# Personalized Fall Detection by Balancing Data with Selective Feedback Using Contrastive Learning

arXiv: https://arxiv.org/abs/2603.17148 · HTML: https://mlsanity.com/paper/2603.17148/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 17 Mar 2026.

## Builds on (most cited first, 7 of 7)

- [Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss](https://mlsanity.com/paper/2106.04156/) (2021)
- [MAVERIC: A Data-Driven Approach to Personalized Autonomous Driving](https://mlsanity.com/paper/2301.08595/) (2023)
- [Meta-HAR: Federated Representation Learning for Human Activity Recognition](https://mlsanity.com/paper/2106.00615/) (2021)
- [CZU-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and 10 wearable inertial sensors](https://mlsanity.com/paper/2202.03283/) (2022)
- [DeePLT: Personalized Lighting Facilitates by Trajectory Prediction of Recognized Residents in the Smart Home](https://mlsanity.com/paper/2304.08027/) (2023)
- [A Cosine Similarity-based Method for Out-of-Distribution Detection](https://mlsanity.com/paper/2306.14920/) (2023)
- [Personalized fall detection monitoring system based on learning from the user movements](https://mlsanity.com/paper/2012.11195/) (2020)

## Abstract (arXiv)

Personalized fall detection models can significantly improve accuracy by adapting to individual motion patterns, yet their effectiveness is often limited by the scarcity of real-world fall data and the dominance of non-fall feedback samples. This imbalance biases the model toward routine activities and weakens its sensitivity to true fall events. To address this challenge, we propose a personalization framework that combines semi-supervised clustering with contrastive learning to identify and balance the most informative user feedback samples. The framework is evaluated under three retraining strategies, including Training from Scratch (TFS), Transfer Learning (TL), and Few-Shot Learning (FSL), to assess adaptability across learning paradigms. Real-time experiments with ten participants show that the TFS approach achieves the highest performance, with up to a 25% improvement over the baseline, while FSL achieves the second-highest performance with a 7% improvement, demonstrating the effectiveness of selective personalization for real-world deployment.