COVID-Safe Spatial Occupancy Monitoring Using OFDM-Based Features and Passive WiFi Samples

During the COVID-19 pandemic, authorities have been asking for social distancing to prevent transmission of the virus. However, enforcing such distancing has been challenging in tight spaces such as elevators and unmonitored commercial settings such as offices. This article addresses this gap by proposing a low-cost and non-intrusive method for monitoring social distancing within a given space, using Channel State Information (CSI) from passive WiFi sensing. By exploiting the frequency selective behavior of CSI with a Support Vector Machine (SVM) classifier, we achieve an improvement in accuracy over existing crowd counting works. Our system counts the number of occupants with a 93% accuracy rate in an elevator setting and predicts whether the COVID-Safe limit is breached with a 97% accuracy rate. We also demonstrate the occupant counting capability of the system in a commercial office setting, achieving 97% accuracy. Our proposed occupancy monitoring outperforms existing methods by at least 7%. Overall, the proposed framework is inexpensive, requiring only one device that passively collects data and a lightweight supervised learning algorithm for prediction. Our lightweight model and accuracy improvements are necessary contributions for WiFi-based counting to be suitable for COVID-specific applications.

THE ROLE OF DAYLIGHT IN LIBRARY USERS’ SEAT PREFERENCES

Seating that meets the needs and preferences of students can promote a longer stay in libraries and keep students motivated, which in turn influences their emotions and learning abilities. However, existing knowledge on the interaction between daylighting and seating preferences is limited. This study aims to understand what type of spaces are in more demand and the relationship between seat occupancy and daylight availability. Occupancy data of the UCL Bartlett library acquired from motion sensors located underneath each desk was used to assess occupancy, which was then compared to characteristics of space, including daylight availability. The study revealed that although daylight has a considerable impact on students’ seat selection, the seating preference of the students cannot be explained by daylight alone. The seats with a good combination of daylight, outdoor view and privacy are in more demand compared to seats that provide only a high level of daylight. Future research should involve individual perception in addition to occupancy monitoring data, considering daylight conditions together with other components such as privacy, outdoor views, and quietness.

Human Activity Classification Using Multilayer Perceptron

The number of smart homes is rapidly increasing. Smart homes typically feature functions such as voice-activated functions, automation, monitoring, and tracking events. Besides comfort and convenience, the integration of smart home functionality with data processing methods can provide valuable information about the well-being of the smart home residence. This study is aimed at taking the data analysis within smart homes beyond occupancy monitoring and fall detection. This work uses a multilayer perceptron neural network to recognize multiple human activities from wrist- and ankle-worn devices. The developed models show very high recognition accuracy across all activity classes. The cross-validation results indicate accuracy levels above 98% across all models, and scoring evaluation methods only resulted in an average accuracy reduction of 10%.

Multi-Bernoulli Tracking Approach for Occupancy Monitoring of Smart Buildings Using Low-Resolution Infrared Sensor Array

Knowledge about the indoor occupancy is one of the important sources of information to design smart buildings. In some applications, the number of occupants in each zone is required. However, there are many challenges such as user privacy, communication limit, and sensor’s computational capability in development of the occupancy monitoring systems. In this work, a people flow counting algorithm has been developed which uses low-resolution thermal images to avoid any privacy concern. Moreover, the proposed scheme is designed to be applicable for wireless sensor networks based on the internet-of-things platform. Simple low-complexity image processing techniques are considered to detect possible objects in sensor’s field of view. To tackle the noisy detection measurements, a multi-Bernoulli target tracking approach is used to track and finally to count the number of people passing the area of interest in different directions. Based on the sensor node’s processing capability, one can consider either a centralized or a full in situ people flow counting system. By performing the tracking part either in sensor node or in a fusion center, there would be a trade off between the computational complexity and the transmission rate. Therefore, the developed system can be performed in a wide range of applications with different processing and transmission constraints. The accuracy and robustness of the proposed method are also evaluated with real measurements from different conducted trials and open-source dataset.