Sensor-based human activity recognition (HAR) has received considerable attention due to its wide applications in health care. Each sensor modality has its advantages and limitations. Single sensor modalities sometimes may not cope with complex situations in practice. To resolve this challenge, we design and develop a practical hybrid sensory HAR system for older people. To enhance the performance of the system, we propose a unique data fusion method through combining both wearable sensors and ambient sensors. The wearable sensors in this paper are used for identifying the specific daily activities. The ambient sensors delivering the occupant's room-level daily routine provide a more comprehensive surveillance with the wearable sensors together; meanwhile, the captured room-level location information is also used in the data fusion to trigger the sub classification models pretrained by wearable data. We also explore a new feature set extracted from wearable sensors to improve the system performance. We experimentally evaluate our system by applying four typical mutual information-based feature selection methods and the support vector machines classification algorithm instead of other complex algorithms, with the aim of exploring a practical way to improve recognition accuracy. The ground-truth data are gathered from 21 subjects, including 17 daily activities with the sample size of 2,142,000. The experimental results demonstrate the effectiveness of our method. The new feature set help improve the accuracy to 96.82% ± 0.15 from 89.81% ± 0.54 using wearable data only; and the data fusion with ambient information achieves a further increased accuracy of 98.32%.