Healthcare Can't Compromise on Response
Connected health demands intelligent monitoring that is responsive, scalable, and privacy-conscious. SensiML brings AI directly to wearable and connected health devices, enabling real-time insights from motion, audio, and biosensor data without relying on continuous cloud connectivity.
Key market drivers
- 61M+ Americans aged 65+ — and growing rapidly
- Remote patient monitoring market projected at $166B by 2030
- Digital physical therapy and rehabilitation growing at 13%+ CAGR
- HIPAA and global data privacy law blocking cloud-based patient monitoring
- Hospital systems mandating edge-first architecture for patient data
10 years
Human activity datasets and models developed
Nearly 10 years of biosensor model development across motion, audio, and activity domains — a compounding IP asset.
61M+
US adults aged 65+ needing ambient health monitoring
The addressable eldercare opportunity alone represents one of the largest IoT sensing markets globally.
On-device
Zero cloud exposure for sensitive patient data
HIPAA-compatible by design. Patient biometric and activity data never leaves the monitoring device.
Market Position & Proven Deployments
Eldercare & Aging in Place
SensiML's motion, audio, and biosensor models enable ambient monitoring systems that support independent aging without sacrificing privacy or burdening caregivers. Fall detection, daily activity recognition, medication adherence, and gait trend analysis operate entirely on low-power wearable and ambient sensor hardware — no camera, no cloud, no privacy trade-off. SensiML's user-adaptive models accommodate the natural variation in movement patterns across older adults, dramatically reducing the false alert rates that undermine trust in first-generation eldercare systems.
- Fall detection and prevention with on-device alerting — no cloud latency in an emergency
- Continuous gait trend analysis detecting early indicators of mobility decline
- Medication adherence and daily activity recognition for remote caregiver visibility
- User-adaptive models that reduce false positives across individual variation
Physical Therapy, Rehabilitation & Sports Medicine
SensiML pioneered motion analytics for clinical wearables — enabling physical therapists and exercise physiologists to deliver AI-powered movement assessment without data science expertise. The Analytics Toolkit empowers clinical teams to build sophisticated movement quality models trained on proper and improper form across specific therapeutic exercises. Validated models and labeled datasets in ergonomics, running gait, and sports motion are available for commercial license, compressing OEM development timelines from months to weeks.
- Physiologist-validated running gait analysis and automated remote coaching
- Ergonomics assessment models (box lift, posture, repetitive motion) for clinical and occupational use
- Sports motion classification for injury prevention and performance optimization
- Licensable clinical motion datasets across multiple rehabilitation domains
Healthcare Screening & Public Safety
Mass-scale health screening — whether for respiratory illness, emergency event detection, or first responder safety — demands real-time intelligence that cloud-dependent systems cannot reliably deliver. Network latency, outages, and bandwidth constraints are unacceptable failure modes in clinical and public safety environments. SensiML transforms commodity microphone and motion sensors into autonomous, intelligent event detectors — providing faster response, minimal network requirements, and HIPAA-compatible data handling in a single architecture.
- Respiratory illness screening from cough acoustic signatures
- Firefighter man-down detection and first responder activity monitoring
- Gunshot detection for hospital and clinical facility security
- Seismic vibration classification for emergency preparedness and response
Validated Use Cases
Eldercare
Fall Detection & Prevention
On-device motion analysis detecting falls and monitoring gait trends in eldercare and clinical wearables.
Rehabilitation
Running Gait Analysis
Physiologist-validated AI coaching for running biomechanics — deployed in consumer and clinical wearables.
Clinical Screening
Cough Detection
On-device acoustic classification detecting human cough events for respiratory health screening applications.
Occupational Health
Ergonomics Assessment
Real-time box lift and posture monitoring for occupational health and worker injury prevention.
Public Safety
Firefighter Activity Monitoring
Wearable activity recognition for first responders — detecting running, climbing, crawling, and man-down.
Clinical
Biosensor Pattern Recognition
Classification of EKG, EMG, and motion biosensor data for clinical decision support in wearable devices.
SensiML's healthcare edge AI position is strengthened by two compounding assets that are extremely difficult to replicate: nearly a decade of clinical and health motion datasets built with domain experts, and a HIPAA-compatible, on-device architecture that is increasingly mandated by healthcare institutions and patient privacy regulation. New entrants face both a data gap and a compliance burden — SensiML has solved both.