Human Activity Recognition Using Deep Learning

Abstract

Human Activity Recognition (HAR) focuses on the detection and classification of human movements using data collected from various sources, including video and wearable sensors. This paper presents a deep learning-based approach for identifying daily activities such as walking, running, sitting, and standing by utilizing accelerometer and gyroscope sensor data. HAR plays a vital role in domains like healthcare, smart environments, and fitness tracking. The study employs machine learning (ML) and deep learning (DL) models to address the challenges of data variability and real-time processing. As artificial intelligence and pervasive computing continue to evolve, HAR systems are becoming more precise, efficient, and scalable for practical applications.

Introduction

Overview:
Human Activity Recognition (HAR) is a key area in computer vision focused on identifying human actions—from simple gestures to complex tasks—using sensor and video data. Recent advances in artificial intelligence and deep learning have significantly improved HAR accuracy while reducing manual effort.

Key Contributions:

1. Literature Survey:

• Agarwal (1997): Used model-based techniques to track body movements.

• Wang (2003): Combined shape and motion features for real-time behavior prediction.

• Weinland (2011): Developed a 3D multi-view method robust to occlusions and varying angles.

2. Existing Systems:

• Use Convolutional Neural Networks (CNNs) for spatial analysis.

• Use Recurrent Neural Networks (RNNs), including LSTM and GRU, for temporal motion patterns.

3. Proposed System:

• A hybrid model integrating CNNs (for spatial features) and LSTM/GRU (for temporal sequences).

• Applies transfer learning to enhance performance in data-scarce environments.

• Suitable for real-time applications in surveillance, healthcare, and smart devices.

4. Implementation Steps:

• Data collection from video/sensors.

• Preprocessing and training deep learning models.

• Activity detection and classification in real-time.

5. System Modules:

1. Video Preprocessing: Captures video, extracts frames.

2. Feature Extraction: Uses pretrained CNNs for spatial features.

3. Temporal Analysis: Analyzes sequences of movement.

4. Action Classification: Labels activities based on learned features.

5. Visualization: Displays annotated predictions on video.

6. Algorithms Used:

• Frame Capture

• Feature Learning

• Motion Analysis

• Activity Labelling

• Output Display

7. System Architecture & Data Flow:
Describes the flow from video input to real-time activity annotation through a structured pipeline (visual diagrams implied).

8. Results:
Screenshots illustrate:

• Folder selection

• Video path input

• Code execution

• Real-time activity detection

Conclusion

Deep learning technologies have transformed the HAR landscape by enabling accurate recognition of complex patterns. Despite current challenges such as computational requirements and data scarcity, the evolution of edge AI and improved algorithms will continue enhancing the adaptability and scope of these systems.

References

[1] Romaissa et al., \"Vision-based human recognition: A survey,\" Multimedia Tools and Applications, 2020. [2] Wang et al., \"Deep learning for sensor-based activity recognition: A survey,\" Pattern Recognition Letters, 2019. [3] Ronao & Cho, \"HAR with smartphone sensors using deep learning,\" Expert Systems with Applications, 2016. [4] Nweke et al., \"Deep learning algorithms for HAR,\" Expert Systems with Applications, 2018. [5] Kay et al., \"The Kinetics Human Action Video Dataset,\" arXiv, 2017. [6] Weinland et al., \"Free viewpoint action recognition using motion history volumes,\" CVIU, 2006.

Copyright

Copyright © 2025 Mrs. P. Neelima, A. Swathi, K. Susmitha, P. Haripriya, I. L. S. Geethika. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.