Occupancy detection, including presence detection and head count, as one of the fast growing areas plays an important role in providing safety, comfort and reducing energy consumption both in residential and commercial setups. The focus of this study is proposing affordable strategies to increase occupancy detection performance in realistic scenarios using only audio signal collected from the environment. We use approximately 100-hour of audio data in residential and commercial environments to analyze and evaluate our setup. In this study, we take advantage of developments in feature selection methods to choose the most relevant audio features for the task. Attribute and error vs. human activity analysis are also performed to gain a better understanding of the environmental sounds and possible solutions to enhance the performance. Experimental results confirm the effectiveness of audio sensor for occupancy detection using a cost effective system with presence detection accuracy of 96% and 99%, and the head count accuracy of 70% and 95% for the residential and commercial setups, respectively.
Purpose: In robotic radiosurgery, tumor movements are compensated by tracking external optical surrogates. These surrogates are used to compensate for time delays (prediction) and to calculate the internal fiducial position (correlation). We aim to increase the accuracy and robustness by using a multi‐modal sensor approach including different physiological sensors. We evaluate the correlation coefficient of a strain belt, acceleration and air flow sensor with respect to external standard optical sensors and an internal landmark in the liver tracked using 3D ultrasound and evaluate the variance for a measurement over 20 minutes. Methods: We recorded sensor data from 6 subjects (5 male/1 female). All sensors have been synchronized and downsampled to the optical sampling rate (fs = 47 Hz) or in case of internal correlation to the ultrasound sampling rate (fs = 17 Hz). Pearson's correlation coefficient r was calculated for each minute, discarding the first and last minute. Results: The mean (standard deviation) external correlation coefficients over all subjects and time periods were obtained as: 0.88 (0.036) for strain, 0.75 (0.024) for flow and 0.73 (0.052) for acceleration with respect to an optical marker on the chest. The mean (standard deviation) internal correlation coefficients are: 0.81 (0.045) for strain, 0.76 (0.041) for flow, 0.58 (0.088) for acceleration and 0.80 (0.057) for the optical marker on the chest with respect to the internal landmark. Conclusion: This study indicates that apart from the optical markers, strain and flow data show the best correlation to external and internal motion and seem to be promising for increasing the prediction and correlation accuracy as well as robustness. Among the investigated sensors, the strain data have the lowest standard deviation for internal and external correlation, being even lower than the standard deviation of the optical chest marker.
Purpose: The CyberKnife™ compensates translational target motion by moving the beams synchronously. While the system was found to operate with sub‐millimeter accuracy in phantoms, determining the clinical accuracy is challenging. Measuring the delivered dose distribution inside a patient is impractical. Hence an analysis of treatment data is typically used to estimate residual errors. Methods: We implant 3‐5 fiducials for target tracking and treat liver tumors in 3‐5 fractions with 45Gy at 80% to the PTV (CTV+3mm). Patients are aligned based on X‐ray images in expiration breath hold. During delivery, X‐ray images are acquired every 60‐90s, and the translational and rotational misalignment is computed. We grouped this data into 10 respiratory phases. The mean misalignment for each phase was used to simulate the translation and rotation of the target with respect to the alignment center. The resulting dose distribution was computed and compared to the planned dose. Additionally, the quality of motion prediction was evaluated. Results: We analyzed 5 cases with a total of 17 fractions. The maximal target motion per fraction ranged from 9.2mm to 25.7mm (3D trajectory). The mean error for each patient ranged from ‐0.76/‐0.01/‐ 0.32mm to 0.35/0.17/0.10mm (Translation IS/LR/AP) and ‐0.94/‐0.82/‐2.07 degrees to 0.24/1.95/2.36 degrees (Rotation roll/pitch/yaw). The dose simulation showed point dose difference for each patient ranging from ‐ 0.10Gy to ‐0.76Gy (Mean) and ‐1.13Gy to ‐5.05Gy (Max). The resulting reduction in coverage ranged from 0.37% to 4.19% (PTV) and ‐0.43% to +0.94% (CTV). Finally, the mean prediction error over all fractions was 0.33mm. Conclusions: We demonstrated that while maximum point dose differences can be considerable, the coverage of the CTV is maintained even in the presence of substantial respiratory motion. The results indicate that the standard 3mm system uncertainty margin can account for errors due to rotation and deformation during robotic radiosurgery for tumors in the liver.
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