Applications Of Wearable Sensors In Delivering Biologically Relevant Signals

With continued advancements in wearable technologies, the applications for their use are growing. Wearable sensors can be found in smart watches, fitness trackers, and even our cellphones. The common applications in everyday life are usually step counting, activity tracking, and heart rate monitoring. However, researchers have developed ways to use these similar sensors for clinically relevant diagnostic measures, as well as, improved athletic training and performance. Two areas of interest for the use of wearable sensors are mental health diagnostics in children and heart rate monitoring during intense physical activity from new locations, which are discussed further in this thesis. About 20% of children will experience an anxiety or depressive disorder. These disorders, if left untreated, can lead to comorbidity, substance abuse, and even suicide. Current methods for diagnosis are time consuming and only offered to those most at risk (i.e., reported or referred by a teacher, doctor, or parent). For the children that do get referred to a specialist, the process is often inaccurate. Researchers began using mood induction task to observe behavioral responses to specific stimuli in hopes to improve the accuracy of diagnostics. However, these methods involve long hours of training and watching videos of the activities. Recently, a few studies have focused on using wearable sensors during mood induction tasks in hopes to pick up on relevant movements to distinguish those with and without an internalizing disorder. The first study presented in this thesis focuses on using wearable inertial measurement units during the ‘Bubbles’ mood induction task. A decision tree was developed to identify children with internalizing disorders, accuracy of this model was 71% based on leave-one-subject-out cross validation. The second study focuses on estimating heart rate using wearable photoplethysmography sensors at multiple body locations. Heart rate is an important vital sign used across a variety of contexts. For example, athletes use heart rate to determine whether they are hitting their desired heart rate zones during training and doctors can use heart rate as an early indicator of disease. With the advancements made in wearables, photoplethysmography can now be used to collect signals from devices anywhere on the body. However, estimating heart rate accurately during periods of intense physical activity remains a challenge due to signal corruption cause by motion artifacts. This study focuses on evaluating algorithms for accurately estimating heart rate from photoplethysmograms and determining the optimal body location for wear. A phase vocoder and Wiener filtering approach was used to estimate heart rate from the forearm, shank, and sacrum. The algorithm estimated heart rate to within 6.2 6.9, and 6.7 beats per minute average absolute error for the forearm, shank, and sacrum, respectively, across a wide variety of physical activities selected to induce varying levels of motion artifact

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

Energy Efficient Computing with Time-Based Digital Circuits

University of Minnesota Ph.D. dissertation. May 2019. Major: Electrical Engineering. Advisor: Chris Kim. 1 computer file (PDF); xv, 150 pages.Advancements in semiconductor technology have given the world economical, abundant, and reliable computing resources which have enabled countless breakthroughs in science, medicine, and agriculture which have improved the lives of many. Due to physics, the rate of these advancements is slowing, while the demand for the increasing computing horsepower ever grows. Novel computer architectures that leverage the foundation of conventional systems must become mainstream to continue providing the improved hardware required by engineers, scientists, and governments to innovate. This thesis provides a path forward by introducing multiple time-based computing architectures for a diverse range of applications. Simply put, time-based computing encodes the output of the computation in the time it takes to generate the result. Conventional systems encode this information in voltages across multiple signals; the performance of these systems is tightly coupled to improvements in semiconductor technology. Time-based computing elegantly uses the simplest of components from conventional systems to efficiently compute complex results. Two time-based neuromorphic computing platforms, based on a ring oscillator and a digital delay line, are described. An analog-to-digital converter is designed in the time domain using a beat frequency circuit which is used to record brain activity. A novel path planning architecture, with designs for 2D and 3D routes, is implemented in the time domain. Finally, a machine learning application using time domain inputs enables improved performance of heart rate prediction, biometric identification, and introduces a new method for using machine learning to predict temporal signal sequences. As these innovative architectures are presented, it will become clear the way forward will be increasingly enabled with time-based designs

Characterizing the Noise Associated with Sensor Placement and Motion Artifacts and Overcoming its Effects for Body-worn Physiological Sensors

Wearable sensors for continuous physiological monitoring have the potential to change the paradigm for healthcare by providing information in scenarios not covered by the existing clinical model. One key challenge for wearable physiological sensors is that their signal-to-noise ratios are low compared to those of their medical grade counterparts in hospitals. Two primary sources of noise are the sensor-skin contact interface and motion artifacts due to the user’s daily activities. These are challenging problems because the initial sensor placement by the user may not be ideal, the skin conditions can change over time, and the nature of motion artifacts is not predictable. The objective of this research is twofold. The first is to design sensors with reconfigurable contact to mitigate the effects of misplaced sensors or changing skin conditions. The second is to leverage signal processing techniques for accurate physiological parameter estimation despite the presence of motion artifacts. In this research, the sensor contact problem was specifically addressed for dry-contact electroencephalography (EEG). The proposed novel extension to a popular existing EEG electrode design enabled reconfigurable contact to adjust to variations in sensor placement and skin conditions over time. Experimental results on human subjects showed that reconfiguration of contact can reduce the noise in collected EEG signals without the need for manual intervention. To address the motion artifact problem, a particle filter based approach was employed to track the heart rate in cardiac signals affected by the movements of the user. The algorithm was tested on cardiac signals from human subjects running on a treadmill and showed good performance in accurately tracking heart rate. Moreover, the proposed algorithm enables fusion of multiple modalities and is also computationally more efficient compared to other contemporary approaches

