AUTONOMOUS HEALTHCARE INC. — National Aeronautics and Space Administration SBIR Phase I: H12
AUTONOMOUS HEALTHCARE INC. — SBIR Phase I award from National Aeronautics and Space Administration.
Phase I SBIR feasibility signal
- Phase I awards fund proof-of-concept work. For capture teams, they mark early interest from National Aeronautics and Space Administration in a technical approach.
- Watch for Phase II follow-ons from the same firm/topic family — that conversion path is where budgets and transition pressure rise.
- Obligated amount $124,989. Cross-check similar awards in the same agency and technology tags for going-rate context.
- Topic code H12 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $124,989
- Agency
- National Aeronautics and Space Administration
- Program / Phase
- SBIR · Phase I
- Topic
- H12
- Solicitation
- SBIR_19_P1
- NAICS
- —
- Place of performance
- NJ
- Period
- 2019-08-19 → 2020-02-18
Description
Cardiopulmonary monitoring is of critical importance in a variety of clinical and non-clinical applications ranging from monitoring physiological conditions of crew members during space missions to emotion and stress recognition in applications involving human-machine interaction. Current solutions involve attaching gel-based electrodes for electrocardiogram (ECG) monitoring and pulse oximetry sensors connected to fingertips or earlobes for photoplethysmography (PPG) monitoring. Gel-based electrodes require preparation and their application can cause skin irritation. In addition, the use of current contact-based solutions is further complicated by the fact that a relatively large device such as a Holter monitor has to be carried by the subject at all times. Wearable sensors are a step in the right direction, yet the sensor needs to be continuously worn (on the wrist, chest, etc.) by the subject.nbsp;We propose to build on our prior research experience in non-invasive remote cardiopulmonary monitoringnbsp;as well as computer vision and machine learning to develop anbsp;non-invasive cardiopulmonary monitoring systemnbsp;and extract clinically important information fromnbsp; multiple subjects in the field of view. Specifically, our proposed sensing framework involves i) an optical camera; ii) a depth-sensing camera, iii) a Doppler radar-based solution; and iv) a sensor fusion component for integration of data received by multiple sensing modalities.