PMC:7796058 / 39663-63261 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/7796058","sourcedb":"PMC","sourceid":"7796058","source_url":"https://www.ncbi.nlm.nih.gov/pmc/7796058","text":"5. Results and Discussions\n\n5.1. Smartphone Cleaning App\nCleaning activities play an important role in reducing the risk of being exposed to COVID-19. Three types of user activities were defined for the purposes of this research: Working (i.e., the user is busy working), not working (i.e., the user is either a visitor or having time off), and cleaning (i.e., the user is a staff member who is either cleaning or disinfecting the room). As seen in Figure 8a, these three different activities were taken into consideration by the mobile application and the user-selected types of activities were internally stored in their mobile phones.\nWe assumed that after the cleaning activity was carried out, the risk of any COVID-19 viral load being present returned to zero. Over time, interactions between users and the space such as coughing, talking, and touching surfaces would again increase each room’s risk (Equation (2)). If a cleaner specifies in the mobile app that cleaning is done, the room will be marked as “cleaned”, and the risk will go down to zero. Cleaning staff, based on the COVID-19 dissecting rules and regulations forced by the facilities, are trained and clean the room using advanced cleaning equipment (e.g., electrostatic sprayers), which kills 99% viruses. This cleaning activity ensures the virus is killed, and there is no chance for cross-contamination. It is reasonable to assume that the facilities will take precautions with cleaning as much as possible. However, if this assumption is not valid, the risk will be increased over time, which complicates the calculations and increases virus spread and true-positive alarms. Considering cleaning activities resets the risk calculations for the final risk map and reduces false-positive COVID-19 notification alerts. In the future, we are going to evaluate standard-level cleaning activities for COVID-19 using smart cameras automatically. Furthermore, cleaning should include enhanced space ventilation, as airborne particles are remarkably decreased by adequate ventilation.\nFor this research, a virus transmission interval is assumed to be a time interval of 15 min. In other words, if user A was interacting with a room that had been used by a positive COVID-19 infected person, user B, the system would notify user A of probable exposure to the virus. If we consider the situation in which cleaning activity took place after user B left the room, the risk of being exposed by the infected place would be zero. This case can be considered a false positive notification alert for user A. As a result, the proposed system can considerably reduce false positive notifications by using different types of activities. A demo scenario of cleaning person is presented in Supplementary Materials and the trajectories of both building cleaners and visitors is shown in Supplementary Materials.\n\n5.2. Proximity-Based Contact Tracing\nFor the purposes of this research the third floor of the CCIT building was selected for an experiment. After extracting the related metadata such as room names for the rooms from the IndoorGML, 12 Estimote Proximity beacons were spatially distributed between 12 different cell spaces. The contact tracing technique applied for this research was designed in a way that protects user privacy. The application detects the proximal appearance of users within the proximity zone of each beacon by considering the value of the Received Signal Strength Indicator (RSSI) that was broadcasted by the beacons. The duration of appearance of the user in the proximity zone defined for each beacon and the corresponding date and time information for this proximal appearance are the only information stored in the internal storage of mobile phones. Figure 8b shows a screenshot of the developed mobile application for collecting different types of observations including BeaconID, time, date, and the duration that the target user spent in the proximal zone of each beacon. Assuming that the incubation period of COVID-19 is two weeks, the application will work as a background service that saves data internally for a two-week period.\nIn situations in which the user becomes a positive COVID-19 case, he/she can voluntarily share data captured within the past two weeks with the backend database management system. An AWS product Amazon Cognito was used to control user authentication and access to data storage. As shown in Figure 8c, users are required to sign in/up for an Amazon Cognito account in order to share their information. After signing in as an authorized client, users can publish their internal information to the Amazon cloud as shown in Figure 8d. All of the data related to the COVID-19 cases will be stored and managed in the DynamoDB database in the Amazon cloud. Our developed application was connected to the DynamoDB using another AWS product, the IoT Core. When new data is added to cloud storage, the contact tracing application will look for any matches between the backend data and the data stored internally in the user device. If it finds any matches that show that a confirmed COVID-19 positive case and the target user were close to each other for more than 15 min, the application will then notify the target user about potential exposure to COVID-19 and alert cleaning staff to disinfect the place. This process is shown in Figure 9. A demo of people trajectories is shown in Supplementary Materials.\nThere are various methods for indoor positioning, such as WiFi, BLE beacons, or dead reckoning. Using BLE technology is cost-effective compared to other indoor positioning techniques, which use maintenance, installation, and cabling costs. Generally, Bluetooth devices cost ~20× less than WiFi devices and have a similar WiFi accuracy [60].