UCLA

Biomedical devices in the past provided limited capability for the data acquisition and presented the data in the form of user interface for a care provider to observe. Now, what is required for biomedical devices has fundamentally changed. Many devices must now support secure networking and include a network of sensors to enable machine learning-based sensor fusion for accurate inference of the subject’s state.

This thesis introduces an Internet-of-Things (IoT) body area network (BAN) platform for medical devices that will provide rapid development capability with the assurance of security, networking, and the ability to host computationally intensive processes that are now required by medical devices. The BAN platform consists of seven wearable sensor nodes on the chest, wrists, upper legs, and ankles. Each sensor node includes sixteen general-purpose input/output (GPIO) pins, an analog-to-digital converter (ADC), two inter-integrated circuit (I2C) controllers, a serial peripheral interface (SPI), two universal asynchronous receiver transmitters (UART), and a universal serial bus (USB) on-the-go (OTG) to interface with sensors. The platform base model includes 9 degree-of-freedom inertial measurement unit (9DOF IMU) motion sensors, an electrocardiogram (ECG) sensor, a microphone, and a heart rate sensor. With its flexible interfaces, the platform is highly customizable and more sensors can be easily added.

Each sensor node features an IoT computer-on-module called the Intel Edison. The device can connect to expansion boards for rapid development. Although it has two official expansion options, the BAN platform uses boards from a third party manufacturer due to their small size. Intel provides a library to access the external interfaces. The library is fully compatible only if the Arduino breakout is used. A C library that abstracts /sys/class/gpio interface was developed to access the GPIO. The ADC device used in the platform is an I2C device. A C library was developed that abstracts the I2C communication between the Intel Edison and the ADC to provide an intuitive application program interface (API). The UART interface is accessible via /dev/ttyMFD2. A Python package called PySerial is used to interface the serial port. These interfaces in addition to the Intel’s official breakouts and library enable many more applications.

One of the most powerful features of the Intel Edison is its integrated WiFi module, enabling connection within the BAN and to the Internet. Since the BAN platform collects the user’s private health and activity data, the connection is secured by transport layer security (TLS). The networking among sensor nodes allows time synchronization with network time protocol (NTP) to have accurate sensor fusion.

Powered by its Intel Atom dual-core processors, the BAN platform can host neural network-based classifiers to monitor users’ states. From experiments, the performance of the neural network hosted on the platform was found to be on par with that of neural network implemented in Matlab.

The BAN platform was successfully distributed to freshmen, senior, and graduate IoT courses with exceptional assessment records. The IoT courses have shown that the students were able to rapidly develop fully functioning biomedical devices on the BAN platform.