impedance of the blood and converts it into the concentration of hemoglobin. The proposed test strip that does not contain enzymes or reagents was designed in accordance with class 1 approval by the Food and Drug Administration (FDA). The hemoglobin meter was designed to include a hardware block, an algorithm block and a calibration block through empirical work. We also compared it to reference impedance to prove the accuracy of the hemoglobin meter. The experimental results with human blood indicated the superiority of the anemia diagnostic system. As a result, the overall standard deviation of impedance measurements was less than 1%, and the coefficient of variance of the proposed system was 1.7%, which was better than that of other commercial systems.

With the rise of interest in watch-type and wristband-type devices, wrist-worn devices have become a growing market in the field of wearable activity tracking devices. However, activity tracking with wrist-worn device is more challenging than the task with the other parts of the body, since upper limb movements are uncoupled from the action of walking and reveal irregular movements. This paper proposes a walking speed estimation method with data from a six-axis inertial measurement unit (IMU), which is commonly mounted in wrist-worn devices, and user’s height information. The proposed method provides accurate walking speed estimation results under different sensor-carrying modes and walking speeds. The estimation is based on sensor-carrying mode detection with the TreeBagger model, and on Gaussian process regression (GPR) models, which are adapted to seven predetermined sensor-carrying modes. The speed estimation is done by calculating a weighted sum of multiple GPR models with the probabilities from TreeBagger model. To evaluate the superiority of our method, we implement it on a hardware. An experimental evaluation is performed on 18 healthy subjects with a treadmill. The experimental results show that the proposed method outperforms existing studies and comparable commercial devices for all sensing conditions. The averaged error of the proposed method is about 5% for all sensing conditions, while others show error of more than 15% in different sensing conditions. The results show that proposed method provides better accuracy compared to existing studies in terms of estimating walking speed accurately under changing sensing conditions only with single IMU sensor.

Inter-floor noise is a severe social problem which causes illegal arson, violence, and even murder. In this paper, an inter-floor noise sensing system is proposed to detect and record information related to inter-floor noise in an apartment building. The proposed system measured the noise level and estimated the direction of the noise source along with the type of noise. The noise level measurement is based on the sound pressure level (SPL) measurement, which is a logarithmic measure of the effective pressure of a sound relative to a reference sound pressure. Noise source localization was performed using the estimated time difference of arrival (TDOA) from the microphone array. For the classification of noise types, the Mel frequency cepstral coefficients (MFCC) and zero-crossing rate (ZCR) were extracted from a noise signal, and the k-nearest neighbor algorithm was used to classify the type of noise. In addition, we developed a noise monitoring hardware to evaluate our methods in the actual environment. The experimental results demonstrated that the proposed system had a reliable accuracy for each functional unit. The results showed that the error of the noise level was approximately ±1.5 dB(A), the error of the direction estimation was approximately ±10°, and the accuracy of the classification for the noise type was more than 75%. These output data from the proposed system are expected to be used as important reference data for any dispute cases due to inter-floor noise.