This successful technology is the digital interventions on depression patients based on (1) heart rate variability bio-feedbacks (HRVBs) measured by a novel flexible sensor patch attached on patient’s wrist(2) an APP. An innovative flexible low-power PPG patch has been designed which is capable of providing weeks of high-quality PPG signals which are essential towards accurate estimation of HRV for favorable efficacy of designed digital interventions. Also, An AI model correlating the HRVsand PHQ-9/HDRS scores of depression patients will be built based on collected data.

Some non-invasive therapy systems via an APPHRV sensors were developed with real-time HRVs measured as feedbacks, demonstrated well the positive effect of HRVB in reducing symptoms. However, these systems suffer severely from wearing discomfort of ECG/PPG sensorslow-quality of measured signals. Based on the above reasons, in this effort, PI’s PPG sensor patch will be used to collect long-time, high-quality PPG data by its high comfort in wearabilityminimized motion artifacts. Furthermore, we developed a real-time HRV calculationdepression degree estimation algorithm.

The developed APP aims at predicting the level of depression based on PHQ-9 (patient health questionnaire)HDRS (Hamilton Depression Rating Scale) with HRV-based AI model. Our APP is the first one which used HRV to predict PHQ-9HDRS. Additionally, the built APP is able to provide on-lineoff-line mode, which is based on doctorAPP intervention treatment. HRVs are extracted from PPG signal measured by flexible sensor patch attached on patient’s wrist. This patch can significantly minimize motion artifactbring great comfort in wearability for long time.

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