Effects of Ballistocardiogram Peak Detection Jitters on the Quality of Heart Rate Variability Features: A Simulation-Based Case Study in the Context of Sleep Staging.

Heart rate variability (HRV) features support several clinical applications, including sleep staging, and ballistocardiograms (BCGs) can be used to unobtrusively estimate these features. Electrocardiography is the traditional clinical standard for HRV estimation, but BCGs and electrocardiograms (ECGs) yield different estimates for heartbeat intervals (HBIs), leading to differences in calculated HRV parameters. This study examines the viability of using BCG-based HRV features for sleep staging by quantifying the impact of these timing differences on the resulting parameters of interest. We introduced a range of synthetic time offsets to simulate the differences between BCG- and ECG-based heartbeat intervals, and the resulting HRV features are used to perform sleep staging. Subsequently, we draw a relationship between the mean absolute error in HBIs and the resulting sleep-staging performances. We also extend our previous work in heartbeat interval identification algorithms to demonstrate that our simulated timing jitters are close representatives of errors between heartbeat interval measurements. This work indicates that BCG-based sleep staging can produce accuracies comparable to ECG-based techniques such that at an HBI error range of up to 60 ms, the sleep-scoring error could increase from 17% to 25% based on one of the scenarios we examined.

Pen-Type Electrodermal Activity Sensing System for Stress Detection Based on Likelihood Ratios.

Psychological stress experienced during academic testing is a significant performance factor for some students. While a student may be able to recognize and self-report exam stress, unobtrusive tools to track stress in real time and in association with specific test problems are lacking. This effort pursued the design and initial assessment of an electrodermal activity (EDA) sensor mounted to a pen/pencil 'trainer:' a holder into which a pen/pencil is inserted that can help a person learn how to properly grip a writing instrument. This small assembly was held in the hand of each subject during early experiments and can be used for follow-on, mock test-taking scenarios. In these experiments, data were acquired with this handheld device for each of 36 subjects (Kansas State University Internal Review Board Protocol #9864) while they viewed approximately 30 minutes of emotion-evoking videos. Data collected by the EDA sensor were analyzed by an EDA signal processing app, which calculated and stored parameters associated with significant phasic EDA peaks while allowing intermediate peak detection processes to be visualized. These peak data were then subjected to a hypothesis driven stress-detection test that employed likelihood ratios to identify 'relaxed' versus 'stressed' events. For these initial testing scenarios, which were free of hand motions, this pen-type EDA sensing system discerned 'relaxed' versus 'stressed' phasic responses with 87.5% accuracy on average, where subject self-assessments of perceived stress levels were used to establish ground truth.

Smart bed based daytime behavior prediction in Children with autism spectrum disorder - A Pilot Study.

Monitoring the sleep patterns of children with autism spectrum disorders (ASD) and understanding how sleep quality influences their daytime behavior is an important issue that has received very limited attention. Polysomnography (PSG) is commonly used as a gold standard for evaluating sleep quality in children and adults. However, the intrusive nature of sensors used as part of PSG can themselves affect sleep and is, therefore, not suitable for children with ASD. In this study, we evaluate an unobtrusive and inexpensive bed system for in-home, long-term sleep quality monitoring using ballistocardiogram (BCG) signals. Using the BCG signals from this smart bed system, we define "restlessness" as a surrogate sleep quality estimator. Using this sleep feature, we build predictive models for daytime behavior based on 1-8 previous nights of sleep. Specifically, we use two supervised machine learning algorithms namely support vector machine (SVM) and artificial neural network (ANN). For all daytime behaviors, we achieve more than 78% and 79% accuracy of correctly predicting behavioral issues with both SVM and ANN classifiers, respectively. Our findings indicate the usefulness of our designed bed system and how the restlessness feature can improve the prediction performance.

Bed-Based Ballistocardiography: Dataset and Ability to Track Cardiovascular Parameters.

