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

Signals: Milestone gates met; measurable result reported; documentation; deployment or evidence reachable; reflective log

Functionality & requirements30
Technical quality & architecture25
Documentation15
Deployment & evidence15
Innovation10
Presentation5

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.