Compressive sensing (CS) is a promising solution for low-power on-body sensors for 24/7 wireless health monitoring . In such an application, a mobile data aggregator performing real-time signal reconstruction is desired for timely prediction and proactive prevention. However, CS reconstruction requires solving a sparse approximation (SA) problem. Its high computational complexity makes software solvers, consuming 2-50W on CPUs, very energy inefficient for real-time processing. This paper presents a configurable SA engine in a 40nm CMOS technology for energy-efficient mobile data aggregation from compressively sampled biomedicai signals. Using configurable architecture, a 100% utilization of computing resources is achieved. An efficient data-shuffling scheme is implemented to reduce memory leakage by 40%. At the minimum-energy point (MEP), the SA engine achieves a real-time throughput for reconstructing 61-to-237 channels of biomedicai signals simultaneously with <1% of a mobile device's 2W power budget, which is 76-350× more energy-efficient than prior hardware designs.