The Experts below are selected from a list of 3504 Experts worldwide ranked by ideXlab platform
Bing Xie - One of the best experts on this subject based on the ideXlab platform.
-
human respiration detection with commodity wifi devices do user location and body orientation matter
Ubiquitous Computing, 2016Co-Authors: Hao Wang, Daqing Zhang, Yasha Wang, Yuxiang Wang, Dan Wu, Tao Gu, Bing XieAbstract:Recent research has demonstrated the feasibility of detecting human respiration rate non-intrusively leveraging commodity WiFi devices. However, is it always possible to sense human respiration no matter where the subject stays and faces? What affects human respiration sensing and what's the theory behind? In this paper, we first introduce the Fresnel model in free space, then verify the Fresnel model for WiFi radio propagation in indoor environment. Leveraging the Fresnel model and WiFi radio propagation properties derived, we investigate the impact of human respiration on the receiving RF signals and develop the theory to relate one's Breathing Depth, location and orientation to the detectability of respiration. With the developed theory, not only when and why human respiration is detectable using WiFi devices become clear, it also sheds lights on understanding the physical limit and foundation of WiFi-based sensing systems. Intensive evaluations validate the developed theory and case studies demonstrate how to apply the theory to the respiration monitoring system design.
-
UbiComp - Human respiration detection with commodity wifi devices: do user location and body orientation matter?
Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2016Co-Authors: Hao Wang, Daqing Zhang, Yasha Wang, Yuxiang Wang, Bing XieAbstract:Recent research has demonstrated the feasibility of detecting human respiration rate non-intrusively leveraging commodity WiFi devices. However, is it always possible to sense human respiration no matter where the subject stays and faces? What affects human respiration sensing and what's the theory behind? In this paper, we first introduce the Fresnel model in free space, then verify the Fresnel model for WiFi radio propagation in indoor environment. Leveraging the Fresnel model and WiFi radio propagation properties derived, we investigate the impact of human respiration on the receiving RF signals and develop the theory to relate one's Breathing Depth, location and orientation to the detectability of respiration. With the developed theory, not only when and why human respiration is detectable using WiFi devices become clear, it also sheds lights on understanding the physical limit and foundation of WiFi-based sensing systems. Intensive evaluations validate the developed theory and case studies demonstrate how to apply the theory to the respiration monitoring system design.
Daniel A. Low - One of the best experts on this subject based on the ideXlab platform.
-
SU‐E‐J‐126: Development of a Prospective Gating Algorithm for a Novel 4DCT Technique: Retrospective Data Analysis
Medical Physics, 2013Co-Authors: Daniel A. Low, David Thomas, Benjamin White, S Gaudio, S Jani, Percy Lee, James LambAbstract:Purpose: To develop an algorithm for prospective CT scan selection to support a novel 4DCT acquisition techniqueMethods: The new 4DCT acquisition protocol utilizes repeated rapid helical CT scans. Unless the CT scanner is synchronized with Breathing, scans may be acquired at similar Breathing phases, adding little to the Breathing‐motion characterization and therefore unnecessarily irradiating the patient. A retrospective patient dataset that consisted of 25 repeated CT scans and the accompanying Breathing‐motion model was used to evaluate possible algorithms. A single coronal slice through the lungs was used for the preliminary analysis. The mean model discrepancy for the 25 scans was 0.63mm. The model was fit using every subset of N scans out of the first 15. The discrepancies ranged from nearly 0.68mm to 9mm. The variation was hypothesized to be due to the level of redundancy in the scans used to generate the motion models. Scans were evaluated based on how the Breathing Depth and Breathing rate (the two independent variables in the Breathing motion model) differed at each CT slice. To maximize the variety of breaths selected from the 15, the mean square root difference D of the Breathing Depths and Breathing rates were calculated and D correlated against the model discrepancies. Results: The square‐root difference function provided excellent separation between scan combinations that had small and large model errors. As expected, scans sets with small model errors were well spaced in Breathing Depth and rate. An optimal combination of Breathing Depth and rate was identified and shown to better select good scan combinations relative to separately analyzing Depth or rate. The optimal scans had residual errors of 0.68mm compared with the baseline of 0.63mm. Conclusion: The use of the mean square‐root difference in Breathing Depth and rate will provide guidance for prospective scanning algorithm development. Supported by NIH R01 CA096679
