More and more efforts have been made to assist interdependent people in aging society today, particularly to the elderly living alone at home. This paper proposes an activity recognition system based on detection of acoustic events caused by residents within the living space. In the data collection process, an activity sound recording system with auto gain controlled and linearly amplified capability is equipped in a ward-like environment. The microphones are installed along the path from bedroom to lavatory. An activity sound database containing 105 designed scenarios is simulated and transcript in terms of 21 acoustic events such as footfall, fall down, and calling for help to form the acoustic event database. 21 Hidden Markov models (HMM) corresponding to acoustic events are trained by the acoustic event database. A keyword spotting recognition technology is adopted to recognize acoustic events and its ordered sequences for modeling activities. Acoustic features are computed by 39-dimensional mel-scaled frequency cepstral coefficients (MFCCs) with their first and second derivatives. The preliminary results show the average recognition rate of acoustic events is 91% for inside testing and 72% for outside testing respectively. The proposed approach shows an encouraging potential on the demand of modeling the regular living patterns of every variety.