Quantitative Evaluation of Smart Textile Adoption in Rural Weaving Communities using Machine Learning

It is a socio-economic study examining how the inclusion of smart textiles can benefit rural weaving communities in India, and provides an investigation using a machine learning-assisted quantitative method. Based on knowledge in the domain of textile technologies, fiber science, processing, and the inventions of digital fabrics, the study focuses on the outcomes obtained after being exposed to newer tools including the smart fibers, computer-aided design systems and ergonomic working stations, and their impacts on income, productivity, employment structure as well as health. Structured surveys of 200 of the rural weavers in Chanderi, Salem, and Kutch were administered to collect data and were analyzed using descriptive statistics, regression modeling, K-means clustering, and decision tree algorithms. The findings show that there exists a great positive correlation between socio-economic improvement and the adoption of smart textiles. To be more precise, the training on garment technology and exposure to digital tools have proved to be important predictors of increased productivity and earnings. Cluster analysis validated the stratificational socio-economic positioning on the basis of technology access, but the favorable ergonomic differences and fewer sick leaves by smart textiles users were an indication of the health dividend. Moreover, the work changed into formal and more organized jobs representing the improved economic stability. The results highlight how inclusive development in the rural textile industry could be fuelled by such focus to promote investments in smart textile training and infrastructure.