This paper presents a design and experimental study
of a remote posture monitoring system. The
monitoring system aims for applications in activity
analysis of the elderly. We propose a wearable sensor
system, which consists of a two-axis accelerometer
and RF wireless communication modules. The
acquired body motion signals from accelerometers
are transmitted to a host computer via RF link for
feature extraction and pattern recognition. Wavelet
transform techniques are adopted for feature
extraction of human body postures. The signal is
decomposed into five levels and low-frequency
components are extracted to obtain useful features.
Pattern recognition techniques are then applied to
distinguish five basic postures: up stairs, down stairs,
walking, standing, and sitting. Experimental results
are presented to show the effectiveness of the
proposed method.