\nIn this paper, we focused on BLE proximity detection for contact tracing instead of precise positioning. Three categories of user location will be of importance for this paper including immediate (less than 60 cm), near (1–6 m), and far (\u003e10 m) distance of the Bluetooth receiver from active BLE beacon. On the other hand, it was still a challenge working with BLE signals that are interfered with by structures. Indoor setting and layout have direct effects on radio waves used in Bluetooth technology. Another challenge was that the different beacon types and battery states produce different signal strengths, so using one beacon library for all types of beacons was problematic.\nIn this paper, an active BLE beacon is placed in each IndoorGML cell (e.g., room). Moreover, we focus on proximity detection (i.e., immediate (within 0.6 m away), near (within about 1–8 m), and far (is beyond 10 m) distances from the active BLE beacon) to make indoor spatiotemporal trajectories using IndoorGML cell connectivity. We avoided having to determine the exact range by way of careful beacon placement to prevent overlaps. In the context of COVID-19 spread, locating in the immediate and near distance from the infected host would be dangerous for coronavirus transmission (through droplet transmission). Accordingly, different health organizations such as WHO recommended two meters distance from others. As a result, proximity detection should be of more importance in the COVID-19 context. In other words, considering precise positioning would only increase the computation cost in this specific application. Describing an indoor location using IndoorGML graph cell also helps with privacy. Considering privacy concerns for individual tracking, especially in indoor environments, we believe that proximity positioning respects user privacy more than precise positioning.\nDepending on the size of the data, type of beacons, and network bandwidth, mobile proximity detection performance may differ. In our experiment, various beacons such as Estimote (https://estimote.com/), Accent Systems (https://accent-systems.com/) and Radius Networks (https://www.radiusnetworks.com/) have been evaluated using the developed app on the Samsung Galaxy S9 smartphone. Our results demonstrated that the app could capture a beacon’s proximity of fewer than 60 milliseconds, which is enough for our case study. The complexity of the position determination depends on the beacon software development kit; however, the complexity is O(n) in the worst-case scenario. Concerning the duration spent in a room, we detected and recorded durations of less than five seconds when walking past beacons in a corridor. Significance of time for the sake of COVID-19 risk was not considered important for durations less than 15 min, which was standard practice. So, our sampling and recording intervals were much better than was required for COVID-19 risk evaluation.\nThe mobile application publishes a JSON payload to the AWS IoT Core cloud data management system in which: Online service: A single record showing the presence of a user in the proximity of an active BLE beacon is published to the AWS IoT core.\nOffline service: An array of records showing the user’s pretenses in a time window is published to the AWS cloud.\nA JSON payload showing a single enriched proximity location captured by the developed smartphone app is shown in Supplementary Materials. For more information regarding contact tracing app can be found in [61].\n\n5.3. Video-Based People Density\nThis section discusses the experimental design for our camera surveillance for counting people, People Density, or the number of people who entered or left a geofence polygon area. For indoor spaces, Physical Distancing rules result in restrictions on the number of people occupying a space. The input for the DL models was online video feeds of fixed cameras focused on the regions of interest defined as IndoorGML cells (e.g., rooms, corridors, lobbies, elevators, stairs, and coffee places). Some cameras might even be able to cover multiple regions of interest (IndoorGML cells), depending on where they are installed and if the spaces are separated by glass walls or windows. An alarm can be triggered by the number of people entering or exiting a region (identified in the camera image) if the density of people exceeds the density of that area. Moreover, the number of people violating physical distancing rules can be identified and reported to the IoCT.\nFor our cleaning use case demo (Supplementary Materials), we considered a meeting room as an IndoorGML node (Room 326) with a four-person capacity. For this demo, the OGC indoorGML was used as it offered the following advantages: IndoorGML cells were defined as the geofence; the geometry and area of each cell (geofence) were calculated and the location of each indoorGML cell (the centroid of the geofence) was used for the enrichment of the camera data. The number of people entering or exiting each cell was monitored. People in each frame were detected in real-time using a pre-trained You Only Look Once (YOLO) model [62] and the results were then published as an MQTT message to the AWS IoT Core. On the backend, the maximum allowed people in a cell, or cell capacity, was either assigned by the building management, or calculated by dividing the cell area into squares of six feet two inches. The “Gathering Restriction”—the number of people over each IndoorGML node—was then calculated. This value changes over a range of 0–1 based on the number of people divided by the capacity of the room. Should the number of people exceed the cell capacity, a Gathering Restriction alarm would be generated for the cell. The following figure (Figure 10) shows a frame of the meeting room, detected people, and Gathering Restriction alarm. The video demo of this scene is attached in Supplementary Materials which shows the people count online when they enter or exit the room.