Real-Time Resilient Physiological Sensor Communication Architecture

This project focuses on developing a resilient real-time physiological sensor communication architecture that can collect data from multiple sensors to be processed by a robot to make real-time decisions. The architecture needs to provide reliable collection of the sensor data, that minimizes sensor data loss, and maximizes connectivity.  If the sensors are unable to maintain connectivity, and the system is unable to successfully reconnect to the sensors, alerts to the sensor loss must be provided to the system users (e.g., experimenters).

Objectives


  1. A hardware/software architecture that integrates all system components.
  2. The implemented hardware/software architecture integrating all system components.
  3. Validation of the architecture’s real-time performance.
  4. Sensor Status User Dashboard 

Motivations


Future human-robot teams deployed in unstructured, uncertain and dynamic environments will require robots to obtain real-time data from human worn sensors to understand the human’s current task and performance. This sensor data is used on board the robot to estimate the task and the human’s workload to support the robot making domain and task appropriate adaptations. Dropped sensor data results in errors in the estimated task and workload levels, which negatively impacts the ability to predict future human states and the robot’s ability to adapt in a useful manner.

Qualifications


Minimum Qualifications:
  1. Fluent with Python programming
  2. Network Communications Expertise

Preferred Qualifications:
  1. Fluent with C programming language


Details


Project Partner:

Julie Adams

NDA/IPA:

No Agreement Required

Number Groups:

1

Project Status:

Accepting Applicants

Keywords:
CPythonHardwareNetworkingReal-Time Signal ProcessingConsultancy
Card Image Capstone