Any experiances with Ringconn Gen 2?
Anyone have a Ringconn Gen2 and care to speak about its efficacy re: sleep apnea?
It'd be esp. good if you have a traditional device to compare its results with.
As you may know they use AI/Deep Learning to do detection with non-continuous monitoring. They have at least 1 paper on this.
The first paper, "A Transformer-Based Deep Learning Model for Sleep Apnea Detection and Application on RingConn Smart Ring," explores a new method for detecting Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) using the RingConn Smart Ring. OSAHS is a common sleep disorder characterized by severe snoring, repeated breathing pauses during sleep, excessive daytime sleepiness, morning headaches, and memory loss. Severe cases can also lead to nighttime asphyxia and cardiovascular problems.
Traditional OSAHS detection requires an overnight examination with bulky polysomnography (PSG) equipment in hospitals. This paper proposes a deep learning model based on Transformer, utilizing the multimodal physiological signals of the RingConn Smart Ring to detect OSAHS.
The model achieved excellent detection results on public datasets with an F1 score of 76.6. The study also evaluated the performance of signal combinations in detecting oxygen desaturation events (DESAT) with the RingConn Smart Ring. Notably, no current research validates OSAHS monitoring based on a smart ring. Thus, the study used data collected from the RingConn Smart Ring for verification and performed PSG-AHI estimation.
Results showed a high correlation (ρ=0.96) between the AHI calculated by the RingConn Smart Ring and PSG-AHI, demonstrating the high accuracy and convenience of the RingConn Smart Ring in medical health monitoring.
It'd be esp. good if you have a traditional device to compare its results with.
As you may know they use AI/Deep Learning to do detection with non-continuous monitoring. They have at least 1 paper on this.
The first paper, "A Transformer-Based Deep Learning Model for Sleep Apnea Detection and Application on RingConn Smart Ring," explores a new method for detecting Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) using the RingConn Smart Ring. OSAHS is a common sleep disorder characterized by severe snoring, repeated breathing pauses during sleep, excessive daytime sleepiness, morning headaches, and memory loss. Severe cases can also lead to nighttime asphyxia and cardiovascular problems.
Traditional OSAHS detection requires an overnight examination with bulky polysomnography (PSG) equipment in hospitals. This paper proposes a deep learning model based on Transformer, utilizing the multimodal physiological signals of the RingConn Smart Ring to detect OSAHS.
The model achieved excellent detection results on public datasets with an F1 score of 76.6. The study also evaluated the performance of signal combinations in detecting oxygen desaturation events (DESAT) with the RingConn Smart Ring. Notably, no current research validates OSAHS monitoring based on a smart ring. Thus, the study used data collected from the RingConn Smart Ring for verification and performed PSG-AHI estimation.
Results showed a high correlation (ρ=0.96) between the AHI calculated by the RingConn Smart Ring and PSG-AHI, demonstrating the high accuracy and convenience of the RingConn Smart Ring in medical health monitoring.
