A Review of Machine Learning and TinyML in Healthcare

Tsoukas, Vasileios; Boumpa, Eleni; Γιάννακας, Γεώργιος; Kakarountas, Athanasios

doi:10.1145/3503823.3503836

Cited by 33 publications

(13 citation statements)

References 23 publications

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“…In addition, Ericsson, one of the biggest network companies, predicted that TinyML plays a platform role, such as TinyML-as-a-Service [25]. According to the benefits of TinyML in terms of energy efficiency, low cost, data security, and latency, there have been several efforts to apply TinyML to various services and applications [26][27][28][29][30][31]. Raza et al [26] leveraged TinyML to provide intelligence to unmanned aerial vehicles (UAVs) with advanced decisionmaking capabilities.…”

Section: Related Work 21 Tiny Machine Learningmentioning

confidence: 99%

“…Although the dynamic environments where the current distribution is different from that learned from the training are challenging in autonomous driving, they show that TinyML can improve the robustness and the energy efficiency with suitable decisions. As several review works predicted [31], one of the main candidate applications of TinyML will be wearable medical and healthcare devices. However, since integrating TinyML into medical and healthcare devices is an initial stage, there are very few works on it [31].…”

Section: Related Work 21 Tiny Machine Learningmentioning

confidence: 99%

“…As several review works predicted [31], one of the main candidate applications of TinyML will be wearable medical and healthcare devices. However, since integrating TinyML into medical and healthcare devices is an initial stage, there are very few works on it [31]. Furthermore, no previous work considered the TinyML-based ECG monitoring system.…”

Section: Related Work 21 Tiny Machine Learningmentioning

confidence: 99%

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TinyML-Based Classification in an ECG Monitoring Embedded System

Kim¹,

Kim²,

Park³

et al. 2023

Computers, Materials &Amp; Continua

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Recently, the development of the Internet of Things (IoT) has enabled continuous and personal electrocardiogram (ECG) monitoring. In the ECG monitoring system, classification plays an important role because it can select useful data (i.e., reduce the size of the dataset) and identify abnormal data that can be used to detect the clinical diagnosis and guide further treatment. Since the classification requires computing capability, the ECG data are usually delivered to the gateway or the server where the classification is performed based on its computing resource. However, real-time ECG data transmission continuously consumes battery and network resources, which are expensive and limited. To mitigate this problem, this paper proposes a tiny machine learning (TinyML)-based classification (i.e., TinyCES), where the ECG monitoring device performs the classification by itself based on the machine-learning model, which can reduce the memory and the network resource usages for the classification. To demonstrate the feasibility, after we configure the convolutional neural networks (CNN)-based model using ECG data from the Massachusetts Institute of Technology (MIT)-Beth Israel Hospital (BIH) arrhythmia and the Physikalisch Technische Bundesanstalt (PTB) diagnostic ECG databases, TinyCES is validated using the TinyMLsupported Arduino prototype. The performance results show that TinyCES can have an approximately 97% detection ratio, which means that it has great potential to be a lightweight and resource-efficient ECG monitoring system.

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Section: Related Work 21 Tiny Machine Learningmentioning

confidence: 99%

Section: Related Work 21 Tiny Machine Learningmentioning

confidence: 99%

Section: Related Work 21 Tiny Machine Learningmentioning

confidence: 99%

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TinyML-Based Classification in an ECG Monitoring Embedded System

Kim¹,

Kim²,

Park³

et al. 2023

Computers, Materials &Amp; Continua

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“…Medical devices have become part of our daily lives, especially in the wellness field [1,2]. In the Internet of Things (IoT) era, systems of wireless, interconnected and networked digital devices can now continuously collect, analyze, send and store data over a network, making it tremendously easier to monitor a patient in real time in their living environment [3].…”

Section: Introductionmentioning

confidence: 99%

Heart Rate Measurement Using the Built-In Triaxial Accelerometer from a Commercial Digital Writing Device

Payette,

Vaussenat,

Cloutier

2024

Sensors

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Currently, wearable technology is an emerging trend that offers remarkable access to our data through smart devices like smartphones, watches, fitness trackers and textiles. As such, wearable devices can enable health monitoring without disrupting our daily routines. In clinical settings, electrocardiograms (ECGs) and photoplethysmographies (PPGs) are used to monitor heart and respiratory behaviors. In more practical settings, accelerometers can be used to estimate the heart rate when they are attached to the chest. They can also help filter out some noise in ECG signals from movement. In this work, we compare the heart rate data extracted from the built-in accelerometer of a commercial smart pen equipped with sensors (STABILO’s DigiPen) to standard ECG monitor readouts. We demonstrate that it is possible to accurately predict the heart rate from the smart pencil. The data collection is carried out with eight volunteers writing the alphabet continuously for five minutes. The signal is processed with a Butterworth filter to cut off noise. We achieve a mean-squared error (MSE) better than 6.685 × 10−3 comparing the DigiPen’s computed Δt (time between pulses) with the reference ECG data. The peaks’ timestamps for both signals all maintain a correlation higher than 0.99. All computed heart rates (HR =60Δt) from the pen accurately correlate with the reference ECG signals.

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“…Although there are numerous applications of image segmentation, such as satellite image segmentation [1,2] or flood segmentation [3], one of the most important lies in the medical field [4][5][6]. Medical image segmentation refers to the process of dividing a medical image into different sections or regions containing similar medical features or structures.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Architectures for Diagnosis of Diabetic Retinopathy

et al. 2023

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For many years, convolutional neural networks dominated the field of computer vision, not least in the medical field, where problems such as image segmentation were addressed by such networks as the U-Net. The arrival of self-attention-based networks to the field of computer vision through ViTs seems to have changed the trend of using standard convolutions. Throughout this work, we apply different architectures such as U-Net, ViTs and ConvMixer, to compare their performance on a medical semantic segmentation problem. All the models have been trained from scratch on the DRIVE dataset and evaluated on their private counterparts to assess which of the models performed better in the segmentation problem. Our major contribution is showing that the best-performing model (ConvMixer) is the one that shares the approach from the ViT (processing images as patches) while maintaining the foundational blocks (convolutions) from the U-Net. This mixture does not only produce better results (DICE=0.83) than both ViTs (0.80/0.077 for UNETR/SWIN-Unet) and the U-Net (0.82) on their own but reduces considerably the number of parameters (2.97M against 104M/27M and 31M, respectively), showing that there is no need to systematically use large models for solving image problems where smaller architectures with the optimal pieces can get better results.

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A Review of Machine Learning and TinyML in Healthcare

Cited by 33 publications

References 23 publications

TinyML-Based Classification in an ECG Monitoring Embedded System

TinyML-Based Classification in an ECG Monitoring Embedded System

Heart Rate Measurement Using the Built-In Triaxial Accelerometer from a Commercial Digital Writing Device

Deep Learning Architectures for Diagnosis of Diabetic Retinopathy

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