Applying an Integrated System of Cloud Management and Wireless Sensing Network to Green Smart Environments-Green Energy Monitoring on Campus.

With increasing urbanization, the application of Internet of things (IoT) technology to city governance has become a trend in architecture, transportation, and healthcare management, making IoT applicable in various domains. This study used IoT to inspect green construction and adopted a front-end sensing system, middle-end wireless transmission, and a back-end multifunctional system structure with cloud management. It integrated civil and electrical engineering to develop environmental monitoring technology and proposed a management information system for the implementation of green engineering. This study collected physical "measurements" of the greening environment on a campus. Ambient temperature and humidity were analyzed to explore the greening and energy-saving benefits of a green roof, a pervious road, and a photovoltaic roof. When the ambient temperature was below 25 °C, the solar panels had an insulation effect on the roof of the building during both 4:00−5:00 and 12:00−13:00, with an optimal insulation effect of 2.45 °C. When the ambient temperature was above 25 °C, the panels had a cooling effect on the roof of the building, whether during 4:00−5:00 or 12:00−13:00, with an optimal cooling effect of 5.77 °C. During the lower temperature period (4:00−5:00), the ecological terrace had an insulation effect on the space beneath, with an effect of approximately 1−3 °C and a mean insulation of 1.95 °C. During the higher temperature period (12:00−13:00), it presented a cooling effect on the space beneath, with an effect of approximately 0.5−9 °C and a mean cooling temperature of 5.16 °C. The cooling effect of the three greening areas on air and ground temperature decreased in the following order: pervious road > photovoltaic roof > ecological terrace.

Multiple inertial measurement unit combination and location for recognizing general, fatigue, and simulated-fatigue gait.

BACKGROUND: Muscle fatigue of the lower limbs results in dynamic imbalance and gait instability, increasing the risk of falling. However, people might slow walk without physical muscle fatigue due to mental fatigue. Wearable inertial measurement units (IMU) and machine learning approaches have been well employed for recognizing human activities. RESEARCH QUESTION: The study aims to use a machine learning technique to recognize the data collected from IMUs for physically fatigued or slow-walking gaits. Second, the study aims to reveal the location or the number of IMUs can have the best performance. METHODS: Sixteen healthy adults with six IMUs attached to their heels, toes, sacrum, and head participated in the experiment. On the first day, the participants were instructed to walk along a hallway before and after the fatigue protocol as the Pre- and Post-fatigue gait. On the second day, the participants were instructed to walk along a hallway following the beat of their fatigue gait cadence measured on the first day as the simulated cadence (SC) gait. Gait cycles of each condition were segmented as the inputs of the Long Short-Term Memory (LSTM) model for recognization. RESULTS: The result revealed that the LSTM model could recognize the gait of simulated cadence with the highest accuracy among these three gaits. For the signal body part, the highest accuracy was 93.20 % observed at the IMUs of toes. For the best combination, the IMUs of toes and sacrum achieved the highest accuracy of 95.71 %. SIGNIFICANCE: The machine learning technique of LSTM with one or more IMUs can recognize the gait under normal, physical fatigue, or simulated cadence without muscle fatigue. Our model and approach would be expected to provide conditional warning in multiple fields, such as industrial safety for potential applications.