Evaluation of pulse rate measurement with a wrist worn device during different tasks and physical activity

The purpose of this study was to evaluate the wrist-worn device Mio FUSE, which estimates heart rate (HR) based on photo-plethysmography, 1) in a large study group during a standardised activity, 2) in a small group during a variety of activities and 3) to investigate factors affecting HR accuracy in a real-world setting. First, 53 male participants (20 ±1 years; 1.79 ±0.07 m; 76.1 ±10.5 kg) completed a 35-km march wearing the Equivital EQ-02 as a criterion measure. Second, 5 participants (whereof 3 female; 29 ±5 years; 1.74 ±0.07 m; 67.8 ±11.1 kg) independently performed 25 activities, categorised as sitting passive, sitting active, standing, cyclic and anti-cyclic activities with the Polar H7 as a criterion device. Equivalence testing and Bland-and-Altman analyses were undertaken to assess the accuracy to the criterion devices. Third, confounders affecting HR accuracy were investigated using multiple backwards regression analyses. The Mio FUSE was equivalent to the respective criterion measures with only small systematic biases of -3.5 bpm (-2.6%) and -1.7 bpm (-1.3%) with limits of agreements of ±10.1 bpm and ±10.8 bpm during the 35-km march and during different activities, respectively. Confounding factors negatively affecting the accuracy of the Mio FUSE were found to include larger wrist size and intensified arm and/or wrist movement. The wrist-worn Mio FUSE can be recommended to estimate overall HR accurately for different types of activities in healthy adults. However, during sporting activities involving intensified arm and/or wrist movement or for detailed continuous analysis, a chest strap is preferred to the Mio FUSE to optimise HR estimation accuracy

Hardware Prototype for Wrist-Worn Simultaneous Monitoring of Environmental, Behavioral, and Physiological Parameters

We designed a low-cost wrist-worn prototype for simultaneously measuring environmental, behavioral, and physiological domains of influencing factors in healthcare. Our prototype continuously monitors ambient elements (sound level, toxic gases, ultraviolet radiation, air pressure, temperature, and humidity), personal activity (motion tracking and body positioning using gyroscope, magnetometer, and accelerometer), and vital signs (skin temperature and heart rate). An innovative three-dimensional hardware, based on the multi-physical-layer approach is introduced. Using board-to-board connectors, several physical hardware layers are stacked on top of each other. All of these layers consist of integrated and/or add-on sensors to measure certain domain (environmental, behavioral, or physiological). The prototype includes centralized data processing, transmission, and visualization. Bi-directional communication is based on Bluetooth Low Energy (BLE) and can connect to smartphones as well as smart cars and smart homes for data analytic and adverse-event alerts. This study aims to develop a prototype for simultaneous monitoring of the all three areas for monitoring of workplaces and chronic obstructive pulmonary disease (COPD) patients with a concentration on technical development and validation rather than clinical investigation. We have implemented 6 prototypes which have been tested by 5 volunteers. We have asked the subjects to test the prototype in a daily routine in both indoor (workplaces and laboratories) and outdoor. We have not imposed any specific conditions for the tests. All presented data in this work are from the same prototype. Eleven sensors measure fifteen parameters from three domains. The prototype delivers the resolutions of 0.1 part per million (PPM) for air quality parameters, 1 dB, 1 index, and 1 °C for sound pressure level, UV, and skin temperature, respectively. The battery operates for 12.5 h under the maximum sampling rates of sensors without recharging. The final expense does not exceed 133€. We validated all layers and tested the entire device with a 75 min recording. The results show the appropriate functionalities of the prototype for further development and investigations

Data-driven methods for analyzing ballistocardiograms in longitudinal cardiovascular monitoring

Cardiovascular disease (CVD) is the leading cause of death in the US; about 48% of American adults have one or more types of CVD. The importance of continuous monitoring of the older population, for early detection of changes in health conditions, has been shown in the literature, as the key to a successful clinical intervention. We have been investigating environmentally-embedded in-home networks of non-invasive sensing modalities. This dissertation concentrates on the signal processing techniques required for the robust extraction of morphological features from the ballistocardiographs (BCG), and machine learning approaches to utilize these features in non-invasive monitoring of cardiovascular conditions. At first, enhancements in the time domain detection of the cardiac cycle are addressed due to its importance in the estimation of heart rate variability (HRV) and sleep stages. The proposed enhancements in the energy-based algorithm for BCG beat detection have shown at least 50% improvement in the root mean square error (RMSE) of the beat to beat heart rate estimations compared to the reference estimations from the electrocardiogram (ECG) R to R intervals. These results are still subject to some errors, primarily due to the contamination of noise and motion artifacts caused by floor vibration, unconstrained subject movements, or even the respiratory activities. Aging, diseases, breathing, and sleep disorders can also affect the quality of estimation as they slightly modify the morphology of the BCG waveform.Includes bibliographical reference