Abstract Wearable electrocardiogram (ECG) measurement systems have been widely used in patients with CVD (Cardiovascular Disease) which can be worn in daily lives. However, currently the main problem is motion artifact interference, and reducing motion artifacts (MA) is one of the most challenging problems encountered in the filtering and processing of physiological signals. In this paper, by analyzing the spectral energy changes during the input process of motion artifacts, a cosine transform LMS adaptive cancellation algorithm (DCT-LMS) implementation is proposed aiming to remove the motion artifacts from the ECG. In order to study the performance of the algorithm and effectively remove the motion artifacts in the ECG signal, this thesis collects ECG signals of people's daily activities from fabric-based chest straps with dry electrodes. It verifies the classic LMS adaptive elimination algorithm and the normalized one. Besides, two LMS adaptive cancellation algorithms based on sine and cosine transform are compared. The simulation and experimental results show that the cosine-based adaptive algorithm is superior to the classical LMS algorithm in eliminating high-amplitude motion artifact noise of ECG.