\n\n5.4. Video-Based Physical Distancing\nPhysical Distancing was monitored for each cell using a pre-trained YOLO model for detecting people in that cell. Relative distance was then calculated as follows: The pairwise distance between two people is the distance between the two similar corners of their bounding box. In order to minimize the camera’s vanishing point effect, the distance was then compared to their bounding box diameters. If the distance was less than the longest diameter, it was assumed that the relative distance between those people was violating the Physical Distancing rule. For the following example, the view from a fixed camera was divided into several polygons (geofences). This can result in the creation of separate geofences (indicated by the IndoorGML nodes if they were in the building) from the camera’s viewpoint. The number of people per geofence polygon and the number of times that people were closer than two metres were reported to the IoCT. The following figure (Figure 11) shows a frame of multiple geofences in an outdoor area, the detected people, and the Physical Distancing violations. The video demo of this scene is attached in Supplementary Materials which shows the people count online when they entered or exited the geofences, as well as the physical distancing violations. Outdoor geofences can be connected to the IndoorGML graph nodes.\n\n5.5. Video-Based Risky Behavior Detection\nCamera stream processing is a popular and quick way to detect objects. Human behaviors and actions can be detected as objects from the video frames using a trained deep learning model. For the detection of risky behaviors such as coughing, hugging, handshaking, and doorknob touching, the You Only Look Once version3 (YOLOv3) which is suitable for real-time behavior detection for online video streams, was trained and applied [63,64]. This library classifies and localizes detected objects in one step with a speed of faster than 40 frames per second (FPS). We considered two main types of risky behaviors for COVID-19 indoor transmission: Group risky behaviors (e.g., hugging) and individual risky behaviors (e.g., coughing). Figure 12 illustrates how to train a model for COVID-19 transmission risky behavior detection using YOLOv3.\nIn total, 603 images for coughing, 634 images for hugging, 608 images for handshaking, and 623 images for door touching were used from COCO dataset [62] for transfer learning for the pre-trained model (YOLOv3). These images were taken from free sources found through Google image searches. For labelling objects, a semi-automatic method was applied. Darknet library was also used for training. For individual behaviors, all of the people in images were detected and labelled in a text file whilst the algorithm aggregated intersected bounding boxes of people into a single bounding box. As wrong labels might be generated, the images should be manually checked to correct misclassified objects. For this step 80 percent of the images were selected for training and 20 percent for testing. To increase the accuracy of this model, the configuration in Table 3 was used.\nTo increase training accuracy and speed, a transfer learning process was applied. The base layer is a pre-trained YOLOv3 that uses the COCO dataset for all of the layers of our model except the last. Transfer learning helps with training by exploiting the knowledge of a pre-trained supervised model to address the problems of small training datasets for COVID-19 risky behaviors [65]. To evaluate the accuracy of the model, we tried to check the results for different video datasets by exporting all of the frames for detection under various circumstances for the metrics listed in Table 4.\nAfter studying the outcomes, we found that the “hugging” and “handshaking” classes experienced the highest false negative results compared to coughing as the larger dataset was being prepared for training. It appeared that hugging and handshaking (grouping actions) were more varied in terms of the types of handshaking and hugging. Therefore, training precision could be improved with the preparation of more varied data. Moreover, some of the false positive results for coughing showed that in most cases, moving a hand near the face was detected as coughing, regardless whether it had actually taken place. Furthermore, the number of false negatives increased in a more populated area. Detected touching behavior results demonstrated high numbers of false negative cases. About 75 percent of false negative cases occurred when the predictor incorrectly detected small objects. Therefore, specifying limitations for box sizes and level of confidence for the predictor can reduce false negatives. The results of evaluating precision, recall, F-score, and number of samples for each behavior action class is listed in Table 5.\n\n5.6. Audio-Based Risky Behavior Detection\nThis section examines an audio classification algorithm that recognizes coughing and sneezing using an audio sensor with an embedded DL engine. The methodology for audio detection is shown in Figure 13. This figure shows the four main steps of the audio DL process.The recording needs to first be preprocessed for noise before being used for extracting sound features. The most commonly known time-frequency feature is the short-time Fourier transform [67], Mel spectrogram [68], and wavelet spectrogram [69]. The Mel spectrogram was based on a nonlinear frequency scale motivated by human auditory perception and provides a more compact spectral representation of sounds when compared to the STFT [3]. To compute a Mel spectrogram, we first convert the sample audio files into time series. Next, its magnitude spectrogram is computed, and then mapped onto the Mel scale with power 2. The end result would be a Mel spectrogram [70]. The last step in preprocessing would be to convert Mel spectrograms into log Mel spectrograms. Then the image results would be introduced as an input to the deep learning modelling process.