An official website of the United States government
Long-term and high-dose prescription opioid use places individuals at risk for opioid misuse, opioid use disorder (OUD), and overdose. Existing methods for monitoring opioid use and detecting misuse rely on self-reports, which are prone to reporting bias, and toxicology testing, which may be infeasible in outpatient settings. Although wearable technologies for monitoring day-to-day health metrics have gained significant traction in recent years due to their ease of use, flexibility, and advancements in sensor technology, their application within the opioid use space remains underexplored. In the current work, we demonstrate that oral opioid administrations can be detected using physiological signals collected from a wrist sensor. More importantly, we show that models informed by opioid pharmacokinetics increase reliability in predicting the timing of opioid administrations. Forty-two individuals who were prescribed opioids as a part of their medical treatment in-hospital and after discharge were enrolled. Participants wore a wrist sensor throughout the study, while opioid administrations were tracked using electronic medical records and self-reports. We collected 1,983 hours of sensor data containing 187 opioid administrations from the inpatient setting and 927 hours of sensor data containing 40 opioid administrations from the outpatient setting. We demonstrate that a self-supervised pre-trained model, capable of learning the canonical time series of plasma concentration of the drug derived from opioid pharmacokinetics, can reliably detect opioid administration in both settings. Our work suggests the potential of pharmacokinetic-informed, data-driven models to objectively detect opioid use in daily life.
more »
« less
Boosting Lying Posture Classification with Transfer Learning
Automatic lying posture tracking is an important factor in human health monitoring. The increasing popularity of the wrist-based trackers provides the means for unobtrusive, affordable, and long-term monitoring with minimized privacy concerns for the end-users and promising results in detecting the type of physical activity, step counting, and sleep quality assessment. However, there is limited research on development of accurate and efficient lying posture tracking models using wrist-based sensor. Our experiments demonstrate a major drop in the accuracy of the lying posture tracking using wrist-based accelerometer sensor due to the unpredictable noise from arbitrary wrist movements and rotations while sleeping. In this paper, we develop a deep transfer learning method that improves performance of lying posture tracking using noisy data from wrist sensor by transferring the knowledge from an initial setting which contains both clean and noisy data. The proposed solution develops an optimal mapping model from the noisy data to the clean data in the initial setting using LSTM sequence regression, and reconstruct clean synthesized data in another setting where no noisy sensor data is available. This increases the lying posture tracking F1-Score by 24.9% for left-wrist and by 18.1% for right-wrist sensors comparing to the case without mapping.
more »
« less
- Award ID(s):
- 2210133
- PAR ID:
- 10325314
- Date Published:
- Journal Name:
- The 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2022)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Effective Posture Classification using Statistically Significant Data from Flexible Pressure SensorsAdvancements in flexible and printable sensor technologies to overcome the limitations of conventional rigid counterparts offer an excellent opportunity to design various healthcare applications for humans, and their potential flexibility can be used in real-time health monitoring and personalized physical conditions with minimal or no inconvenience. However, managing a large volume of obtained sensor datasets and ensuring accurate predictions can take time and effort. While statistical analysis and the Pearson correlation coefficient can reduce data volume, whether this would lead to losing important information and affect downstream application performance is still being determined. In this paper, we use posture classification as an exemplar of timely services in digital healthcare, especially for bedsores or decubitus ulcers. Our sensors, placed under hospital beds, have a thickness of just 0.4mm and collect pressure data from 28 sensors (7 by 4) at an 8 Hz cycle, categorizing postures into 4 types from 5 patients. We then collected sensor data to explore the possibility of using a small number of pressure sensors for patient posture classification. Next, we apply a statistical analysis to the datasets obtained to select the featured sensor data cells and evaluate the performance of posture classification models on various groups of sensors. Our evaluation involves the analysis of reduced datasets through statistical methods and the Pearson correlation coefficient. The classification performance using datasets comprising 5 featured and 28 sensors are 0.93 and 0.99, respectively. These results suggest comparable performance and the viability of useful classifiers for both cases. Consequently, comparable posture classification performance can be achieved using only 17.9% of the entire dataset.more » « less
-