-
WE‐C‐ValA‐06: Characterizing and Modeling Patient Respiratory Patterns for Radiation Therapy
Medical Physics, 2006Co-Authors: J Repp, James P. Hubenschmidt, Parag J. Parikh, Sasa Mutic, J. Bradley, Daniel A. LowAbstract:Purpose:Lungtumor Breathing motion is a function of both the Breathing Depth (tidal volume) and Breathing rate (airflow). Treatment planning for lungtumors will require a patient‐specific tumor motion model. An understanding of the patient's typical Breathing cycle will be critical to accurate treatment planning predictions for linear‐accelerator gating or tracking. This work examines patient‐Breathing characteristics to determine the patterns and stability of the Breathing cycle. Method and Materials: A total of 34 patients (with and without lungcancer) were scanned with a previously described 4DCT protocol under synchronized tidal Breathing monitoring. We examined the scatter plot of air flow against tidal volume for the entire scan session. A 2D histogram was then generated by calculating the frequency of data points falling into each tidal volume‐airflow window. The 2D histogram depicted the probability that the patient breathed at certain tidal volume and air flow window. We evaluated the Breathing consistency of the three patients with multiple scans. Results: There was no significant differences in the Breathing period (P=0.11) or in the peak‐to‐peak amplitude (P=0.22) between patients with lungcancers and patients with upper‐abdomen cancers. The 2D histogram revealed two different Breathing patterns: patients who spent more time Breathing at the end of exhalation (20 patients), exhibiting a characteristic volume‐flow curve, and patients who spent the majority of time inhaling and exhaling (14 patients). The latter patients did not spend any appreciable amount of time between successive breaths. For the three patients with two sessions, the mean frequency difference between corresponding tidal volume‐airflow windows was
-
MO‐D‐ValA‐01: Modeling & Characterization of Respiratory Motion
Medical Physics, 2006Co-Authors: Daniel A. Low, James P. Hubenschmidt, Parag J. Parikh, Sasa Mutic, Jeffrey D. BradleyAbstract:A quantitative understanding of respiratory motion is critical to improving radiation therapy for lung and upper abdominal cancers. Breathing motion impacts the quality of diagnostic and treatment planning images, causes conformal therapy portals to be larger than the cross‐sectional projection of the tumor, and increases irradiated normal organ volumes. Methods intended to reduce or eliminate the impact of Breathing motion have been proposed, including breath hold, linear accelerator gating, and tracking either using the linear accelerator or the patient support assembly. A quantitative model of the patient's Breathing motion, both tumor and normal organs, is necessary to optimize the gating or tracking methods. The form of the respiratory model will depend on the ultimate use of the model. In the case of radiation therapy, we are interested in understanding the positions of the tumor and normal organs as a function of time, because our radiation delivery systems operate as a function of time. However, Breathing is not sufficiently reproducible to use time directly as the independent model variable. A different, time‐dependent metric needs to be selected as the quantity that will be characterized as a function of time and, during acquisition of the motion model data and radiation treatment, be monitored. The metric needs to be: easily measured, quantitative, reproducible, and correlated with Breathing motion. Metrics that have been proposed include abdomen or thorax height, abdomen circumference, and spirometry‐measured tidal volume. The motion model requires input data to provide the patient‐specific parameters. The input data is typically derived from CT scans that are acquired while the patient undergoes simultaneous monitoring of the metric. This process is labeled “4D CT” in that multiple CT scans are acquired at each location, each