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Last Modified: 05/11/20
Data we are collecting includes coughing sounds as recorded and/or submitted by you through the survey and the following anonymous data:
SensiML does not collect, capture or retain any identifiable personal information about you through this survey.By completing the online survey submission form and clicking the “Submit Data” button, you consent to allowing SensiML to use the data that you have provided. You further represent that you are a resident of the United States and that you are of legal age to provide consent to use the data.
Your data will be aggregated with all other user-submitted data, processed, and analyzed by SensiML researchers, and published as an open source public dataset for the intended benefit of other researchers as well.
This survey is NOT intended to provide medical advice. IF YOU ARE EXPERIENCING A MEDICAL EMERGENCY, CALL 911 IMMEDIATELY.
None of the participants in this survey will be provided with any results or findings beyond aggregated results and findings that may be published on our public website or by third parties using our publicly shared aggregated dataset.
If you have a question about this policy, you must submit your question in writing to us at email@example.com with the filename of the WAV file you submitted.
Q1) Why is SensiML collecting this data?
A: AI technology is a powerful tool for finding patterns in rich and/or large sets of data. Our company has worked across a broad set of sensor data applications since our inception in 2012 as an Intel project team applying AI to wearable computing and industrial sensing prior to spinning out to form SensiML in 2017.
Whether detecting faulty pumps, bearings, and machinery, or analyzing cough audio data for the subtleties to differentiate suspected COVID-19 disease, AI relies on high quality training data to produce good results. Training data is a combination of readily measurable source sensor data combined with labeled outcomes (so-called ‘ground truth’) typically not so easy to obtain. Together this data is used to teach the AI algorithm how to predict future data without having to be supplied the ground truth. It’s for this reason that we ask for both the cough sound itself and a set of basic questions to help establish ground truth to properly train the AI algorithm for subsequent insight.
Q2) Who is SensiML and what is its expertise in COVID-19 detection?
A: SensiML is a software company specializing in AI tools for developers of smart sensor devices. Thus while you may not have heard of us directly, you’ve no doubt heard of many of the companies who use our AI software to build intelligence into their wearable and IoT products. Our origins are also well recognized having spun out in 2017 from Intel Corporation where the progenitor of our current software toolkit supported intelligent sensing for Intel Curie and Quark SE microcontrollers. That said, we are not pulmonologists nor epidemiologists. We rely on our academic and healthcare partners for underlying medical domain expertise as required.
Q3) What will you do with any data I provide?
A: Our intent is to quickly collect a sufficiently large dataset to support good AI science in the application of sound analysis for symptom classification. Our envisioned usage for this application is as a decision support tool for clinical diagnostic testing and/or to aid in the screening of suspected infectious people in high-risk environments. If you share our desire to get to better tools to help re-open economies safely, we invite you to participate in our effort by supplying data.
Those who do provide data should know that we:
Q4) What if I don’t feel comfortable answering some or all of the questions?
A: It’s entirely up to you to decide what data you are comfortable sharing (or even to share anything at all). Certainly, the more complete the survey, the more useful it is for training either our or other’s AI algorithms, but even partial responses can be of use.
Q5) When will SensiML be publishing the open source dataset?
A: Once we have reviewed and filtered the samples provided (we will reject any audio files other than those containing three coughs) and concluded there is statistical significance to the aggregate sample size, then we will publish the dataset. We will publish on datadepot.sensiml.com so you may check periodically to see when it is published. We will not be collecting names nor can we proactively follow-up with participants to let them know. Thank you for your understanding.
By default, browsers will typically prompt you for permission to use your microphone for recording with sites requesting this. This is a security/privacy provision to protect you, but it can potentially cause problems for our recording tool on this site. Should you get the following error…
… make sure you answer “Allow”
In other cases, your browser may be configured to disallow any use of the microphone by default. This is dependent on your security settings and varies by browser. If you are not prompted to allow use of the microphone, look in your browser settings for privacy and security options and temporarily enable the microphone. Below is an example for where to find this setting in the Firefox browser:
Uncheck the box below(temporarily) to allow the recording.
If all else fails, you can collect audio data through a separate audio recording application so long as that application supports creation of WAV files at 48kHz 16-bit mono uncompressed PCM format.
The location of the audio file you recorded and saved locally will depend on where your browser normally saves downloaded website files.
For the Chrome Desktop browser, the files are usually visible in a footer bar at the bottom of the browser like this:
Ensure the file you select after clicking matches that shown above.
For the Microsoft Edge browser, the audio files by default are saved without a .WAV extension.
The downloaded file needs to be renamed to be a .WAV file type before it can be successfully uploaded:
With file now renamed as a valid .WAV audio file, it can now be selected in “Choose File” and uploaded successfully.
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