This study presents a radar-based remote measurement system for classification of human behaviors and falls in restrooms without privacy invasion. Our system uses a dual Doppler radar mounted onto a restroom ceiling and wall. Machine learning methods, including the convolutional neural network (CNN), long short-term memory, support vector machine, and random forest methods, are applied to the Doppler radar data to verify the model’s efficiency and features. Experimental results from 21 participants demonstrated the accurate classification of eight realistic behaviors, including falling. Using the Doppler spectrograms (time–velocity distribution) as the inputs, CNN showed the best results with an overall classification accuracy of 95.6% and 100% fall classification accuracy. We confirmed that these accuracies were better than those achieved by conventional restroom monitoring techniques using thermal sensors and radars. Furthermore, the comparison results of various machine learning methods and cases using each radar’s data show that the higher-order derivative parameters of acceleration and jerk, and the motion information in the horizontal direction are the efficient features for behavior classification in a restroom. These findings indicate that daily restroom monitoring using the proposed radar system accurately recognizes human behaviors and allows early detection of fall accidents.

Various remote sensing technologies have been applied in intelligent vehicles and robots for surrounding-environment recognition. However, these technologies experience difficulties in detecting pedestrians in blind areas and their motions, such as rush-out behaviors. To address this issue, we present a radar-based technique for the detection of pedestrians in blind areas and the classification of different risks of rush-out behaviors among detected pedestrians. We verify their ability to detect pedestrian motion in blind areas by conducting experiments in two environments with blind areas formed by outdoor cars and indoor walls. Then, the classification of motions with different risks of rush-out behaviors among pedestrians detected in the blind areas is demonstrated. We use the clustering method to accurately classify several types of behaviors with different rush-out risks in both environments.

Although real-time tracking of moving objects using a variety of sensor parameters is in great demand in monitoring systems, no studies have reported α– $$\beta$$ – $$\gamma$$ tracking filters using simultaneous measurements including acceleration. In this report, we propose and analyze two α– $$\beta$$ – $$\gamma$$ filters using acceleration measurements, namely, position–acceleration-measured (PAM) and position–velocity–acceleration-measured (PVAM) α– $$\beta$$ – $$\gamma$$ filters. Based on our previous work on position–velocity-measured (PVM) α– $$\beta$$ – $$\gamma$$ filters, performance indices of the proposed filters are theoretically derived. Then, numerical analyses clarify the conditions under which the performance of the PAM filter surpasses that of the position-only-measured (POM) α– $$\beta$$ – $$\gamma$$ filter. The results indicate that the PVAM filter achieves better accuracy than the other filters, even with a relatively large measurement noise. This report verifies the effectiveness of the $$\alpha$$ – $$\beta$$ – $$\gamma$$ filters using acceleration measurements based on numerical analyses using derived performance indices. These results are useful in the design of tracking systems including acceleration measurements (e.g., in deciding whether to use the measured acceleration to improve tracking filter performance).

This paper presents the effects of artificial periodic sound on the growth of Hatsuka daikon (radish). In our experiment, radishes were exposed to artificial periodic sound generated by vibration for 6 hours a day. In this experiment, three different frequencies of periodic sound (100, 500, and 1,000 Hz) were used, and the sound volume was set at 50 and 70 dBA. As a result, we found that when the periodic sound was applied, the growth of the tuberous root of the radish was promoted compared to the control, and that the higher the frequency, the more the tuberous roots grew. Also, we found that the conditions of 1,000 Hz and 50 dBA were most effective compared to other conditions, resulting in that the weight of the tuberous root was approximately 1.34 times greater than that of the control. In addition, the Dunnett’s test-based statistical assessment confirmed that there were statistically significant differences in the mean value between the control and the 1,000 Hz and 50 dBA condition-group at the significance level of 5%.