  There has been a continuing demand for improving the accuracy and ease of use
of medical devices used on or around the human body. Communication is critical
to medical applications, and wireless body area networks (WBANs) have the
potential to revolutionize diagnosis. Despite its importance, WBAN technology
is still in its infancy and requires much research. We consider body channel
communication (BCC), which uses the whole body as well as the skin as a medium
for communication. BCC is sensitive to the body's natural circulation and
movement, which requires a noncoherent model for wireless communication. To
accurately handle practical applications for electronic devices working on or
inside a human body, we configure a realistic system model for BCC with on-off
keying (OOK) modulation. We propose novel detection techniques for OOK symbols
and improve the performance by exploiting distributed reception and
supervised-learning approaches. Numerical results show that the proposed
techniques are valid for noncoherent OOK transmissions for BCC.
