Detailed motion reconstruction is a prerequisite of biomotion analysis and physical function assessment for a variety of scenarios. For example, biomechanical analysis can be used to assess physical activity to diagnose pathological conditions, to provide an objective measure of biomechanics for peri-operative care, and to monitor patients with mobility issues. Unfortunately, current motion capture systems cannot perform biomechanical analysis continuously in the patient's natural environment. In this paper, a pose estimation scheme from a sparse network of accelerometer-based wearable sensors, which does not impose restrictions upon the patient's daily life, is presented. In the proposed method, a marker-based motion capture system is used for acquiring the 3D motion data, and partial least squares regression (PLSR) is used to establish the implicit model between 3D body pose and the wearable sensor measurements. A linear constant velocity process model and measurement model are designed and a Kalman filter is then deployed to estimate the posture. Experimental results demonstrate the strength of the technique and how it can be used to estimate detailed 3D motion from a sparse set of sensors.