Development of a Wearable Camera and AI Algorithm for Medication Behavior Recognition

Lee, Hwiwon; Youm, Sekyoung

doi:10.3390/s21113594

Cited by 11 publications

(11 citation statements)

References 22 publications

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“…Visual tracking and behavior recognition are applied in automation, medical image, artificial intelligence, and other fields [ 28 ]. This paper mainly studies the classification and behavior understanding of the target in the tracking model.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Athlete Behavior Recognition Technology Based on Siamese‐RPN Tracker Model

Gao

2021

Computational Intelligence and Neuroscience

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With the rapid development of deep learning algorithms, it is gradually applied in UAV (Unmanned Aerial Vehicle) driving, visual recognition, target tracking, behavior recognition, and other fields. In the field of sports, many scientists put forward the research of target tracking and recognition technology based on deep learning algorithms for athletes’ trajectory and behavior capture. Based on the target tracking algorithm, a regional proposal network RPN algorithm combined with the twin regional proposal network Siamese algorithm is proposed to study the tracking and recognition technology of athletes’ behavior. Then, the adaptive updating network is used to track the behavior target of athletes, and the simulation model of behavior recognition is established. This algorithm is different from the traditional twin network algorithm. It can accurately take the athlete’s behavior as the target candidate box in model training and reduce the interference of environment and other factors on model recognition. The results show that the Siamese-RPN algorithm can reduce the interference from the background and environment when tracking the athletes’ target behavior trajectory. This algorithm can improve the training behavior recognition model, ignore the background interference elements of the behavior image, and improve the accuracy and overall performance of the model. Compared with the traditional twin network method for sports behavior recognition, the Siamese-RPN algorithm studied in this paper can perform offline operations and distinguish the interference factors of athletes’ background environment. It can quickly capture the characteristic points of athletes’ behavior as the data input of the tracking model, so it has excellent popularization and application value.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Athlete Behavior Recognition Technology Based on Siamese‐RPN Tracker Model

Gao

2021

Computational Intelligence and Neuroscience

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“…When applying a quadratic function, such as RMSE, it is possible to highlight errors that would be smoothed by the mean value. Having said that, RMSE from heart rate and SpO2 show acceptable results in the average and within each group, however, in respiration rate and further in SBP and DBP it is possible to see the impact of large errors in a quadratic metric [13].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of a camera-based monitoring solution against regulated medical devices to measure: Heart Rate, Respiratory Rate, Oxygen Saturation, and Blood Pressure

Talukdar

Deus

Sehgal

2022

Preprint

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Regular monitoring of common physiological signs, including heart rate, blood pressure, and oxygen saturation, can be an effective way to either prevent or detect several potential conditions. In particular, cardiovascular diseases (CVDs) are a worldwide concern. According to the World Health Organization, 31% of all deaths worldwide are from CVDs. Recently, the COVID-19 pandemic has increased the interest in remote monitoring. At present, contact devices are required to extract most of an individual’ s physiological information, which can be inconvenient for users and may cause discomfort. However, remote photoplethysmography (rPPG) technology offers a solution for this issue, which enables contactless monitoring of the blood volume pulse signal using a regular camera, and ultimately can provide the same physiological information as a contact device. In this paper we propose an evaluation of rPPG technology against medical devices in a clinical environment, with a variety of subjects in a wide range of age, height, weight, and baseline vital signs. Results have shown that rPPG technology was able to meet the initial hypothesis of mean error of +/-3 units for Heart rate, Respiration Rate, and SpO2 estimation, as well as +/-10 mmHg for Systolic and Diastolic Blood Pressure.

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“…When applying a quadratic function, such as RMSE, it is possible to highlight errors that would be smoothed by the mean value. Having said that, RMSE from HR and SpO 2 showed acceptable results in the average and within each group; however, in RR and further in SBP and DBP, it is possible to see the impact of large errors in a quadratic metric [ 13 ].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of a Camera-Based Monitoring Solution Against Regulated Medical Devices to Measure Heart Rate, Respiratory Rate, Oxygen Saturation, and Blood Pressure

Talukdar¹,

Deus²,

Sehgal³

2022

Cureus

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BackgroundRegularly monitoring common physiological signs, including heart rate, blood pressure, and oxygen saturation, can effectively prevent or detect several potential conditions. In particular, cardiovascular diseases (CVDs) are a worldwide concern. According to the World Health Organization, 31% of all deaths worldwide are from CVDs. Recently, the coronavirus disease 2019 pandemic has increased the interest in remote monitoring. At present, contact devices are required to extract most of an individual's physiological information, which can be inconvenient for users and may cause discomfort. MethodologyHowever, remote photoplethysmography (rPPG) technology offers a solution for this issue, enabling contactless monitoring of the blood volume pulse signal using a regular camera. Ultimately, it can provide the same physiological information as a contact device. In this paper, we propose an evaluation of Vastmindz's rPPG technology against medical devices in a clinical environment with a variety of subjects in a wide range of age, height, weight, and baseline vital signs. ResultsThis study confirmed the findings that the contactless technology for the estimation of vitals proposed by Vastmindz was able to estimate heart rate, respiratory rate, and oxygen saturation with a mean error of ±3 units as well as ±10 mmHg for systolic and diastolic blood pressure. ConclusionsReported results have shown that Vastmindz's rPPG technology was able to meet the initial hypothesis and is acceptable for users who want to understand their general health and wellness.

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Development of a Wearable Camera and AI Algorithm for Medication Behavior Recognition

Cited by 11 publications

References 22 publications

[Retracted] Athlete Behavior Recognition Technology Based on Siamese‐RPN Tracker Model

[Retracted] Athlete Behavior Recognition Technology Based on Siamese‐RPN Tracker Model

Evaluation of a camera-based monitoring solution against regulated medical devices to measure: Heart Rate, Respiratory Rate, Oxygen Saturation, and Blood Pressure

Evaluation of a Camera-Based Monitoring Solution Against Regulated Medical Devices to Measure Heart Rate, Respiratory Rate, Oxygen Saturation, and Blood Pressure

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