Remote Respiratory Monitoring with the RESP® Biosensor
FDA 510(k) Cleared | CE Mark | HIPAA Compliant
Continuous Auscultation:
Novel Insights into Lung Health
Traditional auscultation with a stethoscope has been the gold standard for assessing lung health for over a century, with abnormal events, such as cough, wheezing, rhonchi, or crackles providing reliable indicators of blockage and other problems of the airways.
As auscultation has been limited to episodic, in-person exams, Strados Labs developed the RESP® Biosensor to extend the temporal and geographic range of stethoscopes, providing clinicians and researchers with greater insight into patient lung health.
Our respiratory monitoring technology, backed by numerous publications and awards, is used globally by life sciences companies to measure treatment effects and in healthcare settings to enhance patient monitoring and disease management.
Clients & Partners
The RESP® Biosensor: Wearable Device Overview
- Clinically validated accuracy, with equivalent performance to Littman 3200 electronic stethoscope¹
- Passive collection of lung sounds such as cough and wheeze and breathing patterns to offer objective data on respiratory status passively and remotely
- Patient-friendly form factor: wireless and discreet without interfering with daily life
1. M. Glass, et al. Results of Clinical Performance Testing of a Novel Wearable e-Stethoscope in Patients with Chronic Lung Diseases [abstract]. Am J Respir Crit Care Med
How It Works
An end-to-end solution for remote respiratory monitoring
RESP® Biosensor
Wearable respiratory monitoring device collects lung sounds and physiological data
RESP® Cloud
Captured data are securely and wirelessly transferred to HIPAA-compliant cloud for overread
Patient Reports
Once overread is complete, client receives detailed respiratory reports
Track Respiratory Status Objectively and Remotely
KEY FEATURES
- Passive & continuous
- HIPAA compliant
- Reimbursable under RTM & RPM codes
- 24+ hour battery life
- Rechargeable
- EHR/telehealth integration
- Flexible overread options, including human, semi-automated*, and full automated annotation*
*Machine learning algorithms not FDA cleared
Lung sound recordings captured by the biosensor are depicted visually as spectrograms
Cough
Wheeze
Crackles
OVERVIEW VIDEO
“With this information we can predict if the patient is doing better or worse and intervene ahead of time to prevent the patient from having major complications”
– Sadia Benzaquen, MD, Chair of Pulmonary Critical Care, Einstein Medical Center
Interested in a Research Collaboration?
We’re seeking clinical and academic collaborators to explore how our wearable respiratory monitoring device can improve patient outcomes. Partners may be eligible for in-kind support.
For Healthcare
Passive, remote respiratory monitoring to enhance disease surveillance and patient management
For Research
Gain stronger, objective insight into subject treatment response in drug development and clinical trials
Filling a Gap in Standard of Care
While pulmonary function tests, episodic auscultation, and patient-reported outcomes remain standard of care in respiratory care and research, RESP® addresses key limitations of each approach.
| RESP® Biosensor | Episodic Auscultation | Pulmonary Function Tests | Patient Reported-Outcomes | Traditional Cough Monitors | |
|---|---|---|---|---|---|
| Continuous | |||||
| Objective | |||||
| Passive | |||||
| Comprehensive | |||||
| Patient-Friendly |
Publications
View our clinical validation, whitepapers and case studies
Cough Detection Performance of the CoughCheck Algorithm in Subjects with Subacute Cough
Poster Presentation, American Thoracic Society, 2025
Strados Labs: An Efficient Process to Acquire and Characterize Clinically Validated Respiratory Information
Poster Presentation, IEEE Signal Processing Symposium, 2019
Results of Clinical Performance Testing of a Novel Wearable Stethoscope in Chronic Lung Diseases
Poster Presentation, American Thoracic Society, 2022
Let’s Connect
Explore using the RESP® Biosensor in your study or practice