Smart wearable sensor-based model for monitoring medication adherence using sheep flock optimization algorithm-attention-based bidirectional long short-term memory (SFOA-Bi-LSTM)
Objective Medication adherence (MA) is crucial to patient treatment and vital for therapeutic outcomes. Due to its ability to continuously monitor a patient's MA behavior, the recent focus on sensor technology for MA monitoring is a promising development. The primary objective of this research...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-06-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076251349692 |
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| author | Yasser Alatawi Palanisamy Amirthalingam Narmatha Chellamani Manimurugan Shanmuganathan Mostafa A. Sayed Ali Saleh Fahad Alqifari Vasudevan Mani Muralikrishnan Dhanasekaran Abdulelah Saeed Alqahtani Ahmed Aljabri |
| author_facet | Yasser Alatawi Palanisamy Amirthalingam Narmatha Chellamani Manimurugan Shanmuganathan Mostafa A. Sayed Ali Saleh Fahad Alqifari Vasudevan Mani Muralikrishnan Dhanasekaran Abdulelah Saeed Alqahtani Ahmed Aljabri |
| author_sort | Yasser Alatawi |
| collection | DOAJ |
| description | Objective Medication adherence (MA) is crucial to patient treatment and vital for therapeutic outcomes. Due to its ability to continuously monitor a patient's MA behavior, the recent focus on sensor technology for MA monitoring is a promising development. The primary objective of this research is to implement sensor devices/smart wearables powered by advanced deep learning (DL) techniques to evaluate complex data patterns effectively and make accurate predictions. This study introduces a novel smart wearable sensors-based hand gesture recognition system to predict medication behaviors. Methods A device equipped with accelerometer and gyroscope sensors acquires and analyzes data from hand motions. A mobile app records the data from the smart device, subsequently storing it in a database in .csv file. The data is gathered, preprocessed, and classified to identify MA behavior utilizing the developed DL model known as the sheep flock optimization algorithm-attention-based bidirectional long short-term memory network (SFOA-Bi-LSTM). The data was initially gathered and preprocessed via the Z -score normalization method. The data samples are classified using the attention-based Bi-LSTM model after undergoing preprocessing. The SFOA method was utilized to optimize the hyperparameters of the attention-based Bi-LSTM model. Results The model's performance was examined using a five-fold cross-validation based on recall, accuracy, F1 score, and precision. The SFOA-Bi-LSTM model achieved 98.90% accuracy, 97.80% recall, 98.80% precision, and 98.62% F1 score, demonstrating its novelty and potential to inspire and motivate healthcare professionals to adopt this promising method for monitoring MA in healthcare applications. Conclusion The results indicate that the SFOA-Bi-LSTM model performs well in predicting MA. The SFOA-Bi-LSTM model offers several unique advantages, including efficient hyperparameter tuning via the SFOA, enhanced feature representation through an attention mechanism, and comprehensive temporal analysis using Bi-LSTM. It demonstrates superior performance compared to conventional models while being robust to noisy data due to effective preprocessing. |
| format | Article |
| id | doaj-art-606224d2afdb43718d48d0f35b004da4 |
| institution | OA Journals |
| issn | 2055-2076 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj-art-606224d2afdb43718d48d0f35b004da42025-08-20T02:09:19ZengSAGE PublishingDigital Health2055-20762025-06-011110.1177/20552076251349692Smart wearable sensor-based model for monitoring medication adherence using sheep flock optimization algorithm-attention-based bidirectional long short-term memory (SFOA-Bi-LSTM)Yasser Alatawi0Palanisamy Amirthalingam1Narmatha Chellamani2Manimurugan Shanmuganathan3Mostafa A. Sayed Ali4Saleh Fahad Alqifari5Vasudevan Mani6Muralikrishnan Dhanasekaran7Abdulelah Saeed Alqahtani8Ahmed Aljabri9 Department of Pharmacy Practice, Faculty of Pharmacy, , Tabuk, Saudi Arabia Department of Pharmacy Practice, Faculty of Pharmacy, , Tabuk, Saudi Arabia Faculty of Computers and Information Technology, , Tabuk, Saudi Arabia Faculty of Computers and Information Technology, , Tabuk, Saudi Arabia Department of Pharmacy