tinyCare: A tinyML-based Low-Cost Continuous Blood Pressure Estimation on the Extreme Edge

Ahmed, K.; Hassan, Mohamed

doi:10.1109/ichi54592.2022.00047

Cited by 7 publications

(3 citation statements)

References 30 publications

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“…One way to see how TinyML can find its place in healthcare is by investigating [97], where researchers apply the edge device to monitor high level blood pressure, called "TinyCare" [97]. The authors developed a cloud independent TinyML solution that merely relies on data obtained from patients.…”

Section: A Blood-pressure Monitoringmentioning

confidence: 99%

“…The authors developed a cloud independent TinyML solution that merely relies on data obtained from patients. The authors in [97] adopt a systematic procedure to tackle the problem, starting by preprocessing the data based on physiological signals and then extracting the features.Their model used a variety of ML algorithms deployed on three Edge Devices: Arduino uno, ESP32 Wrover Board, and AdaFruit PyBadge. [97]'s methodology enabled to test a variety of models, not only with respect to accuracy, but also latency and complexity.…”

Section: A Blood-pressure Monitoringmentioning

confidence: 99%

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A Comprehensive Survey on TinyML

Abadade,

Temouden,

Bamoumen

et al. 2023

IEEE Access

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Recent spectacular progress in computational technologies has led to an unprecedented boom in the field of Artificial Intelligence (AI). AI is now used in a plethora of research areas and has demonstrated its capability to bring new approaches and solutions to various research problems. However, the extensive computation required to train AI algorithms comes with a cost. Driven by the need to reduce the energy consumption, the carbon footprint and the cost of computers running machine learning algorithms, TinyML is nowadays considered as a promising AI alternative focusing on technologies and applications for extremely low-profile devices. This paper presents the results of a literature survey of all TinyML applications and related research efforts. Our survey builds a taxonomy of TinyML techniques that have been used so far to bring new solutions to various domains, such as healthcare, smart farming, environment, and anomaly detection. Finally, this survey highlights the remaining challenges and points out possible future research directions. We anticipate that this survey will motivate further discussions on the various fields of applications of TinyML and the synergy of resource-constrained devices and edge intelligence.

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Section: A Blood-pressure Monitoringmentioning

confidence: 99%

Section: A Blood-pressure Monitoringmentioning

confidence: 99%

A Comprehensive Survey on TinyML

Abadade,

Temouden,

Bamoumen

et al. 2023

IEEE Access

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“…Moreover, it has shown success in the prediction of the BGLs of patients with T1DM with a CGM sensor and a recurrent neural network that builds on long-short term memory (LSTM) [ 61 ]. This tool has proven effectiveness in the broader health domain [ 62 ], facilitating predictions of vital metrics such as blood pressure, cough detection, the pre-screening of oral tongue lesions, and employing a head imaging system for brain stroke detection [ 63 , 64 , 65 , 66 ].…”

Section: Introductionmentioning

confidence: 99%

Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose

Gudiño-Ochoa,

García-Rodríguez,

Ochoa-Ornelas

et al. 2024

Sensors

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Volatile organic compounds (VOCs) in exhaled human breath serve as pivotal biomarkers for disease identification and medical diagnostics. In the context of diabetes mellitus, the noninvasive detection of acetone, a primary biomarker using electronic noses (e-noses), has gained significant attention. However, employing e-noses requires pre-trained algorithms for precise diabetes detection, often requiring a computer with a programming environment to classify newly acquired data. This study focuses on the development of an embedded system integrating Tiny Machine Learning (TinyML) and an e-nose equipped with Metal Oxide Semiconductor (MOS) sensors for real-time diabetes detection. The study encompassed 44 individuals, comprising 22 healthy individuals and 22 diagnosed with various types of diabetes mellitus. Test results highlight the XGBoost Machine Learning algorithm’s achievement of 95% detection accuracy. Additionally, the integration of deep learning algorithms, particularly deep neural networks (DNNs) and one-dimensional convolutional neural network (1D-CNN), yielded a detection efficacy of 94.44%. These outcomes underscore the potency of combining e-noses with TinyML in embedded systems, offering a noninvasive approach for diabetes mellitus detection.

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