Sensoriis, Inc

History & Vision

Sensoriis, Inc. was formed in Seattle in 2016 by co-founders Michael Lau, MD, MBA and Hung Cao, PhD to commercialize innovative sensors for biomedical applications. 

The co-founders bring to the company complementing skill sets, expertise, and experience in the science and technology of advanced sensors, biomedical and clinical sciences, and clinical trials for regulatory approvals; as well as a rich medical and technological devices startup experience track record.

The company is privately funded, but  has been partially supported by the National Institutes of Health Phase I and II Small Business Technology Transfer/Small Business Innovation Research (STTR/SBIR) grants, as well as other institutional development grants.

The co-founders of Sensoriis envision the company becoming a premier enterprise in sensor technologies for digital-health, telehealth and telemedicine, as well as for drug and natural products development research infrastructure. 

The Phase II SBIR grant allows for the rapid development, during Phase II, of the pipeline products for drug and natural products development for the zebrafish Electrocardiography/Electroencephalography (ECG/EEG) platform. 

Concurrently, the company has been, and will continue to develop expertise and intellectual property in non-contact electrode (NCE) sensors to capture biophysiological potentials of low amplitude such as zebrafish ECG/EEG, human EEG and fetal ECG with optimized signal to noise ratio (SNR). Such expertise in optimizing SNR for innovative NCE sensors, and the development of  the mobile data management system, would enable Sensoriis to gain an important position  in the tele/digital health sphere. 

The Sensoriis technology platforms enable the collection of digital input from physiological sensors, the ability to effectively and securely transfer the data for processing in the computing cloud, and the ability to analyze and extract the relevant information from the data in real-time using machine learning—specifically transfer learning.

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