Wearable Knee Health Rehabilitation Assessment using Acoustical Emissions

Each year, approximately 200,000 Americans endure anterior cruciate ligament (ACL) tears, and 100,000 reconstructive procedures are conducted to repair the injured knees (1). The injury itself, and the long rehabilitation process that follows, can majorly disrupt the quality of life for these Americans through missed workdays, reduction of overall physical activity, and increased risk of re-injury in future activities. Wearable technologies for quantifying the state of rehabilitation, and providing feedback to the user regarding which activities or intensities of activities are safe to perform at any given time, could potentially help accelerate the rehabilitation process as well as reduce the risk of re-injury. Our lab has developed a novel, wearable sensing system based on miniature piezoelectric contact microphones for measuring the acoustical emissions from the knee during movements such as unloaded flexion / extension, sit-to-stand, and walking activities. The system consists of two Knowles BU-23173 contact microphones (Knowles, Itasca, IL) positioned on the medial and lateral sides of the patella, connected to custom, analog pre-amplifier circuits and a microcontroller for digitization and data storage on a secure digital (SD) card. In addition to the acoustical sensing, the system includes two integrated inertial measurement sensors including accelerometer and gyroscope modalities to enable joint angle calculations; these sensors, with digital outputs, are connected directly to the same microcontroller via serial peripheral interface (SPI). The system provides low noise, accurate joint acoustical emission and angle measurements in a wearable form factor, and has several hours of battery life. We have also taken measurements from healthy subjects, and athletes following acute ACL tear, to determine initial features from these acoustical emissions that are associated with injured versus healthy joints. We have found that the main acoustic clicks during particular motions occurred at consistent joint angles for healthy subjects based on intraclass correlation coefficient analysis (ICC(1,1) = 0.94 and ICC(1,k) = 0.99) (2). For one subject with an ACL tear, we found that the consistency of the joint acoustical emissions was lower for the injured knee as compared to the healthy knee in the recording immediately following the injury (\u3c 7 days), and improved following six months of rehabilitation. We envision using the wearable system we have recently completed to conduct further experiments with subjects following acute ACL tears, and tracking the progress of the rehabilitation while simultaneously measuring acoustical emissions in the context of particular movements. This data will then serve as a foundation for creating subject-specific algorithms for assessing rehabilitation and providing feedback to the users

Toward Continuous, Noninvasive Assessment of Ventricular Function and Hemodynamics: Wearable Ballistocardiography

Ballistocardiography, the measurement of the reaction forces of the body to cardiac ejection of blood, is one of the few techniques available for unobtrusively assessing the mechanical aspects of cardiovascular health outside clinical settings. Recently, multiple experimental studies involving healthy subjects and subjects with various cardiovascular diseases have demonstrated that the ballistocardiogram (BCG) signal can be used to trend cardiac output, contractility, and beat-by-beat ventricular function for arrhythmias. The majority of these studies has been performed with "fixed" BCG instrumentation-such as weighing scales or chairs-rather than wearable measurements. Enabling wearable, and thus continuous, recording of BCG signals would greatly expand the capabilities of the technique; however, BCG signals measured using wearable devices are morphologically dissimilar to measurements from "fixed" instruments, precluding the analysis and interpretation techniques from one domain to be applied to the other. In particular, the time intervals between the electrocardiogram (ECG) and BCG-namely, the R-J interval, a surrogate for measuring contractility changes-are significantly different for the accelerometer compared to a "fixed" BCG measurement. This paper addresses this need for quantitatively normalizing wearable BCG measurement to "fixed" measurements with a systematic experimental approach. With these methods, the same analysis and interpretation techniques developed over the past decade for "fixed" BCG measurement can be successfully translated to wearable measurements

Comparison of Different Methods for Estimating Cardiac Timings: A Comprehensive Multimodal Echocardiography Investigation

