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Recording a personal life log (PLL) of daily activities is an emerging technology for u-lifecare and e-health services. In this paper, we present an accelerometer-based personal life log system capable of human activity classification and exercise information generation. In our system, we use a tri-axial accelerometer and a real-time activity recognition scheme in which a set of augmented features of accelerometer signals, processed with Linear Discriminant Analysis (LDA), is classified by our hierarchical artificial neural network classifier: in the lower level of the classifier, a state of an activity is recognized based on the statistical and spectral features; in the upper level, an activity is recognized with a set of augmented features including autoregressive (AR) coefficients, signal magnitude area (SMA), and tilt angles (TA). Upon the recognition of each activity, we further estimate exercise information such as energy expenditure based on Metabolic Equivalents (METS), step count, walking distance, walking speed, activity duration, etc. Our PLL system functions in real-time and all information generated from our system is archived in a daily-log database. By testing our system on seven different daily activities, we have obtained an average accuracy of 84.8% in activity recognition and generated their relative exercise information.