Physicians assess a patient’s respiratory health by detection of abnormal lung sounds, such as crackles and wheezes, found during pulmonary auscultation using their stethoscope and on their physical examination by interpretation of their respiratory rate combined with a visual assessment of the work of breathing (WoB) to identify common pathological lung diseases. Since these methods are subjective, a low-profile device with a capable, accurate, and quantitative remote monitoring approach, could provide valuable preemptive insights into a patient’s respiratory health, proving to be clinically beneficial. To achieve this goal, we have used a miniature lung patch consisting of a sensitive wideband MEMS seismometer that can be individually placed on the anatomical areas of a patient’s lungs to replicate traditional lung auscultation and WoB assessment. This seismometer patch captures the patient’s lung sounds as pulmonary induced vibrations (PIVs) during deep breathing and inspiratory effort via high-frequency mechanomyogram (MMG) during tidal breathing. It also detects corresponding low frequency patterns, specifically respiratory rate and pattern during the breathing cycle. To determine if the seismometer patch recordings can be used to automatically detect adventitious lung sounds, a binary classifier of wheeze versus normal breath sounds was first used to determine its applicability. The binary classifier was later expanded to a categorical classifier with using a novel data fusion deep learning model to determine if the recording contained normal, crackles or wheezing. The data fusion deep learning model was developed with combined inputs of PIV lung sounds and corresponding respiratory phase. The categorical data fusion deep learning architecture exhibited high accuracy, sensitivity, specificity, precision and F1 score of 93%, 93%, 97%, 93% and 93% respectively. The seismometer patch was able to accurately quantity a patent's work of breathing (WoB) by combining the average inspiratory effort via high-frequency mechanomyogram (MMG) signals and respiratory rate compared to the clinical standard. This work empowers remote patient monitoring via adventitious lung sound detection with PIVs acoustic map and non-invasive WoB measurement, providing essential respiratory data for tracking of pulmonary disease development.