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Guangyi Zhao
Guangyi Zhao

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Elder-Sentinel: Pose-Based Action Recognition for Elder-Care Safety Monitoring

#ai #python #opensource

Pose-based action recognition for elder-care safety monitoring.

A real-time pipeline that watches body movement — not identity — and flags safety-relevant events: help-seeking gestures, wandering into off-limits areas, prolonged inactivity that may signal a problem, and falls. Built for the realities of care homes, where caregivers can't be in every room at once, and where a fall detected minutes earlier can save a life.

Project status: scaffold v0.1 (honest edition).
The end-to-end pipeline runs today on procedurally generated, simulated skeleton sequences. What is real: pose data model, rule-based action detectors (hand-raise counting, loitering, fall alarm), event reporting.
What is still TODO (clearly marked in code): the field-validated action thresholds and the MediaPipe live-camera backend. This repo contains no face recognition and no identity matching — pose only.

Background

In a care home, caregivers can't watch every room at once. This system defines measurable action standards — e.g. "hand raised above shoulder N times in M seconds" as a call-for-help signal, "resident stationary near a stairwell or exit for T seconds", "sudden vertical drop consistent with a fall" — and raises alerts the moment a live pose stream matches one. The same architecture applies anywhere safety depends on body movement without identifying the person: elder-care fall detection, hospital ward monitoring, assisted-living wandering alerts.

What it does

Module Input Output
Pose estimation camera frame (or simulated skeleton) SkeletonFrame joint coordinates
Hand-raise counter wrist/shoulder trajectories count + timestamps per side
Loitering detector body centroid trajectory loiter_alert after dwell threshold
Fall detector hip-height trajectory fall_alarm (critical) on sudden drop
Event reporter all detector outputs console summary + JSON report

Architecture 

                +------------------+
                |  Pose source     |
                |  MediaPipe cam   |  (TODO: live backend)
                |  Simulated seq   |  <-- demo runs on this
                +--------+---------+
                         | SkeletonFrame stream
          +--------------+--------------+
          |              |              |
   HandRaiseCounter LoiteringDetector FallDetector
   (config thresholds — TODO: field values)
          |              |              |
          +--------------+--------------+
                         v
                  EventReporter
                  console + report.json
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Quickstart 

cd Elder-Sentinel
pip install -r requirements.txt
python examples/demo.py        # simulated end-to-end demo
python -m pytest tests/ -q     # unit tests
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The demo runs three simulated scenarios — hand raises, loitering, a fall — and writes examples/demo_report.json.

Configuration

All action thresholds live in config/action_thresholds.yaml. The shipped values are placeholders, not standards:

hand_raise:
  raise_margin: 0.05   # TODO(ezra): field-validated value
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TODO(ezra): replace every threshold with values validated from your real deployments (raise margins, dwell times, drop ratios, zone geometry). The detectors are deliberately dumb rules — your experience is what makes them trustworthy. Do not present placeholder values as operational standards.

Live camera backend (TODO)

pose/mediapipe_backend.py is a stub. To go live:

  1. pip install -r requirements-optional.txt (MediaPipe + OpenCV)
  2. Implement MediaPipeBackend.estimate() mapping the 33 MediaPipe pose landmarks onto this repo's joint set, emitting SkeletonFrame(simulated=False).

Ethics & lawful use

  • Pose only. This repository contains no face recognition, no identity matching, and no person re-identification. It answers "what is the body doing", never "who is this".
  • Authorized safety use only. Intended solely for lawful, authorized safety deployments (e.g. licensed elder-care facilities, with family/guardian consent). Do not use for covert surveillance, and do not deploy without proper legal authorization and oversight.
  • Simulated data. Every demo frame is procedurally generated and labeled simulated: true. Nothing here was recorded from real people.

Roadmap

  • [ ] Field-validated thresholds (Ezra's deployment experience)
  • [ ] MediaPipe live backend + zone polygons on floor plans
  • [ ] Multi-person tracking (ID by track, not by face)
  • [ ] Alert routing (SMS/dashboard webhook)
  • [ ] Recurring-pattern analytics (e.g. agitation escalation over hours)

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