Smart wearable sensor-based model for monitoring medication adherence using sheep flock optimization algorithm-attention-based bidirectional long short-term memory (SFOA-Bi-LSTM)

Abstract

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.

Keywords: Bi-LSTM; Medication adherence; SFOA; Z-score normalization; deep learning; smart wearable sensor.

Figure 1.

Pipeline of the proposed model.

Figure 2.

Specification of MetaMotionRL device.

Figure 3.

Slap band with MetaMotionRL device.

Figure 4.

Sensors selection and data collection using the metaBase app.

Figure 5.

Framework of the long short-term memory (LSTM).

Figure 6.

The structure of the bidirectional long short-term memory (Bi-LSTM).

Figure 7.

Structure of the attention mechanism.

Figure 8.

Architecture of attention-based bidirectional long short-term memory (Bi-LSTM) model.

Figure 9.

Sheep flock optimization (SFO) algorithm flowchart.

Figure 10.

Graphical plot of sheep flock optimization algorithm-attention-based bidirectional long short-term memory (SFOA-Bi-LSTM) model's results.

Figure 11.

Graphical plot of results comparison.