MWB_Analyzer: An Automated Embedded System for Real-Time Quantitative Analysis of Morphine Withdrawal Behaviors in Rodents
Abstract
1. Introduction
- (1)
- Multi-angle video and full-scene audio capture via an edge computing platform for comprehensive data acquisition;
- (2)
- Real-time data reduction algorithms that significantly lower processing loads while preserving critical information;
- (3)
- Created and published a dataset of morphine withdrawal behavior video clips in rats for future research;
- (4)
- Enhanced behavioral recognition accuracy and objectivity through an improved YOLO-based framework and signal processing methods;
- (5)
- A more objective scoring protocol was used to score morphine withdrawal behavior to assess the degree of morphine addiction.
2. Materials and Methods
2.1. Animals and Drugs
2.2. Hardware Design and System Integration
2.3. Video Capture and External Trigger Synchronization
2.4. Motion Segment Filtering and Camera Angle Selection
2.5. Wet-Dog Shakes and Scratching Behavior Recognition
2.6. Lateral Behavior Recognition
2.7. Audio Recognition of Teeth Chattering Based on Mel Spectrogram and ResNet-SE
3. Results
3.1. Analysis of Withdrawal Behavior Recognition Metrics
3.2. Exploring Behavioral Assessment Schemes for Morphine Addiction
3.3. Comprehensive Scoring Results Based on Our Proposed Evaluation Scheme
3.4. Experimental Study on Withdrawal Behaviors at Different Morphine Doses
3.5. Summary and Implications
4. Discussion
- (1)
- Easy Integration into Standard Workflows: MWB_Analyzer combines multi-angle video and full-scene audio capture in a fully automated manner. Its decentralized design (based on three compact Orange Pi units) requires no technical expertise for setup or operation. Once installed, the system runs autonomously, providing synchronized behavioral recordings without disrupting normal experimental procedures.
- (2)
- Substantial Reduction in Manual Workload Through Automation: The system leverages automatic data filtering and prioritization of key views (e.g., top view for locomotion, side view for postural signs) to reduce redundant data by over 95%. This optimization significantly lowers the computational load, allowing real-time behavior recording and analysis to be performed efficiently on compact edge devices. By automating both data acquisition and analysis, MWB_Analyzer minimizes the need for manual scoring and video review, enabling researchers to obtain objective results with minimal hands-on effort.
- (3)
- Enhanced Objectivity and Reproducibility: MWB_Analyzer achieves high classification accuracy (over 94% for video-based and 92% for audio-based detection of withdrawal behaviors) using a refined machine learning model. Unlike traditional scales such as Gellert and Holtzman [37], the system eliminates subjective judgment, ensuring consistent and unbiased assessments across experiments and operators. This is especially valuable for detecting subtle dose–response differences that might be missed in manual scoring. The system’s real-time analysis and precise behavior recognition facilitate more reliable dose–response evaluations, improved standardization across studies, and higher throughput for preclinical drug screening. By reducing human variability, MWB_Analyzer strengthens the statistical power of pharmacodynamic assessments and enhances confidence in drug efficacy and safety evaluations.
- (4)
- Broad Applicability and Flexibility: While optimized for morphine withdrawal, the system’s modular design allows easy adaptation to other models of dependence (e.g., fentanyl, alcohol). Its architecture also supports studies on drug delivery methods, pharmacokinetics, and systemic effects, making it a versatile tool for a wide range of preclinical research applications.
