Hakan Toreyin

Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice

Ubiquitous blood pressure (BP) monitoring is needed to improve hypertension detection and control and is becoming feasible due to recent technological advances such as in wearable sensing. Pulse transit time (PTT) represents a well-known potential approach for ubiquitous BP monitoring. The goal of this review is to facilitate the achievement of reliable ubiquitous BP monitoring via PTT. We explain the conventional BP measurement methods and their limitations; present models to summarize the theory of the PTT-BP relationship; outline the approach while pinpointing the key challenges; overview the previous work toward putting the theory to practice; make suggestions for best practice and future research; and discuss realistic expectations for the approach.

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 Field-Programmable Analog Array Development Platform for Vestibular Prosthesis Signal Processing

We report on a vestibular prosthesis signal processor realized using an experimental field programmable analog array (FPAA). Completing signal processing functions in the analog domain, the processor is designed to help replace a malfunctioning inner ear sensory organ, a semicircular canal. Relying on angular head motion detected by an inertial sensor, the signal processor maps angular velocity into meaningful control signals to drive a current stimulator. To demonstrate biphasic pulse control a 1 k Ω resistive load was placed across an H-bridge circuit. When connected to a 2.4 V supply, a biphasic current of 100 μA was maintained at stimulation frequencies from 50-350 Hz, pulsewidths from 25-400 μ sec, and interphase gaps ranging from 25-250 μsec.

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.

Reconfigurable analog classifier for knee-joint rehabilitation

We present a System-On-Chip Field Programmable Analog Array (FPAA) for analyzing and processing the signals off an accelerometer for a wearable joint health assessment device. FPAAs have been shown to compute with an efficiency of 1000 times, as well as area efficiencies of 100 times, more than digital solutions. This work presents a low power signal processing system which allows us to extract features from the output of the accelerometer. These features are used by the classifier, implemented using a vector matrix multiplication and a two output 1-winner-take-all, to detect flexion and extension cycles in the subject. The compiled design consumes 0.636 μW of power for the front end analog signal processing chain where as the single layer classifier uses 13 μW of power. Thus the system is highly suitable for wearable applications where power consumption is a major concern. The current FPAA is fabricated in a 0.35 μm CMOS process and is operated at a power supply of 2.5 volts. The Gm-C filters and other circuits are operated in the subthreshold regime of the transistor to obtain the highest transconductance to current ratio offered by the process.

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.

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...

A Low-Power ASIC Signal Processor for a Vestibular Prosthesis

A low-power ASIC signal processor for a vestibular prosthesis (VP) is reported. Fabricated with TI 0.35 μm CMOS technology and designed to interface with implanted inertial sensors, the digitally assisted analog signal processor operates extensively in the CMOS subthreshold region. During its operation the ASIC encodes head motion signals captured by the inertial sensors as electrical pulses ultimately targeted for in-vivo stimulation of vestibular nerve fibers. To achieve this, the ASIC implements a coordinate system transformation to correct for misalignment between natural sensors and implanted inertial sensors. It also mimics the frequency response characteristics and frequency encoding mappings of angular and linear head motions observed at the peripheral sense organs, semicircular canals and otolith. Overall the design occupies an area of 6.22 mm 2 and consumes 1.24 mW when supplied with ± 1.6 V.