This paper proposes a joint sparse spectrum reconstruction-based motion artifact reduction algorithm for Photoplethysmo-graphy (PPG) signals to overcome the artifact removing problem in the PPG sensor data collection.Firstly

our algorithm constructs a spectral matrix

using PPG signals and acceleration signals during the same time period.The sparse characteristics of the spectral matrix and its rows are extracted.Secondly

we use the compressive sensing to model the motion artifact removing process in PPG signals as a joint sparse signal reconstruction process.Then this process is further modeled as an optimal model.We exploit the iterative method to obtain the optimal solution to the model.Finally

we combine the spectrum subtraction to remove the motion artifact in PPG signals.In the result

we can effectively decrease the impact of the motion artifact on PPG signals.Simulation results demonstrate that the algorithm proposed in this paper can effectively remove the motion artifact in PPG signals and attain the better noise reduction performance.