scan acquired at a different time. CT scans are typically reconstructed or resorted at a variety of Breathing phases. The reconstructed CT scans are then used to determine tumor and normal organ positions as a function of the Breathing metric. In the use of respiratory motion modeling there are some confusing and overlapping uses for the word “phase” that are worth differentiating. Firstly, the use of the term “Breathing phase” is used to describe a general part of the Breathing cycle, such as mid‐inhalation. Secondly, “phase angle” is used to describe a hypothetical angle used when the Breathing cycle is described as a periodic function of time, and finally, “phase” itself is used for any quantitatively defined Breathing state. Prior to the development of a biophysically based Breathing motion model, there have been two competing methods for describing the behavior of the metric as a function of time; phase‐angle and amplitude. Phase‐angle descriptions divide the Breathing cycles between selected Breathing phases, for example, inhalation. The time between successive inhalations is recast linearly as an angle from 0 to 2π (alternatively, some investigators separate the inhalation and exhalation processes, placing 0 and π at inhalation and exhalation, respectively, with linear time interpolation between these Breathing phases). In the phase‐angle approach, each inhalation and exhalation is treated equally, irrespective of the Depth of Breathing, but the model can accurately characterize variations in Breathing frequency (at least retrospectively). In this model, the description of motion as a function of phase angle can either be the positions as a function of angle or be written as periodic functions with parameters that provide the positions. The phase‐based process is capable of describing the hysteresis‐like motion of lungtumors well, but is not capable of adequately describing variations in Breathing Depth. Patient Breathing training is often employed to reduce variations in Breathing Depth. Amplitude‐based methods describe the tumor and organ positions as a function of the metric's amplitude, or numerical value. The time‐dependence of the Breathing cycle is taken from the time‐dependence of the metric amplitude. The amplitude‐based approaches are capable of describing variations in Breathing Depth, but because modeling of hysteresis requires degeneracy in tumor positions as a function of amplitude, hysteresis is not easily described using the amplitude models. While both amplitude and phase‐based models have been utilized to define and describe Breathing motion, neither can adequately model even the simplest Breathing motion, namely the amplitude‐variable hysteresis motion of lungtumors and normal organs that is known to exist. Recently a Breathing model has been proposed that describes tissue positions as a function of tidal volume, namely the amount of air inhaled and exhaled during the Breathing process. The model assumes that lungtissue positions vary as a function of tidal volume, or in other words, the deeper the breath, the farther the tissues move in their trajectories. Hysteresis is hypothesized to be due to pressure imbalances within the lungtissues that create the variations in trajectory between inhalation and exhalation. The pressure imbalances are assumed to be linearly proportional to the airflow (time derivative of the tidal volume). The position of a piece of lungtissue is therefore a function of its location at a reference Breathing phase (e.g. tidal exhalation), the tidal volume and airflow relative to the reference Breathing phase. Incidentally, while tidal volume has been used as the metric, any metric that is proportional to tidal volume and its temporal derivative can be used as the metric. For example, published reports indicate that abdomen height is linearly related to tidal volume for quiet respiration. Understanding the variables that govern respiratory motion is insufficient to describe the positions; a mathematical model is still required. The simplest, namely a linear relationship between position and tidal volume and position and airflow, where the two position components are treated as independent, has been used and appears to provide good descriptions of Breathing motion, although supporting data is still limited. The process of modeling Breathing motion is still in its beginning stages, but there are promising approaches being studied. Assuming that the models can accurately describe Breathing motion, they will be key components in the treatment planning process.
