Wearable continuous gait phase estimation is essential for walking assistance, clinical rehabilitation, and clinical assessment; however, most algorithms have only been validated for straight-line and constant-speed walking, and it is unclear how performance will change in real-life locomotion scenarios. A generalized paradigm is needed to comprehensively assess and recommend wearable continuous gait phase estimation strategies for the diverse array of walking situations. We thus propose a comprehensive evaluation indicator system for eight typical gait activities in daily life including slow walking, standard walking, running, walking with turns, stair descent, stair ascent, stop-and-go, and uneven terrain walking. The indicator system was used to evaluate four commonly used continuous gait phase estimation strategies: adaptive oscillators, phase oscillator, neural network, and time-based estimation. Eleven healthy participants were enrolled in the evaluation. All estimation strategies performed well for constant-speed walking but performance varied for other activities. Time-based estimation was most accurate for slowwalking ( 0.094 +/- 0.011 rad root mean square error, 1.50 +/- 0.18 % of one gait cycle), running ( 0.167 +/- 0.028 rad, 2.66 +/- 0.44 %) and walking with turns ( 0.124 +/- 0.047 rad, 2.00 +/- 0.75 %). Adaptive oscillators were most accurate for standard walking( 0.115 +/- 0.037 rad, 1.83 +/- 0.59%). Phase oscillator was most accurate for stair climbing( 0.280 +/- 0.063 rad, 4.46 +/- 1.00 %) and uneven terrain ( 0.204 +/- 0.069 rad, 4.30 +/- 1.10%). Neural network was most accurate for stop-and-go( 0.27 +/- 0.114 rad, 4.30 +/- 1.81 %). These results can potentially provide guidance for determining suitable gait phase estimation strategies in realistic locomotion scenarios, and in comparing and optimizing the current proposed strategies.