Photoplethysmography (PPG) is a non-invasive technique for recording human vital signs. PPG is normally recorded by wearable devices that are prone to artifacts. This results in signal corruption that decreases measurement accuracy. Thus, a signal quality assessment (SQA) system is essential in obtaining reliable measurements. Conventionally, SQA is mainly driven by human-knowledge and supervised through experts’ annotations. However, they are not tailored for the particularities of the domain applications. Hence, we propose a machine-aided SQA framework that generates respective quality criteria for applications. By using the proposed approach, quality criteria can be easily trained for different applications. Then, quality assessment can be applied to several PPG-based physiological signals telemonitoring. Compared with conventional approaches, the proposed system has a higher rejection rate for high-error signals and a lower mean absolute error is achieved when estimating heart rate (-3.06 BPM), determining respiration rate (–1.36 BPM), and predicting hypertension (+24%). The proposed method enhances accuracy in monitoring physiological signals and thus is suitable for healthcare applications.