  Human motion analysis is used in many different fields and applications.
Currently, existing systems either focus on one single limb or one single class
of movements. Many proposed systems are designed to be used in an indoor
controlled environment and must possess good technical know-how to operate. To
improve mobility, a less restrictive, modularized, and simple Inertial
Measurement units based system is proposed that can be worn separately and
combined. This allows the user to measure singular limb movements separately
and also monitor whole body movements over a prolonged period at any given time
while not restricted to a controlled environment. For proper analysis, data is
conditioned and pre-processed through possible five stages namely power-based,
clustering index-based, Kalman filtering, distance-measure-based, and PCA-based
dimension reduction. Different combinations of the above stages are analyzed
using machine learning algorithms for selected case studies namely hand gesture
recognition and environment and shoe parameter-based walking pattern analysis
to validate the performance capability of the proposed wearable device and
multi-stage algorithms. The results of the case studies show that
distance-measure-based and PCA-based dimension reduction will significantly
improve human motion identification accuracy. This is further improved with the
introduction of the Kalman filter. An LSTM neural network is proposed as an
alternate classifier and the results indicate that it is a robust classifier
for human motion recognition. As the results indicate, the proposed wearable
device architecture and multi-stage algorithms are cable of distinguishing
between subtle human limb movements making it a viable tool for human motion
analysis.
