ENGS 89/90 Reports

Project Advisor

William Scheideler


Solomon Diamond and Rafe Steinhauer

Document Type


Publication Date



Introduction: Respiratory monitoring devices are vital to improving the efficacy of prescribed medications and treatments of patients with respiratory diseases such as asthma and Chronic Obstructive Pulmonary Disease (COPD). With an annual increase of respiratory disease diagnosis and fatalities, physicians and pharmaceutical companies are progressively more concerned with clinical trials dealing with the monitoring of medical respiratory treatment efficacy. However, there is a significant concern with the current methodology of monitoring this efficacy: absence of reliable constant surveillance data. This is due to modern wearable respiratory monitoring devices being unable to continuously identify the respiratory metrics of interest (wheezing, coughing and lung sounds) accurately. Therefore, the medical experts and the pharmaceutical companies rely solely on data from personal health records and medical health records, or on data provided by medical hearing experts through manual listening of the audio files recorded by a wearable monitoring device. These methods of data utilization and analysis for treatment insights are subject to potential human error. Therefore, conclusions about the baseline efficacy of the prescribed medications and treatments may not be as reliable as is necessary.

Innovation: Clairways’ respiratory monitoring device (“Antenna”) mitigates the risk of human error and provides the ability to obtain continuous and reliable respiratory data. The device is a band-aid sized wearable sensor that leverages deep learning to capture a picture of respiratory health and automatically transmits analytics and trends to a trial site for analysis. This functionality is due to the device's acoustic piezoelectric sensor which allows for a robust response to low amplitude noises. With this sensitivity, the sensor can accurately distinguish hard to identify noises such as lung sounds or wheezing. Currently, the sensor is highly susceptible to background and residual noises, which greatly impacts its ability to accurately differentiate these hard-to-identify noises. Ultimately, this means that the device is not functional. Our team aims to transform Clairways’ Antenna design to improve the piezoelectric sensor’s signal integrity and produce a functional device that can report reliable monitoring data.

Approach: To achieve our goal, we decided to focus our efforts exclusively on the device’s enclosure design with an emphasis on the portion surrounding the piezo sensor, as modifying the circuitry is beyond the scope of the project. We then divided into two subgroups based on our interests and skill sets: Mechanical and Electrical. By employing the divide and conquer approach, we were able to streamline efforts and obtain results more efficiently. Each subgroup had two main objectives:

Mechanical: Objective 1: Establish key materials and design specifications for the device. Through secondary research and experimental testing, it was determined that the enclosure should be made of ABS or PC (dimensions: 2” x 1.1” x 0.3”), the internal damping material be made of sorbothane oo-70 (1⁄8”), the piezo and wires be encased in silicone (1⁄8”), and the piezo be isolated from the battery and PCB (1⁄4” separation). These materials and the design will work in conjunction to improve the overall signal integrity for improved data quality. Objective 2: Iterate the prototypes to reach a final proposed solution. In order to improve ease-of-assembly and minimize the weight, we are developing an enclosure model that does not require screws and can be opened and closed easily for convenient access to internal components. Through qualitative testing of ease-of-access, we have determined that a “snap-fit” model meets these objectives. A mold was created to encase the piezo sensor and the wires in silicone. The silicone-covered wires will connect the piezo and PCB to maintain separation, protection, and flexibility. This hybrid design will be validated through shear, transverse tapping, and sensitivity testing.

Noise Analysis: Objective 1: Establish baseline characteristics of the piezo sensor and define potential sources of noise. In this aim, we focused on identifying key characteristics of the piezo sensor such as Signal-to-Noise ratio (SNR), Contrast-to-Noise ratio (CNR), frequency response, etc., to use as a baseline to compare results from our mechanical modifications. We also characterized potential sources of noise that could affect the piezo sensor. Objective 2: Test effectiveness of mechanical modifications. In this aim, we focused on testing the effect of the modifications made by the mechanical team on the characteristics of the piezo sensor against the current monitoring device baseline characteristics. We replicated the tests used to obtain the baseline data, and compared results to determine the effectiveness of various modifications

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