Icon Legend

This session is not in your schedule.

This session is in your schedule. Click again to remove it.

Device Technologies and Biomedical Robotics

Device Technologies and Biomedical Robotics - Poster Session D

Poster I20 - Real-Time Physiological Data Recording and Plotting Using Wearable Devices with Lab Streaming Layer

Friday, October 25, 2024

3:30 PM - 4:30 PM EST

Location: Exhibit Hall E, F & G

Presenting Author(s)

Aislinn M. Jones (she/her/hers)

Undergraduate Research Assistant
George Mason University Bioengineering Department, Virginia, United States

Co-Author(s)

Connor M. Delaney, MS (he/him/his)

Graduate Research Assistant and Doctoral Student
George Mason University Bioengineering Department
Alexandria, Virginia, United States

Primary Investigator(s)

Nathalia Peixoto, PhD

Associate Professor
George Mason University Bioengineering Department, United States

Co-Author(s)

Connor Grimburg, BS

Undergraduate Research Assistant
George Mason University Bioengineering Department
Herndon, Virginia, United States

Introduction: Researchers utilizing physiological sensors are limited by the functionality of proprietary recording systems. While these recording systems are nearly always FDA approved, they often come with software requirements that lack interoperability with other recording devices. This constrains research that relies on flexibility and innovation. Furthermore, the recording systems for many physiological sensors are limited to the laboratory setting. To address these concerns, we developed a wireless and wearable system for real-time physiological data recording and visualization. Our system integrates multiple data streams from three devices by leveraging Lab Streaming Layer (LSL) to synchronize the data efficiently.

The recording system is managed by a desktop or laptop computer, which handles the collection of data streams recorded from sensors into LSL. Data is recorded using standard LSL format which includes measurements recorded in a time series and corresponding time stamps. This allows for plotting time stamps vs. time series to visualize the streams synchronized in real-time together (see Figure 1).

Materials and

Methods: Three recording devices were employed: the Muse 2 headband, the Empatica E4 wristwatch, and the HTC Vive VR Headset. The Muse headband records electroencephalography (EEG), photoplethysmography (PPG), acceleration, and gyroscope data. Muse data is transmitted wirelessly through Bluetooth into the PC and LSL by using the BlueMuse software. The Empatica wristwatch captures PPG, skin temperature, electrodermal activity (EDA), acceleration, and instantaneous heart rate calculations. The Empatica data is sent wirelessly to the PC, processed, and sent to LSL by a Python program. This program also interfaces with the BlueMuse software to automatically reconnect any sensors which temporarily lose connection from the computer (e.g. when a device loses battery or exits Bluetooth range). The HTC Vive VR headset and remotes are used to track movement through Unity. Furthermore, Unity can be used as a stimulus presentation system through the display of the VR headset, as well as record the position of objects and timing of trials in LSL.

Data storage and visualization in real-time were managed using a custom graphical interface in MATLAB, which plots data from all or a subset of the sensors in real time. Simultaneously, the Lab Recorder software is utilized to save the data for post hoc analysis as a .xdf file on the PC. This system aims to enhance the portability and usability, making it more feasible to operate.

Results, Conclusions, and Discussions: This system demonstrated strong performance in synchronizing and visualizing multiple physiological data streams in real time . The wireless and wearable aspects improve the usability and flexibility, making the system more suitable for diverse research environments. Real-time visualization enabled immediate insights into physiological responses during experiments, facilitating timely interventions and adjustments to maintain a high quality of recordings.

The integration of multiple devices without a limit on the number of data streams, constrained only by the computer's processing capacity, highlights the system's adaptability. This flexibility offered by utilizing LSL allows researchers to incorporate new devices seamlessly, overcoming the limitations of proprietary systems. By developing and incorporating the real-time plotter into this recording ecosystem, it is possible for researchers to check the quality of recordings before experiments begin and inspect data the moment it is recorded. This is critical as outside of laboratory settings, many more sources of recording error or interference are present.

Our system highlights the potential of real-time, portable, flexible, and robust systems to change how physiological data is recorded and analyzed, by providing a user-friendly, adaptable solution for researchers. Future work will focus on further optimizing the system's performance and expanding its capabilities to include additional wearable devices and data streams. Because LSL is open source, other researchers may also expand upon this work or utilize components of our system such as the real-time plotter. This approach sets us up for a more comprehensive and integrative physiological research process, enhancing our understanding of complex physiological phenomena.

Acknowledgements (Optional):