Estimation of Instantaneous Oxygen Uptake During Exercise and Daily Activities Using a Wearable Cardio-Electromechanical and Environmental Sensor.

OBJECTIVE: To estimate instantaneous oxygen uptake VO2 with a small, low-cost wearable sensor during exercise and daily activities in order to enable monitoring of energy expenditure (EE) in uncontrolled settings. We aim to do so using a combination of seismocardiogram (SCG), electrocardiogram (ECG) and atmospheric pressure (AP) signals obtained from a minimally obtrusive wearable device. METHODS: In this study, subjects performed a treadmill protocol in a controlled environment and an outside walking protocol in an uncontrolled environment. During testing, the COSMED K5 metabolic system collected gold standard breath-by-breath (BxB) data and a custom-built wearable patch placed on the mid-sternum collected SCG, ECG and AP signals. We extracted features from these signals to estimate the BxB VO2 data obtained from the COSMED system. RESULTS: In estimating instantaneous VO2, we achieved our best results on the treadmill protocol using a combination of SCG (frequency) and AP features (RMSE of 3.68 ± 0.98 ml/kg/min and R2 of 0.77). For the outside protocol, we achieved our best results using a combination of SCG (frequency), ECG and AP features (RMSE of 4.3 ± 1.47 ml/kg/min and R2 of 0.64). In estimating VO2 consumed over one minute intervals during the protocols, our median percentage error was 15.8[Formula: see text] for the treadmill protocol and 20.5[Formula: see text] for the outside protocol. CONCLUSION: SCG, ECG and AP signals from a small wearable patch can enable accurate estimation of instantaneous VO2 in both controlled and uncontrolled settings. SCG signals capturing variation in cardio-mechanical processes, AP signals, and state of the art machine learning models contribute significantly to the accurate estimation of instantaneous VO2. SIGNIFICANCE: Accurate estimation of VO2 with a low cost, minimally obtrusive wearable patch can enable the monitoring of VO2 and EE in everyday settings and make the many applications of these measurements more accessible to the general public.

Classification of Decompensated Heart Failure From Clinical and Home Ballistocardiography.

OBJECTIVE: To improve home monitoring of heart failure patients so as to reduce emergency room visits and hospital readmissions. We aim to do this by analyzing the ballistocardiogram (BCG) to evaluate the clinical state of the patient. METHODS: 1) High quality BCG signals were collected at home from HF patients after discharge. 2) The BCG recordings were preprocessed to exclude outliers and artifacts. 3) Parameters of the BCG that contain information about the cardiovascular system were extracted. These features were used for the task of classification of the BCG recording based on the status of HF. RESULTS: The best AUC score for the task of classification obtained was 0.78 using slight variant of the leave one subject out validation method. CONCLUSION: This work demonstrates that high quality BCG signals can be collected in a home environment and used to detect the clinical state of HF patients. SIGNIFICANCE: In future work, a clinician/caregiver can be introduced into the system so that appropriate interventions can be performed based on the clinical state monitored at home.