Efectos de la pérdida de datos en las métricas de Variabilidad del Ritmo Cardíaco

La explosión en el mercado de dispositivos wearables ha supuesto una revolución en el ámbito de la monitorización de la salud. Gran parte de la población, incluida la población no paciente, posee dispositivos de pulsera capaces de detectar sus latidos a lo largo de todo el día. Juntocon las ventajas que esto supone, aparecen nuevos retos. Uno de ellos es la estabilidad de la calidad de la señal. Los movimientos constantes de estos dispositivos hacen que se produzcan grandes pérdidas de datos, que pueden ocasionar un deterioro de las mediciones. Esto es especialmente relevante en los dispositivos que analizan la variabilidad de ritmo cardíaco, una técnica que permite inferir información del sistema nervioso autónomo de forma no invasiva a partir del control que éste ejerce sobre el sistema circulatorio. Esta técnica necesita que todos los pulsos sean detectados para funcionar correctamente, por lo que la pérdida de datos supone inevitablemente un deterioro. Este trabajo se centra en investigar cómo se produce esta degradación para diferentes métodos y qué técnicas se pueden utilizar para reducirla. Para ello, se ha desarrollado un método de simulación de pérdida de pulsos que permite analizar los dos tipos de errores que se suelen dar: errores aleatoriamente distribuidos y en ráfagas. A su vez, se propone un nuevo método de rellenado de pulsos como una posibilidad de preprocesado, que obtiene mejores resultados que el método de referencia. Dependiendo de la aplicación y de los requerimientos de los dispositivos, se sugieren los métodos más robustos teniendo en cuenta también su coste y la información que proveen. Los métodos se han probado en una base de datos con 17 sujetos sometidos a una prueba de mesa basculante, que permite provocar cambios en la activación del sistema nervioso autónomo sin involucrar al sistema central o causar actividad en los músculos. Las métricas se han comparado tanto en la degradación de sus valores como en la capacidad para distinguir los cambios provocados por la prueba de mesa basculante.<br /

A Novel Adaptive Spectrum Noise Cancellation Approach for Enhancing Heartbeat Rate Monitoring in a Wearable Device

This paper presents a novel approach, Adaptive Spectrum Noise Cancellation (ASNC), for motion artifacts removal in Photoplethysmography (PPG) signals measured by an optical biosensor to obtain clean PPG waveforms for heartbeat rate calculation. One challenge faced by this optical sensing method is the inevitable noise induced by movement when the user is in motion, especially when the motion frequency is very close to the target heartbeat rate. The proposed ASNC utilizes the onboard accelerometer and gyroscope sensors to detect and remove the artifacts adaptively, thus obtaining accurate heartbeat rate measurement while in motion. The ASNC algorithm makes use of a commonly accepted spectrum analysis approaches in medical digital signal processing, discrete cosine transform, to carry out frequency domain analysis. Results obtained by the proposed ASNC have been compared to the classic algorithms, the adaptive threshold peak detection and adaptive noise cancellation. The mean (standard deviation) absolute error and mean relative error of heartbeat rate calculated by ASNC is 0.33 (0.57) beats·min-1 and 0.65%, by adaptive threshold peak detection algorithm is 2.29 (2.21) beats·min-1 and 8.38%, by adaptive noise cancellation algorithm is 1.70 (1.50) beats·min-1 and 2.02%. While all algorithms performed well with both simulated PPG data and clean PPG data collected from our Verity device in situations free of motion artifacts, ASNC provided better accuracy when motion artifacts increase, especially when motion frequency is very close to the heartbeat rate

Novel Machine Learning and Wearable Sensor Based Solutions for Smart Healthcare Monitoring

