Comparative Evaluation of Commercial and Laboratory-Developed Stretchable Sensors for Facial Expression Recognition Using Machine Learning

Facial expression recognition (FER) is an important component of human--machine interaction, healthcare monitoring, and rehabilitation robotics. Conventional FER systems are predominantly image-based and rely on computer vision techniques, which require high computational resources and are sensitive to environmental conditions such as illumination and occlusion. To address these limitations, this study investigates a wearable sensing approach for FER using stretchable sensors. A comparative evaluation is conducted between a commercially available capacitance-based stretchable sensor and a laboratory-developed resistance-based stretchable sensor fabricated at the International Islamic University Malaysia (IIUM). Time-series data corresponding to four facial expressions---neutral, happy, sad, and disgust---were collected from 30 participants using multiple sensor placements on the face. Statistical features, including mean and standard deviation, were extracted from the sensor signals and used to train and test five supervised machine-learning classifiers: k-nearest neighbor, decision tree, support vector machine, logistic regression, and random forest. Experimental results demonstrate that the commercial sensor achieves higher overall recognition performance, with the random forest classifier yielding an average F1-score of 96%, compared with 90% for the laboratory-developed sensor. The superior performance of the commercial sensor is attributed to its higher sampling rate and greater signal stability. Nevertheless, the laboratory-developed sensor offers advantages in terms of cost, design flexibility, and the ability to measure both stretch and compression. The findings confirm the feasibility of stretchable sensors for facial expression recognition and highlight the potential of laboratory-developed sensors for cost-effective FER systems, particularly in applications such as rehabilitation robotics and human-centered assistive technologies.