Recent advances in telecommunications, microelectronics, and sensor manufacturing techniques—especially with miniature circuits, microcontroller functions, front-end amplification, and wireless data transmission—have created substantial possibilities for using wearable technology for biometric monitoring.
These physical, chemical, and biological sensors allow for continuous recording of health data, allowing for powerful analytics to monitor and predict patient condition, activity, location, etc. Furthermore, the passive nature of data collection from sensors unlocks the door for big data in health care, and subsequently, artificial intelligence (AI) in medicine.
The success of AI applications has hinged on the availability of big data, a feature not commonly found in health care. Wearable sensors will generate big data, providing the opportunity for AI to revolutionize many medical fields, such as assisted care, management of chronic diseases, as well as the development of so-called smart clinic.
Currently, we are working on the development of a Real Time Location System (RTLS) that tracks the locations of patients, clinical staff, and assets in real time to greatly improve the clinical workflow and patient safety. The system is constructed with a sensor network based on Bluetooth Low Energy (BLE) technology.
Traditional locating algorithms rely on characterizing the node-tag signal attenuation curves, and then using trilateration, multilateration, or triangulation to obtain the tag coordinates. We found that by using these more conventional methods, the tracking accuracy was unable to satisfy the clinical requirements.
We now look toward using deep learning methods to solve the RTLS problem at higher precision previously unobtainable by the established techniques. By assigning the node signal heat maps across the building as inputs, it is possible to train a deep learning model to identify tag locations.
Backboned by this node network, the long-term goal of this project is to develop an IoT system that collects and processes large sets of patient data via multi-function tags—an AI-assisted “smart clinic.”
- Iqbal, Z., Luo, D., Henry, P., Kazemifar, S., Rozario, T., Yan, Y., ... & Jiang, S. (2017). Accurate Real Time Localization Tracking in A Clinical Environment using Bluetooth Low Energy and Deep Learning. arXiv preprint arXiv:1711.08149. (arXiv)