Signal processing techniques for cardiovascular monitoring applications using conventional and video-based photoplethysmography

Photoplethysmography (PPG)-based monitoring devices will probably play a decisive role in healthcare environment of the future, which will be preventive, predictive, personalized and participatory. Indeed, this optical technology presents several practical advantages over gold standard methods based on electrocardiography, because PPG wearable devices can be comfortably used for long-term continuous monitoring during daily life activities. Contactless video-based PPG technique, also known as imaging photoplethysmography (iPPG), has also attracted much attention recently. In that case, the cardiac pulse is remotely measured from the subtle skin color changes resulting from the blood circulation, using a simple video camera. PPG/iPPG have a lot of potential for a wide range of cardiovascular applications. Hence, there is a substantial need for signal processing techniques to explore these applications and to improve the reliability of the PPG/iPPG-based parameters. \par A part of the thesis is dedicated to the development of robust processing schemes to estimate heart rate from the PPG/iPPG signals. The proposed approaches were built on adaptive frequency tracking algorithms that were previously developed in our group. These tools, based on adaptive band-pass filters, provide instantaneous frequency estimates of the input signal(s) with a very low time delay, making them suitable for real-time applications. In case of conventional PPG, a prior adaptive noise cancellation step involving the use of accelerometer signals was also necessary to reconstruct clean PPG signals during the regions corrupted by motion artifacts. Regarding iPPG, after comparing different regions of interest on the subject face, we hypothesized that the simultaneous use of different iPPG signal derivation methods (i.e. methods to derive the iPPG time series from the pixel values of the consecutive frames) could be advantageous. Methods to assess signal quality online and to incorporate it into instantaneous frequency estimation were also examined and successfully applied to improve system reliability. \par This thesis also explored different innovative applications involving PPG/iPPG signals. The detection of atrial fibrillation was studied. Novel features derived directly from the PPG waveforms, designed to reflect the morphological changes observed during arrhythmic episodes, were proposed and proven to be successful for atrial fibrillation detection. Arrhythmia detection and robust heart rate estimation approaches were combined in another study aimed at reducing the number of false arrhythmia alarms in the intensive care unit by exploiting signals from independent sources, including PPG. Evaluation on a hidden dataset demonstrated that the number of false alarms was drastically reduced while almost no true alarm was suppressed. Finally, other aspects of the iPPG technology were examined, such as the measurement of pulse rate variability indexes from the iPPG signals and the estimation of respiratory rate from the iPPG interbeat intervals