Cardiac time intervals are important hemodynamic indices and provide information about left ventricular performance. Phonocardiography (PCG), impedance cardiography (ICG), and recently, seismocardiography (SCG) have been unobtrusive methods of choice for detection of cardiac time intervals and have potentials to be integrated into wearable devices. The main purpose of this study was to investigate the accuracy and precision of beat-to-beat extraction of cardiac timings from the PCG, ICG and SCG recordings in comparison to multimodal echocardiography (Doppler, TDI, and M-mode) as the gold clinical standard. Recordings were obtained from 86 healthy adults and in total 2,120 cardiac cycles were analyzed. For estimation of the pre-ejection period (PEP), 43% of ICG annotations fell in the corresponding echocardiography ranges while this was 86% for SCG. For estimation of the total systolic time (TST), these numbers were 43, 80, and 90% for ICG, PCG, and SCG, respectively. In summary, SCG and PCG signals provided an acceptable accuracy and precision in estimating cardiac timings, as compared to ICG

Non-Invasive Physiological Sensing and Modulation for Human Health and Performance

Presented on November 13, 2018 at 12:00 p.m.-1:00 p.m. in the Marcus Nanotechnology Building, Room 1117-1118, Georgia Tech.Omer Inan is an Associate Professor of Electrical and Computer Engineering and Adjunct Associate Professor of Biomedical Engineering at Georgia Tech. He received his BS, MS, and PhD in Electrical Engineering from Stanford in 2004, 2005, and 2009, respectively. From 2009-2013, he was the Chief Engineer at Countryman Associates, Inc., a professional audio manufacturer of miniature microphones and high-end audio products for Broadway theaters, theme parks, and broadcast networks. He has received several major awards for his research including the NSF CAREER award, the ONR Young Investigator award, and the IEEE Sensors Council Early Career award. While at Stanford as an undergraduate, he was the school record holder and a three-time NCAA All-American in the discus throw.Runtime: 49:48 minutesThe Precision Medicine Initiative challenges biomedical researchers to reframe health optimization and disease treatment in a patientspecific, personalized manner. Rather than a one-size-fits-all paradigm, the charge is for a particular profile to be fit to each patient, and for disease treatment (or wellness) strategies to then be tailored accordingly. Non-invasive physiological sensing and modulation can play an important role in this effort by augmenting existing research in omics and medical imaging towards better developing such personalized models for patients, and in continuously adjusting such models to optimize therapies in real-time to meet patients’ changing needs. While in many instances the focus of such efforts is on disease treatment, optimizing performance for healthy individuals is also a compelling need. This talk will focus on my group’s research on non-invasive sensing of the sounds and vibrations of the body, with application to musculoskeletal and cardiovascular monitoring applications. In the first half of the talk, I will discuss our studies that are elucidating mechanisms behind the sounds of the knees, and particularly the characteristics of such sounds that change with acute injuries. We use miniature microelectromechanical systems (MEMS) air-based and piezoelectric contact microphones to capture joint sounds emitted during movement, then apply data analytics techniques to both visualize and quantify differences between healthy and injured knees. In the second half of the talk, I will describe our work studying the vibrations of the body in response to the heartbeat using modified weighing scales and wearable MEMS accelerometers. Our group has extensively studied the timings of such vibrations in relation to the electrophysiology of the heart, and how such timings change for patients with cardiovascular diseases during treatment. Ultimately, we envision that these technologies can enable personalized titration of care and optimization of performance to reduce injuries and rehabilitation time for athletes and soldiers, improve the quality of life for patients with heart disease, and reduce overall healthcare costs

Towards Continuous and Ambulatory Blood Pressure Monitoring: Methods for Efficient Data Acquisition for Pulse Transit Time Estimation

We developed a prototype for measuring physiological data for pulse transit time (PTT) estimation that will be used for ambulatory blood pressure (BP) monitoring. The device is comprised of an embedded system with multimodal sensors that streams high-throughput data to a custom Android application. The primary focus of this paper is on the hardware–software codesign that we developed to address the challenges associated with reliably recording data over Bluetooth on a resource-constrained platform. In particular, we developed a lossless compression algorithm that is based on optimally selective Huffman coding and Huffman prefixed coding, which yields virtually identical compression ratios to the standard algorithm, but with a 67–99% reduction in the size of the compression tables. In addition, we developed a hybrid software–hardware flow control method to eliminate microcontroller (MCU) interrupt-latency related data loss when multi-byte packets are sent from the phone to the embedded system via a Bluetooth module at baud rates exceeding 115,200 bit/s. The empirical error rate obtained with the proposed method with the baud rate set to 460,800 bit/s was identically equal to 0%. Our robust and computationally efficient physiological data acquisition system will enable field experiments that will drive the development of novel algorithms for PTT-based continuous BP monitoring