BACKGROUND: The goal of this work was to create a sharable dataset of heart-driven signals, including ballistocardiograms (BCGs) and time-aligned electrocardiograms (ECGs), photoplethysmograms (PPGs), and blood pressure waveforms. METHODS: A custom, bed-based ballistocardiographic system is described in detail. Affiliated cardiopulmonary signals are acquired using a GE Datex CardioCap 5 patient monitor (which collects ECG and PPG data) and a Finapres Medical Systems Finometer PRO (which provides continuous reconstructed brachial artery pressure waveforms and derived cardiovascular parameters). RESULTS: Data were collected from 40 participants, 4 of whom had been or were currently diagnosed with a heart condition at the time they enrolled in the study. An investigation revealed that features extracted from a BCG could be used to track changes in systolic blood pressure (Pearson correlation coefficient of 0.54 +/- 0.15), dP/dtmax (Pearson correlation coefficient of 0.51 +/- 0.18), and stroke volume (Pearson correlation coefficient of 0.54 +/- 0.17). CONCLUSION: A collection of synchronized, heart-driven signals, including BCGs, ECGs, PPGs, and blood pressure waveforms, was acquired and made publicly available. An initial study indicated that bed-based ballistocardiography can be used to track beat-to-beat changes in systolic blood pressure and stroke volume. SIGNIFICANCE: To the best of the authors' knowledge, no other database that includes time-aligned ECG, PPG, BCG, and continuous blood pressure data is available to the public. This dataset could be used by other researchers for algorithm testing and development in this fast-growing field of health assessment, without requiring these individuals to invest considerable time and resources into hardware development and data collection.

A Pilot Study of an Unobtrusive Bed-Based Sleep Quality Monitor for Severely Disabled Autistic Children.

The link between daytime performance and sleep quality for severely disabled autistic children is not entirely understood. This paper presents nighttime data collected from a child with severe disabilities during a three-night pilot study conducted at Heartspring, Wichita, KS, using a bed-based system capable of unobtrusively tracking parameters for sleep quality assessment. The 'average sample correlation coefficient signal-to-noise ratio' is compared for ballistocardiograms acquired using four electromechanical film sensors versus four load cell sensors. The "best" signal or sensing modality depends on the subject's sleeping position. These results affirm the importance of a bed system that is robust in its ability to track sleep quality accurately regardless of sleeping position.

Performance Evaluation of Processing Methods for Ballistocardiogram Peak Detection.

Several methods are proposed in the literature to detect ballistocardiogram (BCG) peaks. There is a need to narrow these methods down in terms of their performance under similar conditions. This study reports early results from a systematic performance evaluation. To date, we have replicated three methods from the literature and compared their performance using data from five volunteers. A basic cross-correlation approach was also included as a baseline level of performance. The best-performing method had an average peak-detection success rate of 95.0{%, associated with 0.1090 average false alarms per second and 0.0078 s mean standard deviation between real and detected peaks.

Activities to Invigorate a Student Chapter of the IEEE Engineering in Medicine and Biology Society.

An IEEE Engineering in Medicine and Biology Society (EMBS) student chapter can play an important service role for collegiate biomedical curricula, supporting (a) faculty and administrators as they offer biomedical programs and work to strengthen industry/community relationships, and (b) students as they engage in engineering skill development and seek industry employment, graduate school opportunities, or medical school placement. This paper summarizes recent projects and activities sponsored by the Kansas State University (KSU) Student Chapter of the IEEE EMBS - efforts intended to maintain interest in the student chapter while supporting its service role. Such a role will become more important in upcoming years in light of the increasing demand for biomedical engineers, especially in the Midwest United States, a reality which motivated the inception of a new KSU undergraduate degree in Biomedical Engineering starting in Fall 2018. The KSU IEEE EMBS student chapter can play a large role in the overall success of this new curriculum, and the projects and activities summarized in this paper are offered as examples to programs that may wish to benefit from an IEEE EMBS student chapter in a similar and meaningful way.

Prediction of Lunar- and Martian-Based Intra- and Site-to-Site Task Performance.

BACKGROUND: This study aimed to investigate the feasibility of determining the physiological parameters associated with the ability to complete simulated exploration type tasks at metabolic rates which might be expected for lunar and Martian ambulation. METHODS: Running V̇O2max and gas exchange threshold (GET) were measured in 21 volunteers. Two simulated extravehicular activity field tests were completed in 1 G in regular athletic apparel at two intensities designed to elicit metabolic rates of ∼20.0 and ∼30.0 ml · kg(-1) · min(-1), which are similar to those previously reported for ambulation in simulated lunar- and Martian-based environments, respectively. RESULTS: All subjects were able to complete the field test at the lunar intensity, but 28% were unable to complete the field test at the Martian intensity (non-Finishers). During the Martian field test there were no differences in V̇O2 between Finishers and non-Finishers, but the non-Finishers achieved a greater %V̇O2max compared to Finishers (78.4 ± 4.6% vs. 64.9 ± 9.6%). Logistic regression analysis revealed fitness thresholds for a predicted probability of 0.5, at which Finishing and non-Finishing are equally likely, and 0.75, at which an individual has a 75% chance of Finishing, to be a V̇O2max of 38.4 ml · kg(-1) · min(-1) and 40.0 ml · kg(-1) · min(-1) or a GET of 20.1 ml · kg(-1) · min(-1) and 25.1 ml · kg(-1) · min(-1), respectively (χ(2) = 10.2). Logistic regression analysis also revealed that the expected %V̇O2max required to complete a field test could be used to successfully predict performance (χ(2) = 19.3). DISCUSSION: The results of the present investigation highlight the potential utility of V̇O2max, particularly as it relates to the metabolic demands of a surface ambulation, in defining successful completion of planetary-based exploration field tests.

