In this paper, a novel training/testing process for building/using a classification model
based on human activity recognition (HAR) is proposed. Traditionally, HAR has been accomplished
by a classifier that learns the activities of a person by training with skeletal data obtained from a
motion sensor, such as Microsoft Kinect. These skeletal data are the spatial coordinates (x, y, z) of
different parts of the human body. The numeric information forms time series, temporal records
of movement sequences that can be used for training a classifier. In addition to the spatial features
that describe current positions in the skeletal data, new features called ‘shadow features’ are
used to improve the supervised learning efficacy of the classifier. Shadow features are inferred
from the dynamics of body movements, and thereby modelling the underlying momentum of the
performed activities. They provide extra dimensions of information for characterising activities in the
classification process, and thereby significantly improve the classification accuracy. Two cases of HAR
are tested using a classification model trained with shadow features: one is by using wearable sensor
and the other is by a Kinect-based remote sensor. Our experiments can demonstrate the advantages
of the new method, which will have an impact on human activity detection research.