Abstract:
Human Activity Recognition has enabled state of art applications in medical healthcare,
surveillance systems, digital entertainment and various other sectors. Therefore, prediction of such
kind of movements remained an interesting aspect in the field of research. Wearable sensor and
vision- based systems have been utilized for the detection of Activities of Daily Life (ADL),
however suffer from various limitations including intrusiveness, lighting conditions and privacy
issues. This study proposes a transfer learned, deep learning model for Frequency Modulated
Continuous Wave (FMCW) radar-based system operating at a frequency of 5.8 GHz with 400
MHz bandwidth for classification of human activities. Radars are highly sensitive to human body
movements and can capture small variations as Doppler shift. I have designed and demonstrated a
generalized deep learning classification system for the detection of ADL irrespective of the
geographical location and the ages of the subjects based on data from FMCW radar-based system.
I have utilized experimental public data from 99 participants consisting of 1453 micro-Doppler
signatures, at nine different locations including five retirement homes and four laboratories. This
study propose micro-Doppler images normalized for speed profile of individuals to obtain age
group independent feature maps of human activities and engineer a deep convolutional neural
network architecture with a high classification accuracy, sensitivity and specificity of 99.1%,
99.2% and 100%, respectively.
