In this paper, we propose a low-complexity architecture design methodology for the Single Channel Independent Component Analysis (SCICA) algorithm targeting pervasive personalized healthcare. SCICA, unlike the conventional ICA, separates the signal from multiple sources using only a single sensor that has tremendous potential for reducing the number of body-worn sensors. However, such applications are constrained by power consumption limitation due to the battery backup necessitating low-complexity system design and the on-chip area requirement. On the other hand, SCICA, involving computationally intensive stages including ICA, Fast Fourier Transform (FFT), Eigen Value Decomposition (EVD) and k-means clustering, is not possible to be mapped onto the low-complexity architecture directly from the algorithmic level. Hence, in this paper, adopting algorithm-architecture holistic approach, we introduce the Coordinate Rotation Digital Computer (CORDIC) based low-complexity SCICA architecture design methodology suitable for such resource constrained applications. K-means architecture used for low-complex SCICA based on the proposed methodology consumes core silicon area of 0.28mm2 and power of 0.25mW at 1.2 V, 1-MHz frequency using 0.13µm standard cell technology library (TSMC) that is about 50% less than that of the state-of-the art approaches. The functionality has been compared favorably with the conventional SCICA and hardware analysis has also cross-verified the low complexity nature of the proposed methodology.