Purpose: Physical activity (PA) consists of exercise and non-exercise PA, also termed non-exercise activity thermogenesis (NEAT). NEAT is much larger than that of exercise-induced EE, and varies substantially between individuals. In this lecture, the evaluation methods will be overviewed. Method: The doubly labeled water (DLW) method is an excellent method to use for measuring total energy expenditure (EE) in unrestrained humans under free-living conditions over about 2 weeks, with relatively high accuracy and precision. However, this method can only evaluate total EE and cannot provide day-to-day or minute-by-minute variations.]At present, several methods are used to measure EE in a field setting, i.e., behavioral observation, questionnaires, and physiological markers such as heart rate and motion sensors or activity monitors. Among them, questionnaires, pedometers and activity monitors have been frequently utilized, therefore will be focused. Result: Questionnaires can provide qualitative information, such as type and purpose of PA, that differs from that provided by objective methods. However, the accuracy of questionnaires to measure PA intensity and EE is not sufficient. On the other hand, activity monitors are objective, small, non-invasive tools for measuring PA intensity and EE. Most NEAT is non-locomotive activities, and NEAT, especially NEAT due to non-locomotive activity, is difficult to measure under free-living conditions. Therefore, accurate metabolic equivalents estimation for non-locomotive and sedentary activities is required in addition to estimates of locomotive activity. Activity monitors can be used to study patterns of activity across time (e.g., bout or break). A new generation of accelerometers will provide information on body posture and activity recognition to allow objective assessment of subjects` habitual activities. Step counts mainly reflect moderate intensity PA, rather than total PA or physical activity level. The relationship between step counts and total PA depends on sex, age, and occupation. Accurate estimation of sedentary activities is important, because many people spend almost 10 hours/day in sedentary behavior. The prediction accuracy of lower- intensity PA is generally poor. One possible reason is the low sensitivity of accelerometers. Predictive equations for sedentary behavior with high accelerometer sensitivity may improve prediction accuracy. Conclusion: NEAT consists of non-locomotive activities. Now we can accurately estimate MET even for some non-locomotive and sedentary activities. Novel approaches have recently been developed to provide information on body posture and activity recognition. Therefore, a new generation of accelerometers will provide a greater variety of more accurate information on human activity/behavior continuously and objectively, including sedentary behavior and light activities and the context of those behaviors. Such development will contribute to better personalized lifestyle interventions for health promotion and will facilitate a higher quality of research on PA and sedentary behavior.