Population aging increases the need for accessible cardiac rhythm screening outside clinical settings. We present a proof-of-concept, low-complexity pipeline for at-home electrocardiogram (ECG) rhythm screening tailored to Ambient Assisted Living (AAL). To mimic single-lead devices, we extract interpretable features from lead II, including ventricular rate, QRS duration, QT/QTc, RR-interval statistics, and simple counts derived from wavelet-based delineation. We compare a compact Fully Connected Neural Network (FCN) against a tree-based model (XGBoost). Models are developed on ChapmanECG under an 11-class label space and evaluated in matched splits; XGBoost yields higher mean accuracy than the FCN. For an external, system-level check, we apply the trained pipeline to short excerpts from the MIT-BIH Arrhythmia Database and harmonize labels via a graded protocol (Correct/Partially Correct/Incorrect) to account for taxonomy differences. Under this protocol, the pipeline attains 82.98% accuracy. While not a diagnostic study, the findings indicate that feature-based, interpretable models can provide practical, low-weight rhythm screening suitable for AAL contexts and merit further validation with clinically curated, single-lead home recordings.