Advantages
- Provides continuous, real-time patient monitoring from home
- Uses compact, low-cost IoT devices designed specifically for sleep monitoring in cancer patients
- Enables predictive analysis of sleep apnea using machine learning and longitudinal data
- Improves comfort and accessibility over traditional in-lab sleep studies
Summary
Cancer patients often face hidden comorbidities like sleep-disordered breathing, which can go unnoticed and worsen outcomes during treatment. Traditional sleep monitoring methods like polysomnography are invasive, limited to single-night tests, and uncomfortable especially for patients already undergoing intensive care.
This smart IoT system solves that by enabling at-home, continuous monitoring through wearable, compact sensors paired with cloud analytics. Designed for cancer patients, the device is easy to use and collects vital signals like SpO2 specifically during the cancer treatment time. The system uses Apache Spark and Kafka for real-time processing and enables physicians to detect early signs of worsening conditions. With strong test results across simulator and real-patient trials, this system offers a cost-effective, patient-friendly way to improve cancer care.
This dashboard provides real-time access to a patient’s physiological data, allowing healthcare providers to track changes over time and act quickly helping improve care during cancer treatment.
Desired Partnerships
- License
- Sponsored Research
- Co-Development