Dynamic Blood Oxygen Saturation Monitoring based on a New IPPG Detecting Device

Li, Jie-Wen; Wei, Min; Li, Yunjie; Yan, Zhengrong; Wang, Yan; Guo, Yuhao; Gan, Zhongxue

doi:10.1145/3460238.3460253

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…The rapid development of sensors, the Internet of Things, and artificial intelligence have made earth-shaking changes in people's lives, and realizes the usage of vital signs and physiological indicators. The ageing of the population makes people pursue better health, and at the same time, the demand for real-time health monitoring and early warning is increasing, which has motivated the emergence of wearable health monitoring and testing equipment, for example, wristbands, blood four index monitors, blood oxygen meters [1], etc. Nursing homes use these wearable health monitoring and testing equipment to monitor and detect individuals' daily exercise, sleep quality, and physiological indicators in real time, such as steps, blood oxygen, blood pressure, heart rate [2], blood sugar, sweat, tears, etc.…”

Section: Introductionmentioning

confidence: 99%

Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing Homes

Zhou

et al. 2023

Electronics

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With the innovation of technologies such as sensors and artificial intelligence, some nursing homes use wearable devices to monitor the movement and physiological indicators of the elderly and provide prompts for any health risks. Nevertheless, this kind of risk warning is a decision based on a particular physiological indicator. Therefore, such decisions cannot effectively predict health risks. To achieve this goal, we propose a model Lidom (A LightGBM-based Disease Prediction Model) based on the combination of the LightGBM algorithm, InterpretML framework, and sequence confrontation network (SeqGAN). The Lidom model first solves the problem of uneven samples based on the sequence confrontation network (SeqGAN), then trains the model based on the LightGBM algorithm, uses the InterpretML framework for analysis, and finally obtains the best model. This paper uses the public dataset MIMIC-III, subject data, and the early diabetes risk prediction dataset in UCI as sample data. The experimental results show that the Lidom model has an accuracy rate of 93.46% for disease risk prediction and an accuracy rate of 99.8% for early diabetes risk prediction. The results show that the Lidom model can provide adequate support for the prediction of the health risks of the elderly.

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Section: Introductionmentioning

confidence: 99%

Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing Homes

Zhou

et al. 2023

Electronics

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“…Although these works have both achieved high performances in the ROI tracking with small head motions, large motions especially ROI occluded have not been considered and tested generally. Moreover, according to early research projects (Kong et al 2020, Luo et al 2021, a fixed region or the whole skin region in the cheeks or the forehead is usually used as the ROI. For the camera's limited field of view and the non-planar surface of the head, the ROI could be out of view or be partly invisible with head motions.…”

Section: Introductionmentioning

confidence: 99%

Anti-motion imaging photoplethysmography via self-adaptive multi-ROI tracking and selection

Duan,

He,

Zhou

2023

Physiol. Meas.

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Objective. The imaging photoplethysmography (IPPG) technique allows people to measure heart rate (HR) from face videos. However, motion artifacts caused by rigid head movements and nonrigid facial muscular movements are one of the key challenges. Approach. This paper proposes a self-adaptive region of interest (ROI) pre-tracking and signal selection method to resist motion artifacts. Based on robust facial landmark detection, we split the whole facial skin (including the forehead, cheeks, and chin) symmetrically into small circular regions. And two symmetric sub-regions constitute a complete ROI. These ROIs are tracked and the motion state is simultaneously assessed to automatically determine the visibility of these ROIs. The obscured or invisible sub-regions will be discarded while the corresponding symmetric sub-regions will be retained as available ROIs to ensure the continuity of the IPPG signal. In addition, based on the frequency spectrum features of IPPG signals extracted from different ROIs, a self-adaptive selection module is constructed to select the optimum IPPG signal for HR calculation. All these operations are updated per frame dynamically for the real-time monitor. Results. Experimental results on the four public databases show that the IPPG signal derived by our proposed method exhibits higher quality for more accurate HR estimation. Compared with the previous method, metrics of the evaluated HR value on our approach demonstrates superior or comparable performance on PURE, VIPL-HR, UBFC-RPPG and MAHNOB-HCI datasets. For instance, the RMSEs on PURE, VIPL-HR, and UBFC-RPPG datasets decrease from 4.29, 7.62, and 3.80 to 4.15, 3.87, and 3.35, respectively. Significance. Our proposed method can help enhance the robustness of IPPG in real applications, especially given motion disturbances.

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Innovative approaches in imaging photoplethysmography for remote blood oxygen monitoring

Zhu,

Liu,

Jing

et al. 2024

Sci Rep

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Peripheral Capillary Oxygen Saturation (SpO 2 ) has received increasing attention during the COVID-19 pandemic. Clinical investigations have demonstrated that individuals afflicted with COVID-19 exhibit notably reduced levels of SpO 2 before the deterioration of their health status. To cost-effectively enable individuals to monitor their SpO 2 , this paper proposes a novel neural network model named “ITSCAN” based on Temporal Shift Module. Benefiting from the widespread use of smartphones, this model can assess an individual’s SpO 2 in real time, utilizing standard facial video footage, with a temporal granularity of seconds. The model is interweaved by two distinct branches: the motion branch, responsible for extracting spatiotemporal data features and the appearance branch, focusing on the correlation between feature channels and the location information of feature map using coordinate attention mechanisms. Accordingly, the SpO 2 estimator generates the corresponding SpO 2 value. This paper summarizes for the first time 5 loss functions commonly used in the SpO 2 estimation model. Subsequently, a novel loss function has been contributed through the examination of various combinations and careful selection of hyperparameters. Comprehensive ablation experiments analyze the independent impact of each module on the overall model performance. Finally, the experimental results based on the public dataset (VIPL-HR) show that our model has obvious advantages in MAE (1.10%) and RMSE (1.19%) compared with related work, which implies more accuracy of the proposed method to contribute to public health.

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Dynamic Blood Oxygen Saturation Monitoring based on a New IPPG Detecting Device

Cited by 4 publications

References 27 publications

Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing Homes

Lidom: A Disease Risk Prediction Model Based on LightGBM Applied to Nursing Homes

Anti-motion imaging photoplethysmography via self-adaptive multi-ROI tracking and selection

Innovative approaches in imaging photoplethysmography for remote blood oxygen monitoring

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