Design and implementation of embedded systems in intelligent clothing: Balancing functionality andcomfort

In the era of smart technology, the integration of electronics with textiles has given rise to intelligent clothing garments embedded with electronic systems capable of sensing, processing, and responding to environmental or physiological data. However, ensuring both sensor accuracy and user comfort remains a significant challenge. To address this, the present research aims to design and implement an embedded smart clothing system that combines wearable sensor technology with soft-textile materials to enable real-time monitoring of body temperature while maintaining high wearing comfort. The proposed system utilises strategically placed, skin-loose temperature sensors embedded into infant-friendly fabrics, minimising irritation and enhancing mobility by eliminating the discomfort commonly associated with skin-tight sensors. Custom-designed smart garments equipped with these sensors were used for data collection, followed by pre-processing techniques such as normalisation and one-hot encoding. At the core of the system lies a low-power embedded microcontroller that wirelessly collects, processes, and transmits sensor data, ensuring energy-efficient operation suitable for everyday infant wear. To address the reduced precision of individual loose sensors, a deep learning-based temperature estimation model was developed. This model aggregates multichannel sensor inputs and processes using a Komodo Mlipir fine-tuned Stochastic Temporal Convolutional Network (KM-STCN), enabling real-time estimation and correction of body temperature readings. Implemented using Python, the experimental results demonstrate that the combined use of multiple skin-loose sensors and the deep learning model achieves high temperature estimation accuracy, recall, F1-score, and precision rates exceeding 93%, while significantly enhancing garment comfort. This research highlights the potential of integrating embedded systems, infant garment design, and AI-based signal processing to develop intelligent clothing that effectively balances health-monitoring functionality with everyday wearability in healthcare technologies.