This disclosure relates generally to detection of mild cognitive impairments in subjects. The method and system proposed provides a continuous/seamless monitoring platform for MCI detection in subjects by continuously monitoring routine activities of subjects (Activities of Daily Living (ADL)) in a smart environment using plurality of passive, unobtrusive, binary, unobtrusive non-intrusive sensors embedded in living infrastructure. The proposed method and system detects symptoms of MCI at the onset of the disease, while also addressing issue of sensor failures that causes gaps in the data. The collected sensor data is pre-processed in several stages which includes which includes pre-processing of sensor data, behavior deviation detection, and abnormality detection and so on. Further, the disclosure also proposes an autoencoder based technique, to reduce the dimension of the data to find personalized deviations in behavior of a subject which is used to detect if a subject could be a potential case of MCI.
