Demographic changes are driving an urgent need for Ambient Assisted Living (AAL) technologies to support independent living for the elderly. A critical capability of such systems is the automated detection of health-related anomalies in daily routines, potentially serving as a partial replacement for human monitoring. While Artificial Intelligence (AI) offers promising solutions, the application of Machine Learning (ML) in this domain is severely hampered by the scarcity of labeled reference data, rendering supervised approaches impractical. Furthermore, unsupervised models often operate as “black boxes,” lacking the interpretability required for caregivers to trust and act upon alerts. Following Peffers et al.’s Design Science Research (DSR) methodology, this paper presents the design, development, and evaluation of a web-based artifact that utilizes unsupervised algorithms (Isolation Forest and LSTM-Autoencoder) to detect anomalies in unlabeled sensor data. To ensure practical relevance and trust, the system integrates Explainable AI (XAI) using SHAP values to contextualize alerts. The artifact was evaluated through a dual-perspective approach: a quantitative analysis of model agreement on unlabeled data, and a qualitative study involving a focus group with AAL experts ( \(n=6\) ) and a technical review by a domain specialist. The results highlight the tension between algorithmic precision and human interpretability, contributing validated design guidelines for trust-aware, unsupervised monitoring systems.