# Unsupervised Keypoints for Real-Time Fall Detection: Comparative Analysis Under Real-world Conditions with Predictive Bandwidth Reduction

arXiv: https://arxiv.org/abs/2607.15400 · HTML: https://mlsanity.com/paper/2607.15400/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 54 references that are in the map.
- First posted on arXiv 16 Jul 2026.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [A Recurrent Latent Variable Model for Sequential Data](https://mlsanity.com/paper/1506.02216/) (2015)
- [Unsupervised Learning of Object Landmarks through Conditional Image Generation](https://mlsanity.com/paper/1806.07823/) (2018)
- [Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation](https://mlsanity.com/paper/1804.06055/) (2018)
- [Unsupervised Learning of Object Structure and Dynamics from Videos](https://mlsanity.com/paper/1906.07889/) (2019)
- [Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose](https://mlsanity.com/paper/1811.12004/) (2018)
- [2D Human Pose Estimation: A Survey](https://mlsanity.com/paper/2204.07370/) (2022)
- [Remote patient monitoring using artificial intelligence: Current state, applications, and challenges](https://mlsanity.com/paper/2301.10009/) (2023)
- [Sensor Data Augmentation from Skeleton Pose Sequences for Improving Human Activity Recognition](https://mlsanity.com/paper/2406.16886/) (2024)
- [Towards the extraction of robust sign embeddings for low resource sign language recognition](https://mlsanity.com/paper/2306.17558/) (2023)
- [Continuous Patient Monitoring with AI: Real-Time Analysis of Video in Hospital Care Settings](https://mlsanity.com/paper/2412.13152/) (2024)
- [Towards Efficient Real-Time Video Motion Transfer via Generative Time Series Modeling](https://mlsanity.com/paper/2504.05537/) (2025)
- [Inference-time Stochastic Refinement of GRU-Normalizing Flow for Real-time Video Motion Transfer](https://mlsanity.com/paper/2512.04282/) (2025)

## Abstract (arXiv)

Falls among older adults are a major safety and health-systems challenge, yet continuous in-person monitoring is difficult to sustain across home and clinical care settings. Video-based monitoring can capture fall-relevant motion, but scalable real-time deployment is limited by privacy, compute, and bandwidth constraints, and existing keypoint-based methods typically rely on supervised or anatomical pose representation, which is vulnerable to occlusion and partial body visibility. We propose a fall-monitoring framework that replaces continuous video transmission with compact motion representations, using unsupervised keypoints which are extracted locally, and a variational recurrent model is used to forecast motion at the staff end, followed by fall classification. We evaluate the framework on the UR Fall and Human Fall datasets under random, subject-disjoint, and occlusion-based splits to systematically characterize when each representation has an advantage. We find that random splits do not discriminate between representations, and under subject-disjoint evaluation no uniform advantage emerges; performance varies across held-out subjects with their visual characteristics. Under occlusion, however, unsupervised keypoints substantially outperform supervised keypoints, retaining strong detection sensitivity where supervised keypoints miss approximately half of falls; this advantage reflects their anatomical independence and persists under bandwidth-constrained prediction. The unsupervised detector also requires roughly two orders of magnitude less computation, supporting privacy-preserving, bandwidth-aware, always-on fall monitoring.