Wireless contactless sensing technologies have become vital for individualized healthcare monitoring since they let doctors check on patient’s health without having to touch them. However, it is still hard to choose the best sensing technology because of uncertainty, imprecision, and different expert perspectives. For the sake of solving such problems, a complete decision-making framework based on the Neutrosophic Soft Fuzzy TOPSIS method has been proposed which works well with vague and missing information in evaluations with more than one criterion. The model systematically evaluates different sensing technologies based on important criteria and gives a clear and strong score by calculating how near they are to the perfect solution. A comprehensive discussion and sensitivity analysis on the results obtained have also been presented along with pros and cons. A table comparing the proposed model to other decision-making methodologies has been additionally provided keeping the benchmarking into account to ensure the model is both accurate and consistent in its performance. Also, comparative trials show that the proposed method is better at coping with uncertainty and competing expert opinions. Overall, in this framework, results show that the proposed decision-support tool is dependable and easy-to-understand for healthcare professionals and policymakers. It helps them choose the best contactless human sensing technologies for real-world personalized healthcare applications.
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