Sinan Hersek

A Wearable Patch to Enable Long-Term Monitoring of Environmental, Activity and Hemodynamics Variables

We present a low power multi-modal patch designed for measuring activity, altitude (based on high-resolution barometric pressure), a single-lead electrocardiogram, and a tri-axial seismocardiogram (SCG). Enabled by a novel embedded systems design methodology, this patch offers a powerful means of monitoring the physiology for both patients with chronic cardiovascular diseases, and the general population interested in personal health and fitness measures. Specifically, to the best of our knowledge, this patch represents the first demonstration of combined activity, environmental context, and hemodynamics monitoring, all on the same hardware, capable of operating for longer than 48 hours at a time with continuous recording. The three-channels of SCG and one-lead ECG are all sampled at 500 Hz with high signal-to-noise ratio, the pressure sensor is sampled at 10 Hz, and all signals are stored to a microSD card with an average current consumption of less than 2 mA from a 3.7 V coin cell (LIR2450) battery. In addition to electronic characterization, proof-of-concept exercise recovery studies were performed with this patch, suggesting the ability to discriminate between hemodynamic and electrophysiology response to light, moderate, and heavy exercise.

Novel Methods for Sensing Acoustical Emissions From the Knee for Wearable Joint Health Assessment

Objective: We present the framework for wearable joint rehabilitation assessment following musculoskeletal injury. We propose a multimodal sensing (i.e., contact based and airborne measurement of joint acoustic emission) system for at-home monitoring. Methods: We used three types of microphones - electret, MEMS, and piezoelectric film microphones - to obtain joint sounds in healthy collegiate athletes during unloaded flexion/extension, and we evaluated the robustness of each microphones measurements via: 1) signal quality and 2) within-day consistency. Results: First, air microphones acquired higher quality signals than contact microphones (signal-to-noise-and-interference ratio of 11.7 and 12.4 dB for electret and MEMS, respectively, versus 8.4 dB for piezoelectric). Furthermore, air microphones measured similar acoustic signatures on the skin and 5 cm off the skin (~4.5× smaller amplitude). Second, the main acoustic event during repetitive motions occurred at consistent joint angles (intra-class correlation coefficient ICC(1, 1) = 0.94 and ICC(1, k) = 0.99). Additionally, we found that this angular location was similar between right and left legs, with asymmetry observed in only a few individuals. Conclusion: We recommend using air microphones for wearable joint sound sensing; for practical implementation of contact microphones in a wearable device, interface noise must be reduced. Importantly, we show that airborne signals can be measured consistently and that healthy left and right knees often produce a similar pattern in acoustic emissions. Significance: These proposed methods have the potential for enabling knee joint acoustics measurement outside the clinic/lab and permitting long-term monitoring of knee health for patients rehabilitating an acute knee joint injury.

Quantifying the Consistency of Wearable Knee Acoustical Emission Measurements During Complex Motions.

Knee-joint sounds could potentially be used to noninvasively probe the physical and/or physiological changes in the knee associated with rehabilitation following acute injury. In this paper, a system and methods for investigating the consistency of knee-joint sounds during complex motions in silent and loud background settings are presented. The wearable hardware component of the system consists of a microelectromechanical systems microphone and inertial rate sensors interfaced with a field programmable gate array-based real-time processor to capture knee-joint sound and angle information during three types of motion: flexion-extension (FE), sit-to-stand (SS), and walking (W) tasks. The data were post-processed to extract high-frequency and short-duration joint sounds (clicks) with particular waveform signatures. Such clicks were extracted in the presence of three different sources of interference: background, stepping, and rubbing noise. A histogram-vector V→vn was generated from the clicks in a motion-cycle n, where the bin range was 10°. The Euclidean distance between a vector and the arithmetic mean V→av of all vectors in a recording normalized by the V→av is used as a consistency metric dn. Measurements from eight healthy subjects performing FE, SS, and W show that the mean (of mean) consistency metric for all subjects during SS (μ[μ(dn)]= 0.72 in silent, 0.85 in loud) is smaller compared with the FE (μ[μ(dn)]= 1.02 in silent, 0.95 in loud) and W (μ[μ(dn)]= 0.94 in silent, 0.97 in loud) exercises, thereby implying more consistent click-generation during SS compared with the FE and W. Knee-joint sounds from one subject performing FE during five consecutive work-days (μ[μ(dn) = 0.72) and five different times of a day (μ[μ(dn) = 0.73) suggests high consistency of the clicks on different days and throughout a day. This work represents the first time, to the best of our knowledge, that joint sound consistency has been quantified in ambulatory subjects performing every-day activities (e.g., SS, walking). Moreover, it is demonstrated that noise inherent with joint-sound recordings during complex motions in uncontrolled settings does not prevent joint-sound-features from being detected successfully.