\nConvolutional neural network (CNN) architectures use multiple blocks of successive convolution and pooling operations for feature learning and down sampling along the time and feature dimensions, respectively [71]. The VGG16 is a pre-trained CNN [72] used as a base model for transfer learning (Table 6) [73]. VGG16 is a famous CNN architecture that uses multiple stacks of small kernel filters (3 by 3) instead of the shallow architecture of two or three layers with large kernel filters [74]. Using multiple stacks of small kernel filters increases the network’s depth, which results in improving complex feature learning while decreasing computation costs. VGG16 architecture includes 16 convolutional and three fully connected layers. Audio-based risky behavior detection is based on complex features and distinguishable behaviors (e.g., coughing, sneezing, background noise), which requires a deeper CNN model than shallow architecture (i.e., two or three-layer architecture) offers [75]. VGG16 has been adopted for audio event detection and demonstrated significant literature results [71]. The feature maps were flattened to obtain the fully connected layer after the last convolutional layer. For most CNN-based architectures, only the last convolutional layer activations are connected to the final classification layer [76].\nThe ESC-50 [77] and AudioSet [78] datasets were used to extract cough and sneezing training samples. The ESC-50 dataset is a labelled collection of 2000 environmental audio recordings suitable for benchmarking methods of environmental sound classification. AudioSet consists of an expanding ontology of 632 audio event classes and a collection of 2,084,320 human-labelled, 10 s sound clips taken from YouTube videos. Over 5000 samples were extracted for the transfer learning CNN model which was then divided to train and test datasets. We examined the performance of the trained CNN models using coughing and sneezing. The results are shown in Table 7.\n\n5.7. Risk Calculation and Visualization\nTo demonstrate risk calculation using Equation (2), we evaluated the proposed IoCT using the following cleaning use case scenarios. In meeting room number 326 of the CCIT building, the number of people increased as people entered the room, and this event was detected by a smart camera in the room. The number of people was shown online in the video frame and map visualization browser in green until the room capacity (five) was reached. When the fourth person came in (room capacity is assumed to be three), the alarm notification for “Room exceeded capacity” is shown. After that, a person coughed in the meeting room, and this event was detected by both the smart camera and audio sensors. A notification showed “Cough detected”. Then, the person who coughed opened the door and this event was detected by the smart camera. A “High-risk behavior detected” notification was shown. The risk profile at that moment exceeded the threshold of 0.7 and a notification was sent to the people in room, and to a cleaner. The color of the room polygon turned red indicating high risk and the room polygon was extruded (i.e., the polygon height increases) proportional to the risk value. People started to leave the room causing the risk from People Density to go down, but the risk is higher than at the very beginning as a coughing event had occurred. The total risk value of the meeting room falls but remains higher than before the risky behavior (i.e., cough) took place. The cleaner closer to the room changes his activity status to cleaning (shown by an icon on the map) and moves closer towards the room (from elevator to room). The cleaner trajectory alongside the other people trajectories extracted from BLE beacons were visualized too. After the cleaning activity, the room’s total risk level goes back down to zero and the color of the room polygon changes back to green. The video demo of this scene is attached in the Supplementary Materials which shows the risk profile of the room. A sample screen shot of the Supplementary Materials demo video is presented in Figure 14.\nTo evaluate the impact of various weights assigned to different map layers, we used two sets of weights for map layer aggregations on the client side: Profile 1: W1=W2=W3=W4=1; and Profile 2: W1=0.1, W2=0.4, W3=0.3, and W4=0.2 as mentioned in Section 4.1. Figure 15 shows two risk profiles for room 326 over 40 min from 20:00 to 20: 40 p.m. on 11 June 2020.\nEvaluating precision, recall, and F-Score of video-Based and audio-Based risky behavior detection are listed in in Table 5 and Table 7 accordingly. Table 8 includes time performance of different developed functionalities (e.g., video-based person density, video-based physical distancing, video-based risky behavior detection, and audio-based risky behavior detection) on various platforms such as Jetson NX, laptop, and android smartphone. The performance of using a deep learning engine is highly dependent on Graphics and Computing processors. Therefore, the performance of those functionalities is evaluated on a laptop with more robust processing units. The laptop has NVIDIA GeForce RTX 2070 with 7.5 computation capabilities and a Core i7. Therefore, the performance on Jetson NX is lower than on the laptop. The best performance values are video-based risky behavior detection because they only involve the object detection task. Audio-based risky behavior detection segments the voice in specific time frames and converts them into spectrogram images. Voice patterns are detected in images using the VGG model. Therefore, the time of processing for audio is higher than video object detection. 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