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Deep learning for daily care: medicine recognition and reminder systems for the visually impaired

Uttam Waghmode

Uttam Waghmode

Department of Computer Engineering, Ramrao Adik Institute of TechnologyNavi Mumbai, India
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Waghmode, Uttam
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Pooja Bagane

Pooja Bagane

Department of Computer Science and Engineering, Symbiosis Institute of Technology-Pune Campus, Symbiosis International (Deemed University)Pune, India
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Bagane, Pooja
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Ashwini Naik

Ashwini Naik

Department of Electronic & Communication Engineering, Ramrao Adik Institute of TechnologyNavi Mumbai, India
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Naik, Ashwini
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Rajendra Balaso Mohite

Rajendra Balaso Mohite

Department of Electronics and Telecommunication Engineering, Bharati Vidyapeeth College of EngineeringNavi Mumbai, India
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Mohite, Rajendra Balaso
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Aasha Mahesh Chavan

Aasha Mahesh Chavan

Department of Computer Engineering, Ramrao Adik Institute of TechnologyNavi Mumbai, India
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Chavan, Aasha Mahesh
  
10 jun 2025
Deep learning for daily care: medicine recognition and reminder systems for the visually impaired's Cover Image
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Publicado en línea: 10 jun 2025
Recibido: 18 mar 2025
DOI: https://doi.org/10.2478/ijssis-2025-0025
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© 2025 Uttam Waghmode et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1:

System block diagram.
System block diagram.

Figure 2:

Flowchart of system/CNN layers. CNN, convolutional neural network.
Flowchart of system/CNN layers. CNN, convolutional neural network.

Figure 3:

Dataset of Dolo medicine.
Dataset of Dolo medicine.

Figure 4:

Dataset of Volini medicine.
Dataset of Volini medicine.

Figure 5:

Dataset of Strepsils medicine.
Dataset of Strepsils medicine.

Figure 6:

Epochs result for the inception model.
Epochs result for the inception model.

Figure 7:

CNN architecture. CNN, convolutional neural network.
CNN architecture. CNN, convolutional neural network.

Figure 8:

Summary of parameters and trained CNN model. CNN, convolutional neural network.
Summary of parameters and trained CNN model. CNN, convolutional neural network.

Figure 9:

Output results for Strepsils.
Output results for Strepsils.

Figure 10:

Output results for Volini gel.
Output results for Volini gel.

Figure 11:

Output results for Dolo.
Output results for Dolo.

Figure 12:

Output results for Jovees shampoo.
Output results for Jovees shampoo.

Literature review

Reference Method/algorithm used Merits Demerits
[1] YOLO and OpenCV Provides real-time object detection and visual replacement for the blind. YOLO may struggle with small or distant objects, and OpenCV's accuracy can vary based on lighting conditions and object complexity.
[2] TensorFlow API, CNN, SSD, and MobileNet V2 Achieves high accuracy without needing an Internet connection. May require significant computational resources, especially for training the model.
[3] CNN Utilizes data augmentation to achieve a 94% accuracy rate for banknote recognition. Edge-detected images negatively affect accuracy, indicating a need for larger datasets and varied lighting conditions.
[4] CNN Improves runtime and accuracy for heart rate estimation in large groups. Specific details about the adapted algorithm and its implementation are needed for a thorough evaluation.
[5] YOLO and SSD Uses Raspberry Pi devices for a compact travel aid, demonstrating real-world implementation. The system may face limitations in detecting objects in complex environments or under varying lighting conditions.
[6] CNN and LSTM Uses Braille and sound bite hearing devices for communication, achieving high accuracy. The system's effectiveness may depend on the user's familiarity and comfort with Braille.
[9] FSP algorithm Utilizes a panoramic camera for pedestrian trajectory prediction, improving real-time performance. The algorithm's accuracy and performance in dynamic or crowded environments need further evaluation.
[10] CNN Achieves 98% accuracy in diagnosing glaucoma using retinal images. The effectiveness of the technique in clinical settings and its generalizability to diverse populations need further validation.
[11] Four-layered CNN Detects and classifies objects with high accuracy and low response time. The device's performance may vary based on the complexity of the environment and the types of objects present.
[12] CNN and fuzzy logic Provides auditory feedback for obstacle detection, enhancing interaction with surroundings. The computational complexity of the algorithms may affect real-time performance.
[13] CNN Uses smart glasses to identify medicine, showing promise for real-world implementation. The system's accuracy and reliability in identifying specific medications need further validation.

Comparative study for the success rate of the proposed system with existing work

Reference Method used Dataset size Accuracy (%) Medicines handled Key advantage
[13] CNN ∼2000 images 92 3 Smart glasses integration
[6] CNN + LSTM ∼3000 images 94 5 Includes Braille output
[3] CNN Augmented banknote data 94 Not medicine-specific Focused on money recognition
Proposed system Inception CNN 16,000 images 96.98 4 Audio output + medication reminder

Success rate of the proposed system

Sr. No. Medicine name No of trails
 Success rate (%)
Succeed Failed
1. Strepsils 46 4 92
2. Volini gel 48 2 96
3. Dolo 47 3 94
Success rate≥ 94
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