Wearables with embedded electrodes and sensors are capable of continuously performing Electrocardiography (ECG), recording electrical activity of the heart, while estimating heart-rate of the wearer. Recent advances in wearable technology have generated comfortable flexible sensors that conform to the contours of the body and can measure heart-rate in the field by capturing QRS complexes of ECG signals. Due to various activities of daily living (ADL), skin deformation by means of lateral, rotational or skin stretching can cause changes in the current pathways of the sensor creating noisy data. The challenge is to disentangle the noise from the usable data in order to accurately detect QRS complexes of ECG signals. In this paper we design a framework to capture noise from a miniaturized flexible sensor, the Biostamp1 (with 4 leads), worn on the chest of 16 participants performing a set of structured ADL in a home setting, and a baseball player (pitcher). We present a machine-learning framework using a consensus fusion classifier comprising a Support Vector Machine and Neural Network learned model to remove noise while preserving neighboring R-peaks. We evaluate the model using Leave One Subject Out (LOSO) and yield an average of 83% F-measure on the 17 participants (including the pitcher). The low false negative rate provides accurate heart-rate detection on a finegrained (every 5 seconds) level, in the presence of intermittent stretching of the skin. Our results increase the reliability of detecting heart-rate in real-world and player settings, increasing the utility of flexible ECG-based sensors in the field.