Design projects motivated and informed by the needs of severely disabled autistic children.

Technology can positively impact the lives of severely disabled autistic children if used to (a) gather situational awareness data regarding their health, development, and behavior and (b) assist them with learning and day-to-day activities. This paper summarizes student design projects in the Kansas State University (KSU) College of Engineering that are motivated and informed by the needs of severely disabled children at Heartspring, Wichita, KS. These efforts are supported through the National Science Foundation's General and Age-Related Disabilities Engineering (GARDE) program. Projects relate thematically to (1) facets of a bed sensor system that unobtrusively tracks nighttime health parameters and child activity and (2) miscellaneous resources geared toward paraeducator ("para") and child well-being and development.

Bed-based instrumentation for unobtrusive sleep quality assessment in severely disabled autistic children.

The relationship between sleep quality and daytime wellness and performance in severely disabled, autistic children is not well understood. While polysomnography and, more recently, actigraphy serve as means to obtain sleep assessment data from neurotypical children and adults, these techniques are not well-suited to severely autistic children. This paper presents recent progress on a bed sensor suite that can unobtrusively track physiological and behavioral parameters used to assess sleep quality. Electromechanical films and load cells provide data that yield heart rate, respiration rate, center of position, in-and-out-of-bed activity, and general movement, while thermocouples are used to detect bed-wetting events.

Relationship between brachial artery blood flow and total [hemoglobin+myoglobin] during post-occlusive reactive hyperemia.

The associations between macrovascular and microvascular responses reported previously during post-occlusive reactive hyperemia have been inconsistent. The purpose of this study was therefore to determine the temporal relationship between the reactive hyperemic responses within a conduit artery and the downstream microvessels. Conduit artery blood flow was measured in the brachial artery with pulsed Doppler ultrasound. A potential analog of microvascular flow, changes in skeletal muscle total[hemoglobin+myoglobin] (T[Hb+Mb]), was assessed with near-infrared spectroscopy (NIRS). We found a high degree of correlation between these two measures (r=0.91). Cross-correlation analysis revealed two distinct response patterns. In 10 of our 15 subjects there was time displacement between peak brachial artery blood flow (BABF) and T[Hb+Mb] responses; in the remaining 5 the peaks were coincident. Granger causality testing suggested that reactive hyperemia in the macrovessel determined hyperemia in the downstream microvessels in all 15 study subjects. Time constants for the on (τ1) and off (τ2) kinetics of each response were calculated; our initial hypothesis was that τ1 and τ2 for T[Hb+Mb] would correlate with τ1 and τ2 for BABF, respectively. However, only for τ2 was this observed (r=0.52; p<0.05). No similar relationship was observed for τ1. Adipose tissue thickness did not influence either time constant for T[Hb+Mb]. Taken together, our results show that the temporal characteristics of the hyperemic response in the conduit artery are qualitatively reflected in the downstream microvasculature, but mechanisms for quantitative differences remain to be identified.

Clinical overview of the need for technologies for around-the-clock monitoring of the health status of severely disabled autistic children.

Despite the growing incidence and costs of autism, little has been done to apply technology advancements to the challenges faced by autistic individuals. The recent introduction of digital data collection to track the progress of treatment interventions has been an important contribution, but there are many other opportunities for technology to facilitate the development of autistic children and to assist the clinical staff who work with them. The realization of wearable devices to identify and track behaviors would significantly reduce the manual input of data into digital devices, and wireless physiological monitoring has the potential to provide predictors of unwanted behaviors. The development of such tools would change the intervention paradigms that currently exist.

A paraeducator glove for counting disabled-child behaviors that incorporates a Bluetooth Low Energy wireless link to a smart phone.

Behavior tracking with severely disabled children can be a challenge, since dealing directly with a child's behavior is more immediately pressing than the need to record an event for tracking purposes. By the time a paraeducator (`para') is able to break away and record events, behavior counts can be forgotten. This paper presents a paraeducator glove design that can help to track behaviors with minimal distraction by allowing a paraeducator to touch their thumb to one of their other four fingers, where each finger represents a different behavior. Count data are packaged by a microcontroller board on the glove and then sent wirelessly to a smart phone via a Bluetooth Low Energy (BLE) link. A customized BLE profile was designed for this application to promote real-time recording. These data can be forwarded to a database for further analysis. This para glove design addresses basic needs of a wearable device that employs BLE, including local data collection, BLE data transmission, and remote data recording. More functional sensors can be added to this platform to support other wearable scenarios.

