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 oftextile technologies, fiber science, processing, and the inventions of digital fabrics, the study focuses on the outcomesobtained after being exposed to newer tools including the smart fibers, computer-aided design systems and ergonomicworking stations, and their impacts on income, productivity, employment structure as well as health. Structured surveys of200 of the rural weavers in Chanderi, Salem, and Kutch were administered to collect data and were analyzed usingdescriptive statistics, regression modeling, K-means clustering, and decision tree algorithms. The findings show that thereexists a great positive correlation between socio-economic improvement and the adoption of smart textiles. To be moreprecise, the training on garment technology and exposure to digital tools have proved to be important predictors ofincreased productivity and earnings. Cluster analysis validated the stratificational socio-economic positioning on the basisof technology access, but the favorable ergonomic differences and fewer sick leaves by smart textiles users were anindication of the health dividend. Moreover, the work changed into formal and more organized jobs representing theimproved economic stability. The results highlight how inclusive development in the rural textile industry could be fuelled bysuch focus to promote investments in smart textile training and infrastructure.