Human activity recognition (HAR) has attracted widespread attention in areas such as human–computer interaction, work performance management, and healthcare. Owing to advantages such as continuous monitoring, reduced cost of deployment, and ease of privacy protection, wearable-sensor-based HAR is preferred over the traditional approach of using external sensors. In this study, the influence of different combinations of seven body-worn accelerometer positions on the classification of 23 complex daily activities was examined. A conventional machine learning model, namely, RandomForest (RF), and two deep-learning (DL) models, convolutional neural network (CNN)-long short-time memory (LSTM) and CNN-transformer, were used to understand the impact of using different models on the classification performance. The results showed a strong correlation between the classification models regarding the combinations of sensor positions and classification performance (F1-score). Additionally, the combination of the four sensors from the left and right wrists, right upper arm, and right thigh was determined to be the best. This study also showed that, owing to feature calculation, the RF model took a longer processing time than the DL-based models and that the CNN-LSTM model would be preferable to RF if plenty of data were available for training it. The results can provide a reference for application designers in choosing appropriate combinations of sensor positions based on requirements for wearability and classification performance.