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Criterion | Baseline | Baseline + CBAM (Kernel = 3) | Baseline + CSPM (Kernel = 3) | Baseline + CSPM (Kernel = 7) | Baseline + CBAM (Kernel = 7) | RK3588 (Baseline + CSPM (Kernel = 3)) | |
---|---|---|---|---|---|---|---|
Grooming | Precision | 0.936 | 0.912 | 0.912 | 0.928 | 0.928 | 0.904 |
Recall | 0.860 | 0.832 | 0.950 | 0.885 | 0.959 | 0.950 | |
F1 | 0.897 | 0.870 | 0.931 | 0.906 | 0.943 | 0.926 | |
Head-raising | Precision | 0.977 | 0.989 | 0.989 | 0.977 | 0.977 | 0.989 |
Recall | 0.966 | 0.966 | 0.966 | 0.977 | 0.977 | 0.966 | |
F1 | 0.971 | 0.977 | 0.977 | 0.977 | 0.977 | 0.977 | |
Normal | Precision | 0.879 | 0.894 | 0.924 | 0.909 | 0.924 | 0.924 |
Recall | 0.841 | 0.881 | 0.924 | 0.870 | 0.871 | 0.924 | |
F1 | 0.859 | 0.887 | 0.924 | 0.889 | 0.897 | 0.924 | |
Rearing | Precision | 0.915 | 0.872 | 0.894 | 0.809 | 0.830 | 0.851 |
Recall | 0.977 | 0.854 | 1.000 | 1.000 | 1.000 | 1.000 | |
F1 | 0.945 | 0.863 | 0.994 | 0.894 | 0.907 | 0.920 | |
Genital licking | Precision | 0.831 | 0.803 | 0.958 | 0.887 | 0.972 | 0.958 |
Recall | 0.952 | 0.934 | 0.919 | 0.984 | 0.896 | 0.932 | |
F1 | 0.887 | 0.864 | 0.938 | 0.933 | 0.932 | 0.944 | |
Wall-supported rearing | Precision | 0.990 | 0.930 | 0.990 | 1.000 | 1.000 | 0.990 |
Recall | 0.961 | 0.949 | 0.961 | 0.926 | 0.943 | 0.943 | |
F1 | 0.975 | 0.939 | 0.975 | 0.962 | 0.971 | 0.966 | |
Face-washing | Precision | 0.769 | 0.821 | 0.872 | 0.821 | 0.821 | 0.872 |
Recall | 0.909 | 0.914 | 0.829 | 0.842 | 0.914 | 0.791 | |
F1 | 0.833 | 0.865 | 0.850 | 0.831 | 0.865 | 0.829 | |
All | Top-1 | 0.918 | 0.901 | 0.942 | 0.923 | 0.938 | 0.936 |
Types of Behavior | Scoring Method |
---|---|
Wet-dog shakes | 1 point each time recorded |
Head-raising | 1 point each time recorded |
Stereotypic behaviors | 0.5 point each time recorded |
Teeth chattering | 2 points if it occurs every 5 min |
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Zhang, M.; Li, Q.; Li, S.; Sun, B.; Wu, Z.; Liu, J.; Geng, X.; Chen, F. MWB_Analyzer: An Automated Embedded System for Real-Time Quantitative Analysis of Morphine Withdrawal Behaviors in Rodents. Toxics 2025, 13, 586. https://doi.org/10.3390/toxics13070586
Zhang M, Li Q, Li S, Sun B, Wu Z, Liu J, Geng X, Chen F. MWB_Analyzer: An Automated Embedded System for Real-Time Quantitative Analysis of Morphine Withdrawal Behaviors in Rodents. Toxics. 2025; 13(7):586. https://doi.org/10.3390/toxics13070586
Chicago/Turabian StyleZhang, Moran, Qianqian Li, Shunhang Li, Binxian Sun, Zhuli Wu, Jinxuan Liu, Xingchao Geng, and Fangyi Chen. 2025. "MWB_Analyzer: An Automated Embedded System for Real-Time Quantitative Analysis of Morphine Withdrawal Behaviors in Rodents" Toxics 13, no. 7: 586. https://doi.org/10.3390/toxics13070586
APA StyleZhang, M., Li, Q., Li, S., Sun, B., Wu, Z., Liu, J., Geng, X., & Chen, F. (2025). MWB_Analyzer: An Automated Embedded System for Real-Time Quantitative Analysis of Morphine Withdrawal Behaviors in Rodents. Toxics, 13(7), 586. https://doi.org/10.3390/toxics13070586