A wearable device capable of recognizing sleep stage, comprising: a processor , configured to train a neural network module ; an electrocardiogram sensor , coupled to the processor , and configured to generate an electrocardiogram signal (ECG), wherein the processor is configured to analyze the electrocardiogram signal (ECG) to generate a plurality of first characteristic values; an acceleration sensor , coupled to the processor , and configured to generate an acceleration signal (AS), wherein the processor is configured to analyze the acceleration signal (AS) to generate a plurality of second characteristic values; and an angular acceleration sensor , coupled to the processor , and configured to generate an angular acceleration signal (ANS), wherein the processor is configured to analyze the angular acceleration signal (ANS) to generate a plurality of third characteristic values, wherein the processor is configured to utilize the trained neural network module to perform a sleep stage recognition operation according to the first characteristic values, the second characteristic values and the third characteristic values, so as to obtain a sleep stage recognition result, characterised in that the processor is configured to analyze the angular acceleration signal (ANS) by an X-axis angular acceleration analysis module , a Y-axis angular acceleration analysis module and a Z-axis angular acceleration analysis module to obtain a plurality of angular acceleration detrended fluctuation analysis values (AnX_DFA, AnY_DFA, AnZ_DFA) and a plurality of angular acceleration sample entropy values (AnX_SE1, AnY_SE1, AnZ_SE1) as the plurality of third characteristic values. a processor , configured to train a neural network module ; an electrocardiogram sensor , coupled to the processor , and configured to generate an electrocardiogram signal (ECG), wherein the processor is configured to analyze the electrocardiogram signal (ECG) to generate a plurality of first characteristic values; an acceleration sensor , coupled to the processor , and configured to generate an acceleration signal (AS), wherein the processor is configured to analyze the acceleration signal (AS) to generate a plurality of second characteristic values; and an angular acceleration sensor , coupled to the processor , and configured to generate an angular acceleration signal (ANS), wherein the processor is configured to analyze the angular acceleration signal (ANS) to generate a plurality of third characteristic values, wherein the processor is configured to utilize the trained neural network module to perform a sleep stage recognition operation according to the first characteristic values, the second characteristic values and the third characteristic values, so as to obtain a sleep stage recognition result, characterised in that the processor is configured to analyze the angular acceleration signal (ANS) by an X-axis angular acceleration analysis module , a Y-axis angular acceleration analysis module and a Z-axis angular acceleration analysis module to obtain a plurality of angular acceleration detrended fluctuation analysis values (AnX_DFA, AnY_DFA, AnZ_DFA) and a plurality of angular acceleration sample entropy values (AnX_SE1, AnY_SE1, AnZ_SE1) as the plurality of third characteristic values. <EOS>