Search research articles

Related Concept Videos

Holter Monitor: 24-Hour Monitoring

Holter Monitor: 24-Hour Monitoring

Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...

Pulse rhythm

Pulse rhythm

Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Automated Aneurysm Boundary Detection and Volume Estimation Using Deep Learning.

Diagnostics (Basel, Switzerland)·2025

Same author

Availability and Use of Digital Technology Among Women With Polycystic Ovary Syndrome: Scoping Review.

JMIR infodemiology·2025

Same author

Monitoring Opioid-Use-Disorder Treatment Adherence Using Smartwatch Gesture Recognition.

Sensors (Basel, Switzerland)·2025

Same author

'I Don't Know How to Live Real Life Sober'-Identifying Needed Supports Through the Voices of Pregnant and Parenting People Seeking Recovery.

International journal of mental health nursing·2024

Same author

Toward Concurrent Identification of Human Activities with a Single Unifying Neural Network Classification: First Step.

Sensors (Basel, Switzerland)·2024

Same author

Integrating Omics Data and AI for Cancer Diagnosis and Prognosis.

Cancers·2024

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jul 31, 2025

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

Published on: February 21, 2025

Detecting Medication-Taking Gestures Using Machine Learning and Accelerometer Data Collected via Smartwatch

Chrisogonas Odero Odhiambo¹, Lukacs Ablonczy², Pamela J Wright³

¹Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, United States.

JMIR Human Factors

|May 4, 2023

Summary

Smartwatch sensors can accurately detect medication-taking gestures using an artificial neural network (ANN). This technology offers a nonintrusive method to monitor medication adherence, addressing a key public health challenge.

Keywords:

automated pattern recognition digital biomarkers digital signal processing ecological momentary assessment machine learning medication adherence neural networks

More Related Videos

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Published on: December 11, 2019

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

Published on: February 3, 2022

Related Experiment Videos

Last Updated: Jul 31, 2025

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

Published on: February 21, 2025

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Published on: December 11, 2019

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

Published on: February 3, 2022

Area of Science:

Biomedical Engineering
Human-Computer Interaction
Digital Health

Background:

Medication adherence is a significant global public health issue, with approximately 50% of patients not following their prescribed regimens.
While medication reminders exist, objectively verifying if medication was taken remains a challenge.
Smartwatch technology presents a novel opportunity for unobtrusive, automatic detection of medication-taking events.

Purpose of the Study:

To investigate the feasibility of using smartwatches to detect natural medication-taking gestures.
To develop and evaluate a machine learning model for identifying medication intake from sensor data.

Main Methods:

Recruited 28 participants who recorded scripted and natural medication-taking events over 5 days.
Collected accelerometer data at 25 Hz from smartwatches during these events.
Trained an artificial neural network (ANN) using validated medication-taking data, alongside data from other activities like eating and smoking.

Main Results:

The trained ANN achieved high performance, with average true-positive and true-negative rates of 96.5% and 94.5%, respectively.
The model demonstrated a classification error of less than 5% for medication-taking gestures.
The study successfully validated smartwatch data against self-reports for accuracy.

Conclusions:

Smartwatch technology shows potential for accurately and nonintrusively monitoring medication-taking behaviors.
Further research is needed to explore the use of sensing devices and machine learning for improving medication adherence.