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

Acoustic sensing as a novel approach for cardiovascular monitoring at the wrist

Cardiovascular diseases are the number one cause of deaths globally. An increased cardiovascular risk can be detected by a regular monitoring of the vital signs including the heart rate, the heart rate variability (HRV) and the blood pressure. For a user to undergo continuous vital sign monitoring, wearable systems prove to be very useful as the device can be integrated into the user's lifestyle without affecting the daily activities. However, the main challenge associated with the monitoring of these cardiovascular parameters is the requirement of different sensing mechanisms at different measurement sites. There is not a single wearable device that can provide sufficient physiological information to track the vital signs from a single site on the body. This thesis proposes a novel concept of using acoustic sensing over the radial artery to extract cardiac parameters for vital sign monitoring. A wearable system consisting of a microphone is designed to allow the detection of the heart sounds together with the pulse wave, an attribute not possible with existing wrist-based sensing methods. Methods: The acoustic signals recorded from the radial artery are a continuous reflection of the instantaneous cardiac activity. These signals are studied and characterised using different algorithms to extract cardiovascular parameters. The validity of the proposed principle is firstly demonstrated using a novel algorithm to extract the heart rate from these signals. The algorithm utilises the power spectral analysis of the acoustic pulse signal to detect the S1 sounds and additionally, the K-means method to remove motion artifacts for an accurate heartbeat detection. The HRV in the short-term acoustic recordings is found by extracting the S1 events using the relative information between the short- and long-term energies of the signal. The S1 events are localised using three different characteristic points and the best representation is found by comparing the instantaneous heart rate profiles. The possibility of measuring the blood pressure using the wearable device is shown by recording the acoustic signal under the influence of external pressure applied on the arterial branch. The temporal and spectral characteristics of the acoustic signal are utilised to extract the feature signals and obtain a relationship with the systolic blood pressure (SBP) and diastolic blood pressure (DBP) respectively. Results: This thesis proposes three different algorithms to find the heart rate, the HRV and the SBP/ DBP readings from the acoustic signals recorded at the wrist. The results obtained by each algorithm are as follows: 1. The heart rate algorithm is validated on a dataset consisting of 12 subjects with a data length of 6 hours. The results demonstrate an accuracy of 98.78%, mean absolute error of 0.28 bpm, limits of agreement between -1.68 and 1.69 bpm, and a correlation coefficient of 0.998 with reference to a state-of-the-art PPG-based commercial device. A high statistical agreement between the heart rate obtained from the acoustic signal and the photoplethysmography (PPG) signal is observed. 2. The HRV algorithm is validated on the short-term acoustic signals of 5-minutes duration recorded from each of the 12 subjects. A comparison is established with the simultaneously recorded electrocardiography (ECG) and PPG signals respectively. The instantaneous heart rate for all the subjects combined together achieves an accuracy of 98.50% and 98.96% with respect to the ECG and PPG signals respectively. The results for the time-domain and frequency-domain HRV parameters also demonstrate high statistical agreement with the ECG and PPG signals respectively. 3. The algorithm proposed for the SBP/ DBP determination is validated on 104 acoustic signals recorded from 40 adult subjects. The experimental outputs when compared with the reference arm- and wrist-based monitors produce a mean error of less than 2 mmHg and a standard deviation of error around 6 mmHg. Based on these results, this thesis shows the potential of this new sensing modality to be used as an alternative, or to complement existing methods, for the continuous monitoring of heart rate and HRV, and spot measurement of the blood pressure at the wrist.Open Acces

Evaluation of Wearable Optical Heart Rate Monitoring Sensors

Heart rate monitoring provides valuable information about an individual’s physiological condition. The information obtained from heart rate monitoring can be used for a wide range of purposes such as clinical diagnostics, assessment of the efficiency of training for sports and fitness, or of sleep quality and stress levels in wellbeing applications. Other useful parameters for describing a person’s fitness, such as maximal oxygen uptake and energy expenditure, can also be estimated using heart rate measurement. The traditional ‘gold standard’ for heart rate monitoring is the electrocardiograph, but nowadays there are a number of alternative methods too. Of these, optical sensors provide a relatively simple, lowcost and unobtrusive technology for monitoring heart rate and they are widely accepted by users. There are many factors affecting the measurement of optical signals that have an effect on the accuracy of heart rate estimation. However, there is a lack of standardized and unified methodology for comparing the accuracy of optical heart rate sensors to the ‘gold standard’ methods of measuring heart rate. The widespread use of optical sensors for different purposes has led to a pressing need for a common objective methodology for the evaluation of how accurate these sensors are. This thesis presents a methodology for the objective evaluation of optical heart-rate sensors. The methodology is applied in evaluation studies of four commercially available optical sensors. These evaluations were carried out during both controlled and non-controlled sporting and daily life activities. In addition, evaluation of beat detection accuracy was carried out in non-controlled sleep conditions. The accuracy of wrist-worn optical heart-rate sensors in estimating of maximal oxygen uptake during submaximal exercise and energy expenditure during maximal exercise using heart rate as input parameter were also evaluated. The accuracy of a semi-continuous heart rate estimation algorithm designed to reduce power consumption for long-term monitoring was also evaluated in various conditions. The main findings show that optical heart-rate sensors may be highly accurate during rhythmic sports activities, such as jogging, running, and cycling, including ramp-up running during maximal exercise testing. During non-rhythmic activities, such as intermittent hand movements, the sensors’ accuracy depends on where they are worn. During sleep and motionless conditions, the optical heart-rate sensors’ estimates for beat detection and inter-beat interval showed less than one percent inaccuracy against the values obtained using standard measurement techniques. The sensors were also sufficiently accurate at measuring the interbeat intervals to be used for calculating the heart rate variability parameters. The estimation accuracy of the fitness parameters derived from measured heart rate can be described as follows. An assessment of the maximal oxygen uptake estimation during a sub-maximal outdoor exercise had a precision close to a sport laboratory measurement. The energy expenditure estimation during a maximal exercise was more accurate during higher intensity of exercise above aerobic threshold but the accuracy decreased at lower intensity of exercise below the aerobic threshold, in comparison with the standardized reference measurement. The semi-continuous algorithm was nearly as accurate as continuous heart-rate detection, and there was a significant reduction in the power consumption of the optical chain components up to eighty percent. The results obtained from these studies show that, under certain conditions, optical sensors may be similarly accurate in measuring heart rate as the ‘gold standard’ methods and they can be relied on to monitor heart rate for various purposes during sport, everyday activities, or sleep