Practice, Faculty of Pharmacy, , Tabuk, Saudi Arabia Department of Pharmacy Practice, Faculty of Pharmacy, , Tabuk, Saudi Arabia Department of Pharmacology and Toxicology, College of Pharmacy, , Buraydah, Saudi Arabia Department of Drug Discovery and Development, Harrison College of Pharmacy, , Auburn, AL, USA Faculty of Computers and Information Technology, , Tabuk, Saudi Arabia Department of Pharmacy Practice, Faculty of Pharmacy, , Jeddah, Saudi ArabiaObjective Medication adherence (MA) is crucial to patient treatment and vital for therapeutic outcomes. Due to its ability to continuously monitor a patient's MA behavior, the recent focus on sensor technology for MA monitoring is a promising development. The primary objective of this research is to implement sensor devices/smart wearables powered by advanced deep learning (DL) techniques to evaluate complex data patterns effectively and make accurate predictions. This study introduces a novel smart wearable sensors-based hand gesture recognition system to predict medication behaviors. Methods A device equipped with accelerometer and gyroscope sensors acquires and analyzes data from hand motions. A mobile app records the data from the smart device, subsequently storing it in a database in .csv file. The data is gathered, preprocessed, and classified to identify MA behavior utilizing the developed DL model known as the sheep flock optimization algorithm-attention-based bidirectional long short-term memory network (SFOA-Bi-LSTM). The data was initially gathered and preprocessed via the Z -score normalization method. The data samples are classified using the attention-based Bi-LSTM model after undergoing preprocessing. The SFOA method was utilized to optimize the hyperparameters of the attention-based Bi-LSTM model. Results The model's performance was examined using a five-fold cross-validation based on recall, accuracy, F1 score, and precision. The SFOA-Bi-LSTM model achieved 98.90% accuracy, 97.80% recall, 98.80% precision, and 98.62% F1 score, demonstrating its novelty and potential to inspire and motivate healthcare professionals to adopt this promising method for monitoring MA in healthcare applications. Conclusion The results indicate that the SFOA-Bi-LSTM model performs well in predicting MA. The SFOA-Bi-LSTM model offers several unique advantages, including efficient hyperparameter tuning via the SFOA, enhanced feature representation through an attention mechanism, and comprehensive temporal analysis using Bi-LSTM. It demonstrates superior performance compared to conventional models while being robust to noisy data due to effective preprocessing.https://doi.org/10.1177/20552076251349692 |
| spellingShingle | Yasser Alatawi Palanisamy Amirthalingam Narmatha Chellamani Manimurugan Shanmuganathan Mostafa A. Sayed Ali Saleh Fahad Alqifari Vasudevan Mani Muralikrishnan Dhanasekaran Abdulelah Saeed Alqahtani Ahmed Aljabri Smart wearable sensor-based model for monitoring medication adherence using sheep flock optimization algorithm-attention-based bidirectional long short-term memory (SFOA-Bi-LSTM) Digital Health |
| title | Smart wearable sensor-based model for monitoring medication adherence using sheep flock optimization algorithm-attention-based bidirectional long short-term memory (SFOA-Bi-LSTM) |
| title_full | Smart wearable sensor-based model for monitoring medication adherence using sheep flock optimization algorithm-attention-based bidirectional long short-term memory (SFOA-Bi-LSTM) |
| title_fullStr | Smart wearable sensor-based model for monitoring medication adherence using sheep flock optimization algorithm-attention-based bidirectional long short-term memory (SFOA-Bi-LSTM) |
| title_full_unstemmed | Smart wearable sensor-based model for monitoring medication adherence using sheep flock optimization algorithm-attention-based bidirectional long short-term memory (SFOA-Bi-LSTM) |
| title_short | Smart wearable sensor-based model for monitoring medication adherence using sheep flock optimization algorithm-attention-based bidirectional long short-term memory (SFOA-Bi-LSTM) |
| title_sort | smart wearable sensor based model for monitoring medication adherence using sheep flock optimization algorithm attention based bidirectional long short term memory sfoa bi lstm |
| url | https://doi.org/10.1177/20552076251349692 |
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