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Research Internship in Remote Monitoring and Anomaly Detection for Aging in Place

In this internship you will explore how remote monitoring equipment can be used to discover the normal patterns of elders at home as well as detect anomalies in their behavior to support aging-in-place paradigms. Sensors will be deployed at non-intrusive locations (e.g., a switch sensor to determine whether the front door is open or closed, amp meters measuring currents of stove tops, passive infrared motion sensors, occupancy sensors, light sensors, audio level sensors, etc.) to collect data. The data will be analyzed to determine baselines, to allow discovery of trends, and to understand whether there are potentially unsafe conditions. You will help to implement a sensor data acquisition and streaming service as well as services for visualizing and analyzing the collected data. Privacy of occupants is a primary concern and will be considered as technologies are designed.  

The tasks include:
  1. Identify and select wireless event-triggered sensors of interest.
  2. Write the custom sensor data acquisition module in python using Sensorium SDS library API.
  3. Write the custom sensor data streaming module in python using Sensorium SDS app.
  4. Define a set of analytics that can infer insights from the sensor data.
  5. Write an analytics application which subscribes to the Sensorium broker and generates a report or visualization. This can be a standalone python script, or a web application run on a browser.

We hope to summarize our findings in a peer-reviewed publication.

Background required:
  • Enrolled in MS or Ph.D. program in mechanical engineering, electrical engineering, or computer science.
  • Solid Python programming skills.
  • Hands-on experience using and administrating Raspberry Pi computers.
  • Self-driven individual.
  • Good verbal and written communication skills.

These additional skills are desirable:
  • Experience with designing algorithms for data analytics.
  • An understanding of model-based fault detection and diagnosis.
  • Experience using wired and wireless sensors.
  • Experience using cloud-based hosting services (AWS, Azure, etc.).
  • Web development skills, such as JavaScript, CSS, and HTML
  • Programming language skills C and C++.

Note: Start and end dates are negotiable.