# Baby Physical Safety Monitoring in Smart Home Using Action Recognition System

arXiv: https://arxiv.org/abs/2210.12527 · HTML: https://mlsanity.com/paper/2210.12527/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 22 Oct 2022.

## Builds on (most cited first, 15 of 15)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [Temporal Relational Reasoning in Videos](https://mlsanity.com/paper/1711.08496/) (2017)
- [Temporal Segment Networks for Action Recognition in Videos](https://mlsanity.com/paper/1705.02953/) (2017)
- [The AVA-Kinetics Localized Human Actions Video Dataset](https://mlsanity.com/paper/2005.00214/) (2020)
- [VideoMix: Rethinking Data Augmentation for Video Classification](https://mlsanity.com/paper/2012.03457/) (2020)
- [Transfer Learning for Speech and Language Processing](https://mlsanity.com/paper/1511.06066/) (2015)
- [Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification](https://mlsanity.com/paper/1812.07683/) (2018)
- [Transfer Learning for Speech Recognition on a Budget](https://mlsanity.com/paper/1706.00290/) (2017)
- [Review on Action Recognition for Accident Detection in Smart City Transportation Systems](https://mlsanity.com/paper/2208.09588/) (2022)
- [Reduced-Gate Convolutional LSTM Using Predictive Coding for Spatiotemporal Prediction](https://mlsanity.com/paper/1810.07251/) (2018)
- [Transfer Learning for Action Unit Recognition](https://mlsanity.com/paper/1807.07556/) (2018)
- [A Review of Network Evolution Towards a Smart Connected World](https://mlsanity.com/paper/2105.13964/) (2021)

## Built on by (most cited first, 4 of 4)

- [Smart City Transportation: Deep Learning Ensemble Approach for Traffic Accident Detection](https://mlsanity.com/paper/2310.10038/) (2023)
- [LiteLSTM Architecture Based on Weights Sharing for Recurrent Neural Networks](https://mlsanity.com/paper/2301.04794/) (2023)
- [Big Data and Deep Learning in Smart Cities: A Comprehensive Dataset for AI-Driven Traffic Accident Detection and Computer Vision Systems](https://mlsanity.com/paper/2401.03587/) (2024)
- [Pre-trained Under Noise: A Framework for Robust Bone Fracture Detection in Medical Imaging](https://mlsanity.com/paper/2507.09731/) (2025)

## Abstract (arXiv)

Humans are able to intuitively deduce actions that took place between two states in observations via deductive reasoning. This is because the brain operates on a bidirectional communication model, which has radically improved the accuracy of recognition and prediction based on features connected to previous experiences. During the past decade, deep learning models for action recognition have significantly improved. However, deep neural networks struggle with these tasks on a smaller dataset for specific Action Recognition (AR) tasks. As with most action recognition tasks, the ambiguity of accurately describing activities in spatial-temporal data is a drawback that can be overcome by curating suitable datasets, including careful annotations and preprocessing of video data for analyzing various recognition tasks. In this study, we present a novel lightweight framework combining transfer learning techniques with a Conv2D LSTM layer to extract features from the pre-trained I3D model on the Kinetics dataset for a new AR task (Smart Baby Care) that requires a smaller dataset and less computational resources. Furthermore, we developed a benchmark dataset and an automated model that uses LSTM convolution with I3D (ConvLSTM-I3D) for recognizing and predicting baby activities in a smart baby room. Finally, we implemented video augmentation to improve model performance on the smart baby care task. Compared to other benchmark models, our experimental framework achieved better performance with less computational resources.