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Remote monitoring of vital signs in infectious patients minimizes the risks of viral transmissions to healthcare professionals. Evidence indicates that donning face masks reduces the risk of viral transmissions and is now the norm in medical facilities. We propose attaching an acoustic-sensing device onto face masks to assist medical facilities in monitoring patients' respiration remotely. Usability and functionality studies of the modified face mask were evaluated on 16 healthy participants, who were blindfolded throughout the data collection. Around half of the participants noticed the difference between the modified and unmodified masks but they also reported there was no discomfort in using the modified mask. Respiratory rates of the participants were evaluated for one minute and the mean error of respiratory rate was found to be 2.0 +/- 1.3 breath per minute. As all participants were healthy, the wheeze detection algorithm was assessed by playing 176 wheezes and 176 normal breaths through a foam mannequin. The recordings were played at three different times to account for varying environmental noise. The overall accuracy of the wheeze detection algorithm was 91.9%. The current findings support and suggest the use of the mask attachment in medical facilities., Comment: Published on Inventions, 10 pages

A wireless miniature wearable microphone sensor for cardiopulmonary monitoring was designed and demonstrated. The sensor acquires acoustic signal from the chest of a wearer and transmits the signal to a computing system where a set of algorithms compute heart and respiratory vital signs. This system enabled a wire-free, continuous and concurrent monitoring of both heart and respiratory rate to the mean ± standard deviation of error of 1.30±0.80 beats per minute (bpm) and 1.40±0.40 breaths per minute (brpm) under a moderate environment, comparable to a typical hospital ward. An accuracy of 1.00±0.85 bpm and 2.10±2.00 brpm was also achieved in the presence of noise in the form of background music. When the HR algorithm was evaluated in 4 different subjects, the mean standard deviation error was found to have increased to 1.71±1.75 bpm.

Leaks in wellbore tubulars emit acoustic waves in the borehole, which can be captured by a hydrophone array. Processing the array data yields location and energy level of the wellbore leaks; however, the hydrophones may also capture other coherent noise propagating as guided waves along the borehole, such as road noise caused by the displacement of logging equipment on the wellbore surface, by vibration from surface production facilities, and by distant loud leaks. Such coherent acoustic noise largely interferes with the estimation of a target leak's characteristics. Conventionally, to record higher-quality data, additional recordings may be required to station the tool at selected locations; however, this standard approach prolongs the acquisition time and constrains the vertical resolution. This paper describes an advanced approach to estimate and subsequently remove the guided-wave noise from the array hydrophone data to improve the accuracy of leak source locations. Furthermore, estimating the propagation direction and amplitude of leak-induced guided waves aids logging operations to efficiently locate a leak source. We propose an array data processing approach to separate the guided-wave noise from the direct arriving leak signal. The upward and downward propagating guided waves are identified using a least-square wave separation method. An alternative time-domain stacking method is also proposed for real-time fast computation. The slowness of the guided wave is optimized within a range and then used to estimate and remove the guided waves. Furthermore, characteristics of the estimated guided-wave noise can be extracted, such as the propagation direction, amplitude, and slowness. These parameters plotted in discrete depth locations provide additional information on the well condition. Synthetic and field data results show that the proposed method significantly improves the accuracy of the 2D flow map by removing the erroneous map artifacts due to guided waves. The low-frequency energy due to guided waves is also removed from the frequency spectrum log. The amplitude difference between the upward and downward propagating waves indicates the source direction of the leakage-induced waves. Hence, monitoring the guided wave amplitude and direction in real time provides an efficient way to quickly locate a leak source and reduce operation time. Conventional noise logging data are commonly contaminated by guided-wave noise. Single hydrophone tools cannot separate out these guided waves. The proposed approach, using a hydrophone array for separating guided-wave noise from direct arriving leak signals, provides a high-quality estimation of the energy and location of leaks in wellbore tubulars.

Recently, the grid-free compressive beamforming approach was proposed to perform direction-of-arrival (DOA) of sources using observations collected from an array of sensors; however, it is limited to sources with a single frequency. This paper extends it by considering a wideband signal impinging an array and introduces a continuous formulation of the discretized multiband representation of the impinging signal. Next, a sparsity measure is proposed to promote the continuous variable’s sparsity. In turn, the DOA estimation problem with infinite unknowns is derived and solved over a few optimization variables with the semidefinite programming. Further, it is demonstrated that the derived problem possesses properties that can be exploited. Accordingly, when the absolute frequency differences phrased in wavelengths are larger than twice the array spacing, an aliased-free DOA estimation can be achieved. Results from simulation and experimental example confirms that at aliasing condition, with sufficient sensors, the proposed approach provides improved DOA estimation using a small number of continuous multiband frequencies compared to other methods using discrete signal model formulation.

With the growing number of mature wells globally, more integrity issues are arising, thus creating a need for diagnostic technologies. Aging wells can develop corrosion or mechanical failures, allowing unwanted communication. Existing technologies allow the identification of leak depths, but do not provide reliable information regarding radial location. A new acoustic array technology helps identify wellbore leaks by depth and radially. A description of the leak's flow pattern in and around the wellbore can also be obtained. Having a clearer picture of the location and leak type makes operational planning more efficient and helps prevent nonproductive time (NPT). Typically, noise tools have used a single hydrophone or a few sensors to capture noise measurements. An array of sensors can capture an acoustic event from multiple points simultaneously and uses this information to localize the leak. This measurement removes the high dependency on the frequency and magnitude alone to identify the location of a leak. During well testing, sound waves travel up and down the pipe and build standing waves in the pipe, giving a false echo of the leak location when just examining frequency and magnitude. The array negates these inaccuracies and accurately identifies the leak's vertical and radial location, even when frequency and magnitude alone show a different response. Operators have field tested a new acoustic array tool in aging wells. During this time, the tools were exposed to various leak conditions and problems. The array tools have shown value by identifying depth of communication and the radial location in the wellbore. The radial locater helped discern whether it was tubing, Annuli A, Aannuli B, or an outer casing issue. A unique flow map imaging technology provides a detailed picture of sound-related flow in and around the wellbore. The new acoustic array technology provides the ability to generate two-dimensional (2D) images of flow-generated noise, which can then be used to extract both leak-depth and radial location. This 2D image is structured to show the makeup of the wellbore and, through a spectrum of color, displays the high flow area where the leak is passing through the tubular section and traveling. The image also identifies where no flow is observed for easy identification of flowing annuli.