In low frequency analog signal acquisition and processing circuit, signal measurement is often affected by noise and direct current (DC) drift. To remove DC drift and the existing noise from original signal accurately and obtain useful signal, a method of DC drift elimination based on segmented Empirical Mode Decomposition (EMD) is proposed. First, the signal is decomposed by EMD, and the local extremum points of the intrinsic mode function (IMF) components are identified for interval segmentation. Then, each segment of signal is decomposed by EMD, and the low-frequency components of each signal segment are selected to reconstruct the DC drift signal. In the end, the IMF components with noise as the main ingredients are screened by autocorrelation function for energy analysis, with all segments integrated and the signal after removing DC drift and noise reduction obtained. The simulation in the study shows the obvious effectiveness of the proposed method, compared with polynomial fitting, wavelet analysis, high-pass filtering and other methods. The strain signal of the robot force sensor in minimally invasive surgery is processed. The experimental results demonstrate that the Signal-Noise Ratio (SNR) is improved, specifically more than 6.39 dB, with the root mean square error (RMSE) significantly reduced. This method proves to be effective to eliminate the DC drift in the strain signal and achieve the purpose of noise reduction.