Novel approaches to measure acoustic emissions as biomarkers for joint health assessment

The ultimate objective of this research is to quantify changes in joint sounds during recovery from musculoskeletal injury, and to then use the characteristics of such sounds as a biomarker for quantifying joint rehabilitation progress. This paper focuses on the robust measurement of joint acoustic emissions using miniature microphones placed on the knee and interfaced to custom hardware. Two types of microphones were investigated: (1) miniature microphones with a sound port for detecting airborne sounds; and (2) piezoelectric film based contact microphones for detecting skin vibrations associated with internal sounds. Additionally, inertial measurements were taken simultaneously with joint sounds to observe the consistency in the acoustic emissions in the context of particular activities: knee flexion / extension (without load) and multi-joint weighted movement involving knee and hip flexion / extension (i.e. sit-to-stand). The preliminary data demonstrated that high quality joint sound measurements can be obtained with unique and repeatable acoustic signatures in healthy and injured joints. Additionally, the results suggest that combining piezoelectric contact microphones (which detect high quality acoustic emission signals directly from the skin vibrations but can be compromised with loss of skin contact) and electret microphones (which measure lower signal-to-noise ratio airborne sounds from the joint but can even measure such sounds at 5 cm distance from the skin) can provide robust measurements for a future wearable system to assess joint health in patients during rehabilitation at home.

A Robust System for Longitudinal Knee Joint Edema and Blood Flow Assessment Based on Vector Bioimpedance Measurements

We present a robust vector bioimpedance measurement system for longitudinal knee joint health assessment, capable of acquiring high resolution static (slowly varying over the course of hours to days) and dynamic (rapidly varying on the order of milli-seconds) bioresistance and bioreactance signals. Occupying an area of 78×90 mm2 and consuming 0.25 W when supplied with ±5 V, the front-end achieves a dynamic range of 345 Ω and noise floor of 0.018 mΩrms (resistive) and 0.055 mΩrms (reactive) within a bandwidth of 0.1-20 Hz. A microcontroller allows real-time calibration to minimize errors due to environmental variability (e.g., temperature) that can be experienced outside of lab environments, and enables data storage on a micro secure digital card. The acquired signals are then processed using customized physiology-driven algorithms to extract musculoskeletal (edema) and cardiovascular (local blood volume pulse) features from the knee joint. In a feasibility study, we found statistically significant differences between the injured and contralateral static knee impedance measures for two subjects with recent unilateral knee injury compared to seven controls. Specifically, the impedance was lower for the injured knees, supporting the physiological expectations for increased edema and damaged cell membranes. In a second feasibility study, we demonstrate the sensitivity of the dynamic impedance measures with a cold-pressor test, with a 20 mΩ decrease in the pulsatile resistance associated with increased downstream peripheral vascular resistance. The proposed system will serve as a foundation for future efforts aimed at quantifying joint health status continuously during normal daily life.