-
SU‐FF‐J‐10: A 5‐Dimensional Breathing Motion Model for Radiation Therapy
Medical Physics, 2005Co-Authors: Daniel A. Low, James P. Hubenschmidt, Parag J. Parikh, James F. Dempsey, Sasha H. Wahab, M. Nystrom, Maureen Handoko, J. BradleyAbstract:Purpose: To develop a mathematical formalism that models Breathing motion of lungtumors and normal organs for radiation therapytreatment planning.Method and Materials: The model is based on the assumption that Breathing motion is caused by interactions between muscle‐induced air‐pressure distributions and the supporting structure of lungtissues. For quiet respiration, the motion of each object within the lungs is broken down into two independent components. First, the positions in the steady‐state (zero airflow) are modeled as a function of Breathing Depth, parameterized by the tidal volume v. As the patient breaths deeper, the objects move farther along their trajectories. Second, the hysteresis commonly observed in lung motion is due to local pressure imbalances caused during the dynamic act of Breathing and is assumed to be a deviation from the zero‐flow trajectory. The hysteresis component is assumed to be proportional to local pressure imbalances which are proportional to the airflow at the patient's mouth, so this component is modeled as a function of airflow f=dv/dt. The motion of any internal object, therefore, has five degrees‐of‐freedom; the three Cartesian coordinates of the object during a user‐selected reference Breathing phase, the tidal volume and the airflow. Results: The model was applied using a linear mathematical form used with measured patient Breathing‐trajectory data of 76 tracked objects in 4 patients, which data was acquired using 4DCT and concurrent spirometry‐measured tidal volume. The tracked‐object displacement was a linear combination of two independent vectors with lengths proportional to the tidal volume and airflow. The patient data showed that the mathematical formalism was capable of modeling the objects' motion within 10% and 15% of the objects' maximum extent for 73% and 95% of the objects, respectively. Conclusion: This 5‐dimensional model provides a method for mapping Breathing motion and is being extended to more clinical datasets.
Hao Wang - One of the best experts on this subject based on the ideXlab platform.
-
human respiration detection with commodity wifi devices do user location and body orientation matter
Ubiquitous Computing, 2016Co-Authors: Hao Wang, Daqing Zhang, Yasha Wang, Yuxiang Wang, Dan Wu, Tao Gu, Bing XieAbstract:Recent research has demonstrated the feasibility of detecting human respiration rate non-intrusively leveraging commodity WiFi devices. However, is it always possible to sense human respiration no matter where the subject stays and faces? What affects human respiration sensing and what's the theory behind? In this paper, we first introduce the Fresnel model in free space, then verify the Fresnel model for WiFi radio propagation in indoor environment. Leveraging the Fresnel model and WiFi radio propagation properties derived, we investigate the impact of human respiration on the receiving RF signals and develop the theory to relate one's Breathing Depth, location and orientation to the detectability of respiration. With the developed theory, not only when and why human respiration is detectable using WiFi devices become clear, it also sheds lights on understanding the physical limit and foundation of WiFi-based sensing systems. Intensive evaluations validate the developed theory and case studies demonstrate how to apply the theory to the respiration monitoring system design.
-
UbiComp - Human respiration detection with commodity wifi devices: do user location and body orientation matter?
Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2016Co-Authors: Hao Wang, Daqing Zhang, Yasha Wang, Yuxiang Wang, Bing XieAbstract:Recent research has demonstrated the feasibility of detecting human respiration rate non-intrusively leveraging commodity WiFi devices. However, is it always possible to sense human respiration no matter where the subject stays and faces? What affects human respiration sensing and what's the theory behind? In this paper, we first introduce the Fresnel model in free space, then verify the Fresnel model for WiFi radio propagation in indoor environment. Leveraging the Fresnel model and WiFi radio propagation properties derived, we investigate the impact of human respiration on the receiving RF signals and develop the theory to relate one's Breathing Depth, location and orientation to the detectability of respiration. With the developed theory, not only when and why human respiration is detectable using WiFi devices become clear, it also sheds lights on understanding the physical limit and foundation of WiFi-based sensing systems. Intensive evaluations validate the developed theory and case studies demonstrate how to apply the theory to the respiration monitoring system design.
Daqing Zhang - One of the best experts on this subject based on the ideXlab platform.
