AdvancedSoftware trackUS-CP-39

Edge AI Fall Detection for Senior Living

Internship role: Embedded ML Intern. Detect falls on-device from camera pose data without streaming video, with alerting and a privacy impact note; measured by sensitivity and false alarms per day.

10 Guided Steps480h Internship TierSubmit: GitHub repo or evidence pack + live demo / report + README + milestone logbookVerified CertificateAI Rubric Graded

Program Overview

Internship role: Embedded ML Intern. Detect falls on-device from camera pose data without streaming video, with alerting and a privacy impact note; measured by sensitivity and false alarms per day.

What you submit

GitHub repo or evidence pack + live demo / report + README + milestone logbook

Stack: Raspberry Pi or Jetson, TensorFlow Lite, pose estimation

How it is graded

Works as specified30
Every feature in the brief runs end to end with real data, and the reviewer can follow the README to try it.
Code quality & design20
Clear structure, sensible names, input validation, no secrets in the repo, and a short note on the main design choices.
Testing & evidence20
Automated tests for the core paths, plus a live URL, screenshots or a short demo video.
Documentation15
README covers purpose, setup, how to run tests, and known limits.
Reasoning & presentation15
The write-up explains trade-offs and results in plain language, with numbers where they exist.

Pass 60% · Merit 75% · Distinction 90%

Learning Outcomes

480-hour United States programme. US senior-living operators are piloting privacy-preserving monitoring as the population ages.

Target Audience

Engineering Students

Diploma, B.Tech, B.E., BCA, MCA, or M.Tech students seeking practical experience.

Aspiring Developers

Developers who want to build proof-of-work capstone projects for tech resumes.

Job Seekers

Graduates looking for AI-evaluated code review & verified industry credentials.