SoundTrak: Continuous 3D Tracking of a Finger Using Active Acoustics

The small size of wearable devices limits the efficiency and scope of possible user interactions, as inputs are typically constrained to two dimensions: the touchscreen surface. We present SoundTrak, an active acoustic sensing technique that enables a user to interact with wearable devices in the surrounding 3D space by continuously tracking the finger position with high resolution. The user wears a ring with an embedded miniature speaker sending an acoustic signal at a specific frequency (e.g., 11 kHz), which is captured by an array of miniature, inexpensive microphones on the target wearable device. A novel algorithm is designed to localize the finger’s position in 3D space by extracting phase information from the received acoustic signals. We evaluated SoundTrak in a volume of space (20cm × 16cm × 11cm) around a smartwatch, and show an average accuracy of 1.3 cm. We report on results from a Fitts’ Law experiment with 10 participants as the evaluation of the real-time prototype. We also present a set of applications which are supported by this 3D input technique, and show the practical challenges that need to be addressed before widespread use.

A Proof-of-Concept System to Analyze Joint Sounds in Real Time for Knee Health Assessment in Uncontrolled Settings

A proof-of-concept wearable system for measuring, processing, analyzing, and logging activity-contextualized joint sound signatures from the knee joint is presented. Microelectro-mechanical systems (MEMS)-based microphones are used to detect the acoustical emissions from the knee joint, and MEMS accelerometer-gyroscope pairs at the joint are used to calculate joint angle. The joint angle measurement is used as a context for evaluating the resultant acoustical emissions of the knee joint during unloaded flexion-extension cycles. Automated click detection, performed real-time on-board the field-programmable gate array, is demonstrated successfully in both quiet (lab) and simulated loud (coffee shop) environments for proof-of-concept recordings.

Wearable Knee Health System Employing Novel Physiological Biomarkers

Knee injuries and chronic disorders, such as arthritis, affect millions of Americans, leading to missed workdays and reduced quality of life. Currently, after an initial diagnosis, there are few quantitative technologies available to provide sensitive subclinical feedback to patients regarding improvements or setbacks to their knee health status; instead, most assessments are qualitative, relying on patient-reported symptoms, performance during functional tests, and physical examinations. Recent advances have been made with wearable technologies for assessing the health status of the knee (and potentially other joints) with the goal of facilitating personalized rehabilitation of injuries and care for chronic conditions. This review describes our progress in developing wearable sensing technologies that enable quantitative physiological measurements and interpretation of knee health status. Our sensing system enables longitudinal quantitative measurements of knee sounds, swelling, and activity context during clinical and field situations. Importantly, we leverage machine-learning algorithms to fuse the low-level signal and feature data of the measured time series waveforms into higher level metrics of joint health. This paper summarizes the engineering validation, baseline physiological experiments, and human subject studies-both cross-sectional and longitudinal-that demonstrate the efficacy of using such systems for robust knee joint health assessment. We envision our sensor system complementing and advancing present-day practices to reduce joint reinjury risk, to optimize rehabilitation recovery time for a quicker return to activity, and to reduce health care costs.

Bioacoustics-Based Human-Body-Mediated Communication

An acoustics-based method can utilize the human body as a communication channel to propagate information across different devices. The proposed system can propagate acoustic signals under 20 kHz within or between human bodies and even between the human body and the environment. The web extra at https://youtu.be/6Vo3gm5oJnM illustrates human-body-mediated communication concepts discussed in the article.

A stethoscope for the knee: Investigating joint acoustical emissions as novel biomarkers for wearable joint health assessment

Each year, millions of Americans endure knee injuries, ranging from simple sprains to ligament tears requiring surgical intervention. Our team is investigating wearable rehabilitation assessment technologies for patients recovering from knee injuries based on the measurement and analysis of the acoustical emissions from the knees. Using miniature electret microphones combined with piezoelectric sensors placed on the surface of the skin at the knee, we measure the sounds from the joint as subjects perform basic flexion/extension exercises and standardized sit-to-stand protocols. We then analyze the consistency of the knee acoustical emissions in the context of the activity, and the angle of the joint, to quantify the health of the joint. We have found, in early pilot studies, promising results differentiating the healthy versus injured knee, and longitudinal changes progressing from acute injury and recovery following rehabilitation. We have also determined that, in healthy subjects, the pattern of acousti...