Sensors and instrumentation for unobtrusive sleep quality assessment in autistic children.

Little is understood about the sleep quality of children with autism and other developmental disabilities. Conventional sensors and instrumentation for objective sleep quality assessment, such as those used in polysomnography, are highly obtrusive and not well-suited to this patient population. This paper presents a set of sensors and instrumentation for unobtrusive measurement of physiological and behavioral parameters indicative of sleep quality. Specifically, load cells, an electromechanical film, and thermocouples are used to measure respiratory rate, pulse rate, and physical activity of a subject lying on a bed. The sensor suite is being developed to monitor sleep quality of children at Heartspring, a residential and educational facility in Wichita, KS that serves children with severe developmental disabilities. These technologies have the potential to provide objective sleep quality assessment for children in their home environment.

Real-time processing of electromyograms in an automated hand-forearm ergometer data collection and analysis system.

An automated hand-forearm ergometer with realtime data analysis would be a helpful tool to evaluate muscle fatigue mid-experiment, offering insights into changes in electromyogram parameters that can be used to track fatigue in the hand and forearm musculature. This work presents real-time additions to a custom, automated hand-forearm ergometer that will perform mid-experiment signal processing and help to identify fatigue onset and predict task failure.

GumPack: a personal health assistant with reconfigurable surface components.

Wearable and everyday-carry medical devices can improve quality of life for individuals that need frequent health monitoring. Such tools can supplement ubiquitous home care environments populated with medical sensors, extending the reach of these environments and increasing the freedom of their occupants. This paper presents the concept design for an everyday-carry medical device called a 'GumPack': a small cuboid-shaped device that offers wireless connectivity and plug-and-play surface components, where a component can be a biomedical sensor or a wireless network coordinator that manages a body area network. This geometrical layout optimizes access to surface-based medical hardware mounted on a small form factor. The device offers substantive computing power, supports local component reconfigurability, and promotes interoperability with medical device coordination environments. The GumPack is envisioned to be a personal health assistant carried in a pocket or handbag that can operate alone or interface to, e.g., a cell phone.

Activity recognition in planetary navigation field tests using classification algorithms applied to accelerometer data.

Accelerometer data provide useful information about subject activity in many different application scenarios. For this study, single-accelerometer data were acquired from subjects participating in field tests that mimic tasks that astronauts might encounter in reduced gravity environments. The primary goal of this effort was to apply classification algorithms that could identify these tasks based on features present in their corresponding accelerometer data, where the end goal is to establish methods to unobtrusively gauge subject well-being based on sensors that reside in their local environment. In this initial analysis, six different activities that involve leg movement are classified. The k-Nearest Neighbors (kNN) algorithm was found to be the most effective, with an overall classification success rate of 90.8%.

High resolution wireless body area network with statistically synchronized sensor data for tracking pulse wave velocity.

Wireless body area networks (WBANs) will take on more diverse forms in terms of their sensor combinations and communication protocols as their presence is extended to a greater number of monitoring scenarios. This paper presents an application layer protocol that solves issues caused by sensor nodes that must compete for high speed, real-time communication with the receiver. Such applications emphasize the delivery of large amounts of raw data from different sensor nodes in a time-synchronized manner, rather than channels that experience intermittent operation. An example of tracking pulse wave velocity (PWV) is introduced in this paper, where high-precision PWVs are estimated with the help of timeline recovery and feature extraction processes in MATLAB.

Automated hand-forearm ergometer data collection system.

Handgrip contractions are a standard exercise modality to evaluate cardiovascular system performance. Most conventional ergometer systems of this nature are manually controlled, placing a burden on the researcher to guide subject activity while recording the resultant data. This paper presents updates to a hand-forearm ergometer system that automate the control and data-acquisition processes. A LabVIEW virtual instrument serves as the centerpiece for the system, providing the subject/researcher interfaces as well as coordinating data acquisition from both traditional and new sensors. Initial data indicate the viability of the system with regard to its ability to obtain consistent and physiologically meaningful data.

Wireless slips and falls prediction system.

Accidental slips and falls due to decreased strength and stability are a concern for the elderly. A method to detect and ideally predict these falls can reduce their occurrence and allow these individuals to regain a degree of independence. This paper presents the design and assessment of a wireless, wearable device that continuously samples accelerometer and gyroscope data with a goal to detect and predict falls. Lyapunov-based analyses of these time series data indicate that wearer instability can be detected and predicted in real time, implying the ability to predict impending incidents.