The present paper proposes personalised remote healthcare based on soft computing. The primary goal of the thesis is to develop an intelligent, personalised RHM that uses personalised monitoring to create alerts in almost real-time settings and detect abnormalities in the status of human wellness. Since the activities have a significant impact on the vital sign values, the range of linguistic severity class labels in this research work is fixed based on the same, which results in a good level of personalisation of health data. The suggested study project can be very helpful to those under home quarantine or in specially designated quarantine areas, such as hotels, etc. Soft computing techniques offer a chance to create a RHM that can reliably detect changes in the population health state and accurately monitor many or all metrics of interest.