  Bio-signals exhibit high redundancy, and the algorithms for their processing
are inherently error resilient. This property can be leveraged to improve the
energy-efficiency of IoT-Edge (wearables) through the emerging trend of
approximate computing. This paper presents XBioSiP, a novel methodology for
approximate bio-signal processing that employs two quality evaluation stages,
during the pre-processing and bio-signal processing stages, to determine the
approximation parameters. It thereby achieves high energy savings while
satisfying the user-determined quality constraint. Our methodology achieves, up
to 19x and 22x reduction in the energy consumption of a QRS peak detection
algorithm for 0% and <1% loss in peak detection accuracy, respectively.