The advent of IoT has enabled the design of connected and integrated smart health monitoring systems. These health monitoring systems can be utilized for monitoring the mental and physical wellbeing of a person. Stress, anxiety, and hypertension are the major elements responsible for the plethora of physical and mental illnesses. In this context, the older population demands special attention because of the several age-related complications that exacerbate the effects of stress, anxiety, and hypertension. Monitoring stress, anxiety, and blood pressure regularly can prevent long-term damage by initiating necessary intervention or clinical treatment beforehand. This will improve the quality of life and reduce the burden on caregivers and the cost of healthcare. Therefore, this thesis explores novel technological solutions for real-time monitoring of stress, anxiety, and blood pressure using unobtrusive wearable sensors and machine learning techniques. The first contribution of this thesis is the experimental data collection of 50 healthy older adults, based on which, the works on stress detection and anxiety detection have been developed. The data collection procedure lasted for more than a year. We have collected physiological signals, salivary cortisol, and self-reported questionnaire feedback during the study. Salivary cortisol is an established clinical biomarker for physiological stress. Hence, a stress detection model that is trained to distinguish between the stressed and not-stressed states as indicated by the increase in cortisol level has the potential to facilitate clinical level diagnosis of stress from the comfort of their own home. The second contribution of the thesis is the development of a stress detection model based on fingertip sensors. We have extracted features from Electrodermal Activity (EDA) and Blood Volume Pulse (BVP) signals obtained from fingertip EDA and Photoplethysmogram (PPG) sensors to train machine learning algorithms for distinguishing between stressed and not-stressed states. We have evaluated the performance of four traditional machine learning algorithms and one deep-learning-based Long Short-Term Memory (LSTM) classifier. Results and analysis showed that the proposed LSTM classifier performed equally well as the traditional machine learning models. The third contribution of the thesis is to evaluate an integrated system of wrist-worn sensors for stress detection. We have evaluated four signal streams, EDA, BVP, Inter-Beat Interval (IBI), and Skin Temperature (ST) signals from EDA, PPG, and ST sensors. A random forest classifier was used for distinguishing between the stressed and not-stressed states. Results and analysis showed that incorporating features from different signals was able to reduce the misclassification rate of the classifier. Further, we have also prototyped the integration of the proposed wristband-based stress detection system in a consumer end device with voice capabilities. The fourth contribution of the thesis is the design of an anxiety detection model that uses features from a single wearable sensor and a context feature to improve the performance of the classification model. Using a context feature instead of integrating other physiological features for improving the performance of the model can reduce the complexity and cost of the anxiety detection model. In our proposed work, we have used a simple experimental context feature to highlight the importance of context in the accurate detection of anxious states. Our results and analysis have shown that with the addition of the context-based feature, the classifier was able to reduce misclassification by increasing the confidence of the decision. The final and the fifth contribution of the thesis is the validation of a proposed computational framework for the blood pressure estimation model. The proposed framework uses features from the PPG signal to estimate the systolic and diastolic blood pressure values using advanced regression techniques

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

Reconstructing Human Motion

This thesis presents methods for reconstructing human motion in a variety of applications and begins with an introduction to the general motion capture hardware and processing pipeline. Then, a data-driven method for the completion of corrupted marker-based motion capture data is presented. The approach is especially suitable for challenging cases, e.g., if complete marker sets of multiple body parts are missing over a long period of time. Using a large motion capture database and without the need for extensive preprocessing the method is able to fix missing markers across different actors and motion styles. The approach can be used for incrementally increasing prior-databases, as the underlying search technique for similar motions scales well to huge databases. The resulting clean motion database could then be used in the next application: a generic data-driven method for recognizing human full body actions from live motion capture data originating from various sources. The method queries an annotated motion capture database for similar motion segments, able to handle temporal deviations from the original motion. The approach is online-capable, works in realtime, requires virtually no preprocessing and is shown to work with a variety of feature sets extracted from input data including positional data, sparse accelerometer signals, skeletons extracted from depth sensors and even video data. Evaluation is done by comparing against a frame-based Support Vector Machine approach on a freely available motion database as well as a database containing Judo referee signal motions. In the last part, a method to indirectly reconstruct the effects of the human heart's pumping motion from video data of the face is applied in the context of epileptic seizures. These episodes usually feature interesting heart rate patterns like a significant increase at seizure start as well as seizure-type dependent drop-offs near the end. The pulse detection method is evaluated for applicability regarding seizure detection in a multitude of scenarios, ranging from videos recorded in a controlled clinical environment to patient supplied videos of seizures filmed with smartphones