Opioid use disorder is a medical condition with major social and economic consequences. While ubiquitous physiological sensing technologies have been widely adopted and extensively used to monitor day-to-day activities and deliver targeted interventions to improve human health, the use of these technologies to detect drug use in natural environments has been largely underexplored. The long-term goal of our work is to develop a mobile technology system that can identify high-risk opioid-related events (i.e., development of tolerance in the setting of prescription opioid use, return-to-use events in the setting of opioid use disorder) and deploy just-in-time interventions to mitigate the risk of overdose morbidity and mortality. In the current paper, we take an initial step by asking a crucial question: Can opioid use be detected using physiological signals obtained from a wrist-mounted sensor? Thirty-six individuals who were admitted to the hospital for an acute painful condition and received opioid analgesics as part of their clinical care were enrolled. Subjects wore a noninvasive wrist sensor during this time (1-14 days) that continuously measured physiological signals (heart rate, skin temperature, accelerometry, electrodermal activity, and interbeat interval). We collected a total of 2070 hours (≈ 86 days) of physiological data and observed a total of 339 opioid administrations. Our results are encouraging and show that using a Channel-Temporal Attention TCN (CTA-TCN) model, we can detect an opioid administration in a time-window with an F1-score of 0.80, a specificity of 0.77, sensitivity of 0.80, and an AUC of 0.77. We also predict the exact moment of administration in this time-window with a normalized mean absolute error of 8.6% and R2 coefficient of 0.85.more » « less
-
Abstract Wearable electromechanical sensors are essential to improve health monitoring and off‐site point‐of‐care applications. However, their practicality is restricted by narrow ranges of detection, failure to simultaneously sense static and dynamic pressures, and low durability. Here, an all‐fabric pressure sensor with high sensitivity in a broad range of pressures, from subtle heart pulses to body posture, exceeding that of previously‐reported sensors is introduced. By taking advantage of chemical vapor deposition of p‐doped poly(3,4‐ethylenedioxythiophene) chloride (PEDOT‐Cl) on two natural textiles (cotton gauze and cotton balls), multiscale tunable pressure sensitivity with low power demand for data read‐out is obtained. To protect the sensor against humidity induced degradations, the sensor is encapsulated with a hydrophobic coating that leads to ultrastability of the sensor performance even after 1 week of exposure to 100% relative humidity and 20 laundry cycles. The sensor reveals excellent performance retention of >99% over 70 000 bending cycles under ambient conditions. The varied utility of this sensor for health monitoring is demonstrated by recording heartbeats, respiration, and joint movements. Furthermore, using this sensor, grip strength is successfully detected by 93.6% accuracy as compared to commercial dynamometer, speaking of its potential as the first fabric‐based sensor allowing for personalized real‐time grip strength analysis.more » « less
-
null (Ed.)Smart bracelets able to interpret the wearer's emotional state and communicate it to a remote decision-support facility will have broad applications in healthcare, elder care, the military, and other fields. While there are existing commercial embedded devices, such as the Apple Watch, that have health-monitoring sensors, such devices cannot sufficiently support a real-time health-monitoring system with battery-efficient remote data delivery. Ongoing R&D is developing solutions capable of monitoring multiple psycho-physiological signals. Possible hardware configurations include wrist-worn devices and sensors across an augmented reality headset (e.g., HoloLens 2). The device should carry an array of sensors of psycho-physiological signals, including a galvanic skin response sensor, motion sensor, skin temperature sensor, and a heart rate sensor. Output from these sensors can be intelligently fused to monitor the affective state and to determine specific trigger events for the wearer. To enable real-time remote monitoring applications, the device needs to be low-power to allow persistent monitoring while prolonging usage before recharging. For many applications, specialized sensor arrays are required, e.g. a galvanic skin response sensor. An application-flexible device would allow adding/removing sensors and would provide a choice of communication modules (e.g., Bluetooth 5.0 low-energy vs ZigBee). Appropriate configurations of the device would support applications in military health monitoring, drug-addiction mitigation, autistic trigger monitoring, and augmented reality exploration. A configuration example is: motion sensors (3-axis accelerometers, gyroscopes, and magnetometers to track steps, falls, and energy usage), a heart-rate sensor (e.g., an optical-based heart rate sensor with a single monitoring zone using the process of photoplethysmography (PPS)), at least a Bluetooth 5.0 (but a different communication device may be needed depending on the use case), and flash memory to temporarily store data when the device is not remotely communicating. The wearables field has greatly advanced in the quality of sensors; the fusion of multi-sensor data is the current frontier.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Conference Paper:
- The DOI is not currently available.
