One of the main objectives of Ambient Assisted Living (AAL) systems is to proactively provide intelligent services to improve the quality of people's lives in terms of autonomy, safety, and well-being. Designing AAL systems that can autonomously monitor human's activities and provide assistance services poses several challenges of which Human Activity Recognition (HAR) which is critically important to adapt the assistance services to the user. In this letter, a robust multi-label HAR framework is proposed. The proposed framework is composed of two main modules: (i) activity classification module and (ii) classification error detection and correction module. In the first module, machine-learning models are used to predict human activities. Since these models may produce predictions with errors, there is a requirement to detect and correct these errors. The classification error detection and correction module is based on two acyclic directed graphical models and operates in two phases: (i) classification error detection and (ii) classification error correction. The proposed framework is evaluated on the Opportunity dataset, a benchmark and a unique dataset for multi-label human daily living activity recognition. The obtained results demonstrate the ability of the proposed framework to improve the performances of HAR.