With the increasing use of mobile devices as personal record- ing, communication and sensing tools, extracting the seman- tics of life activities through sensed data (photos, accelerom- eter, GPS etc.) is gaining widespread public awareness. A person who engages in long-term personal sensing is engag- ing in a process of lifelogging. Lifelogging typically involves using a range of (wearable) sensors to capture raw data, to segment into discrete activities, to annotate and subse- quently to make accessible by search or browsing tools. In this paper, we present an intuitive lifelog activity record- ing and management system called ZhiWo. By using a su- pervised machine learning approach, sensed data collected by mobile devices are automatically classified into different types of daily human activities and these activities are inter- preted as life activity retrieval units for personal archives.