Research & News
From Semiconductor Research to Clinical Validation
Healthrian's wearable technology began with biosignal semiconductor research and has been validated through clinical studies in emergency medicine.
Research Foundation
A Research Track Record Rooted in Semiconductor Design
Healthrian's technology originated in CEO Taehwan Roh's research on wearable biosignal semiconductors. The low-power sensor ICs, neurofeedback SoCs, and body communication technologies published at leading semiconductor conferences and journals such as ISSCC and JSSC became the foundation of today's WEDIC products and CDMO modules.
Biosignal Sensor ICs
Analog front-end and SoC designs that measure ExG signals such as ECG, EEG, and EMG at just a few µW.
Neuroengineering · BCI
A wearable neurofeedback system combining EEG monitoring with transcranial electrical stimulation (tES).
Wearable Platforms
Body area network (BAN) transceivers and patch and headband systems built on fabric-based circuit boards (P-FCB).
Clinical Validation
A randomized controlled trial of a patch-type wireless 12-lead ECG, conducted together with emergency medicine clinicians.
1,080
Google Scholar citations
ISSCC
Presented in 2011 · 2013 · 2014 · 2019
3
Clinical papers on wearable 12-lead ECG
VLSI · SoC
Key research areas, including Biomedical
Sensor Physical AI
From On-Device AI Toward On-Sensor AI
Healthrian is adding AI technology on top of its semiconductor design expertise. Aiming for Sensor Physical AI, which interprets biosignals close to the sensor where they originate, we are moving AI computation from the cloud and the device to the sensor itself.
01 · CLOUD AI
Analysis on the Server
Measured signals are sent to a server for analysis. Computing resources are ample, but this comes with communication latency and the transfer of personal data.
02 · ON-DEVICE AI
Analysis on the Device
Analysis runs inside smartphones and wearable devices. Because data never leaves the device, real-time performance and security improve.
03 · ON-SENSOR AI
Interpretation in the Sensor
Signals are interpreted directly inside the sensor IC, and only the necessary information is sent out. This direction, which reduces both power consumption and data volume, is where Healthrian is headed.
Semiconductor design × biosignal AI — Because we bring together hands-on experience designing low-power sensor ICs and ECG/EEG analysis algorithms, we are preparing for the next step: designing the sensor and the AI as one.
Semiconductors · Systems
Selected Publications
The full list is available on Google Scholar.
IEEE JSSC · 47(1):323–334, 2012
A 75 µW Real-Time Scalable Body Area Network Controller and a 25 µW ExG Sensor IC for Compact Sleep Monitoring Applications
An ultra-low-power ExG sensor IC and body area network (BAN) controller for sleep monitoring
Lee S, Yan L, Roh T, Hong S, Yoo H-J
IEEE CICC · 2012
A 46 µW Motion Artifact Reduction Bio-signal Sensor with ICA Based Adaptive DC Level Control for Sleep Monitoring System
A biosignal sensor that reduces motion artifacts using ICA-based adaptive DC control
Hong S, Lee S, Roh T, Yoo H-J
IEEE ISSCC · 2013
A 5.5 mW IEEE-802.15.6 Wireless Body-Area-Network Standard Transceiver for Multichannel Electro-Acupuncture Application
An IEEE 802.15.6 standard body area network transceiver for multichannel electrical stimulation
Lee H, Lee K, Hong S, Song K, Roh T, Bae J, Yoo H-J
IEEE ISSCC · 2014
A 2.14 mW EEG Neuro-Feedback Processor with Transcranial Electrical Stimulation for Mental-Health Management
A neurofeedback processor for mental health management combining EEG analysis with transcranial electrical stimulation (tES)
Roh T, Song K, Cho H, Shin D, et al., Yoo H-J
IEEE Trans. Biomedical Circuits and Systems · 2014
A Wearable Neuro-Feedback System With EEG-Based Mental Status Monitoring and Transcranial Electrical Stimulation
A wearable EEG monitoring and brain stimulation system based on a low-power neurofeedback SoC
Roh T, Song K, Cho H, Shin D, Yoo H-J
Clinical Research
Clinical papers published in international journals
JMIR mHealth and uHealth · 2021;9(4):e24142
Twelve-Lead Electrocardiogram Acquisition With a Patchy-Type Wireless Device in Ambulance Transport: Simulation-Based Randomized Controlled Trial
Yoon S, Kim T, Roh T, Chang H, Hwang S, Yoon H, Shin T, Sim M, Jo I, Cha W
Interactive Journal of Medical Research · 2022;11(2):e36335
Intervention in the Timeliness of Two Electrocardiography Types for Patients in the Emergency Department With Chest Pain: Randomized Controlled Trial
Yoo S, Chang H, Kim T, Yoon H, Hwang S, Shin T, Sim M, Jo I, Choi J, Cha W
Scientific Reports · 13, 4044 (2023)
Feasibility of patch-type wireless 12-lead electrocardiogram in laypersons
Yoon S, Kim T, Kang E, et al.
News
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We welcome research collaboration inquiries, including clinical studies and the development of new biosignal modules.