#703, C's tower, 58, Pangyo-ro 255beon-gil, 

Bundang-gu, Seongnam-si, 13486

Gyeonggi-do, Korea

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Sky Labs 2019 All Rights Reserved 

Ring-type Cardio Tracker,

To help continuous monitoring of Atrial Fibrillation which is difficult to detect

 

technology

CART USES PPG SIGNAL TO DETECT ATRIAL FIBRILLATION

Detect AF by simply wearing CART on your finger It continuously monitors your cardiac signals without any user intervention

Features

2. PPG Sensor / ECG Sensor

4. Wireless Charging Cradle

3. Micro-processor Motion Sensor

1. LED Indicator

data processing & communication

CLINICAL ACCURACY

[Source : Deep Learning Approaches to Detect AtrialFibrillation Using Photoplethysmographic Signals]

HEART RATE MONITORING

CART's heart rate measurement accuracy is over 98% when compared with ECG. This is very close to the accuracy of Gold Standard. And The accuracy of CART is due to the use of a finger ring which is close to the fingertip, and this is known to be the best for measuring heart rate.

ATRIAL FIBRILLATION DETECTION 

CART's atrial fibrillation measurement accuracy is more than 90% based on the clinical data we have so far when compared with the diagnosis result of a cardiologist (Medical doctor). This high detection accuracy is achieved not by heart rate based, but by detecting changes between each heartbeat in the same way as it is used in clinical practice. Our proprietary arrhythmia detection algorithm detects atrial fibrillation and normal sinus rhythm very accurately.

CLINICAL ACCURACY

HEART RATE MONITORING

CART's heart rate measurement accuracy is over 98% when compared with ECG. This is very close to the accuracy of Gold Standard. And The accuracy of CART is due to the use of a finger ring which is close to the fingertip, and this is known to be the best for measuring heart rate.

ATRIAL FIBRILLATION DETECTION 

CART's atrial fibrillation measurement accuracy is more than 90% based on the clinical data we have so far when compared with the diagnosis result of a cardiologist (Medical doctor). This high detection accuracy is achieved not by heart rate based, but by detecting changes between each heartbeat in the same way as it is used in clinical practice. Our proprietary arrhythmia detection algorithm detects atrial fibrillation and normal sinus rhythm very accurately.

[Source :              ]

CLINICAL STUDIES & PAPERS

Deep Learning Approaches to Detect Atrial

Fibrillation Using Photolethysmographic

Signals :

Algorithms Development Study.

June 6th 2019

This study shows that wearable devices have possibility to monitor Atrial Fibrillation by using PPG signals and adopting New Deep Learning classifiers(DL).
proprietary algorithms could detect AF using PPG signals even with frequent PACs and could outperform previously developed AF detectors. Even though diagnostic performance decreased as the burden of PACs increased, performance improved when samples from more patients were trained. Moreover, the reliability of the diagnosis could be indicated by the CL

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CLINICAL ACCURACY

HEART RATE MONITORING

CART's heart rate measurement accuracy is over 98% when compared with ECG. This is very close to the accuracy of Gold Standard. And The accuracy of CART is due to the use of a finger ring which is close to the fingertip, and this is known to be the best for measuring heart rate.

ATRIAL FIBRILLATION DETECTION 

CART's atrial fibrillation measurement accuracy is more than 90% based on the clinical data we have so far when compared with the diagnosis result of a cardiologist (Medical doctor). This high detection accuracy is achieved not by heart rate based, but by detecting changes between each heartbeat in the same way as it is used in clinical practice. Our proprietary arrhythmia detection algorithm detects atrial fibrillation and normal sinus rhythm very accurately.

future line-up

cardiac signals big data platform

INDIVIDUAL HEALTH

Personalized care

and preventive monitoring

PUBLIC HEALTH

Big data analysis

to secure public health

HEALTHCARE PROFESSIONALS

Source of clinical trials

and precise medicine

INSURANCE COMPANIES

Personalized insurance service

development

PHARMACEUTICALS

New product development

and clinical trials