-
human respiration detection with commodity wifi devices do user location and body orientation matter
Ubiquitous Computing, 2016Co-Authors: Hao Wang, Daqing Zhang, Yasha Wang, Yuxiang Wang, Dan Wu, Tao Gu, Bing XieAbstract:Recent research has demonstrated the feasibility of detecting human respiration rate non-intrusively leveraging commodity WiFi devices. However, is it always possible to sense human respiration no matter where the subject stays and faces? What affects human respiration sensing and what's the theory behind? In this paper, we first introduce the Fresnel model in free space, then verify the Fresnel model for WiFi radio propagation in indoor environment. Leveraging the Fresnel model and WiFi radio propagation properties derived, we investigate the impact of human respiration on the receiving RF signals and develop the theory to relate one's Breathing Depth, location and orientation to the detectability of respiration. With the developed theory, not only when and why human respiration is detectable using WiFi devices become clear, it also sheds lights on understanding the physical limit and foundation of WiFi-based sensing systems. Intensive evaluations validate the developed theory and case studies demonstrate how to apply the theory to the respiration monitoring system design.
-
UbiComp - Human respiration detection with commodity wifi devices: do user location and body orientation matter?
Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2016Co-Authors: Hao Wang, Daqing Zhang, Yasha Wang, Yuxiang Wang, Bing XieAbstract:Recent research has demonstrated the feasibility of detecting human respiration rate non-intrusively leveraging commodity WiFi devices. However, is it always possible to sense human respiration no matter where the subject stays and faces? What affects human respiration sensing and what's the theory behind? In this paper, we first introduce the Fresnel model in free space, then verify the Fresnel model for WiFi radio propagation in indoor environment. Leveraging the Fresnel model and WiFi radio propagation properties derived, we investigate the impact of human respiration on the receiving RF signals and develop the theory to relate one's Breathing Depth, location and orientation to the detectability of respiration. With the developed theory, not only when and why human respiration is detectable using WiFi devices become clear, it also sheds lights on understanding the physical limit and foundation of WiFi-based sensing systems. Intensive evaluations validate the developed theory and case studies demonstrate how to apply the theory to the respiration monitoring system design.
Yasha Wang - One of the best experts on this subject based on the ideXlab platform.
-
human respiration detection with commodity wifi devices do user location and body orientation matter
Ubiquitous Computing, 2016Co-Authors: Hao Wang, Daqing Zhang, Yasha Wang, Yuxiang Wang, Dan Wu, Tao Gu, Bing XieAbstract:Recent research has demonstrated the feasibility of detecting human respiration rate non-intrusively leveraging commodity WiFi devices. However, is it always possible to sense human respiration no matter where the subject stays and faces? What affects human respiration sensing and what's the theory behind? In this paper, we first introduce the Fresnel model in free space, then verify the Fresnel model for WiFi radio propagation in indoor environment. Leveraging the Fresnel model and WiFi radio propagation properties derived, we investigate the impact of human respiration on the receiving RF signals and develop the theory to relate one's Breathing Depth, location and orientation to the detectability of respiration. With the developed theory, not only when and why human respiration is detectable using WiFi devices become clear, it also sheds lights on understanding the physical limit and foundation of WiFi-based sensing systems. Intensive evaluations validate the developed theory and case studies demonstrate how to apply the theory to the respiration monitoring system design.
-
UbiComp - Human respiration detection with commodity wifi devices: do user location and body orientation matter?
Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2016Co-Authors: Hao Wang, Daqing Zhang, Yasha Wang, Yuxiang Wang, Bing XieAbstract:Recent research has demonstrated the feasibility of detecting human respiration rate non-intrusively leveraging commodity WiFi devices. However, is it always possible to sense human respiration no matter where the subject stays and faces? What affects human respiration sensing and what's the theory behind? In this paper, we first introduce the Fresnel model in free space, then verify the Fresnel model for WiFi radio propagation in indoor environment. Leveraging the Fresnel model and WiFi radio propagation properties derived, we investigate the impact of human respiration on the receiving RF signals and develop the theory to relate one's Breathing Depth, location and orientation to the detectability of respiration. With the developed theory, not only when and why human respiration is detectable using WiFi devices become clear, it also sheds lights on understanding the physical limit and foundation of WiFi-based sensing systems. Intensive evaluations validate the developed theory and case studies demonstrate how to apply the theory to the respiration monitoring system design.