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Who is wearing me? TinyDL‐based user recognition in constrained personal devices
IET Computers &Digital Techniques, Volume 16, Issue 1, Page 1-9, January 2022., 2022 Abstract Deep learning (DL) techniques have been extensively studied to improve their precision and scalability in a vast range of applications. Recently, a new milestone has been reached driven by the emergence of the TinyDL paradigm, which enables adaptation of complex DL models generated by well‐known libraries to the restrictions of constrained ...
Ramon Sanchez‐Iborra, Antonio Skarmeta
wiley +1 more source
Tiny Machine Learning for Resource‐Constrained Microcontrollers
Journal of Sensors, Volume 2022, Issue 1, 2022., 2022 We use 250 billion microcontrollers daily in electronic devices that are capable of running machine learning models inside them. Unfortunately, most of these microcontrollers are highly constrained in terms of computational resources, such as memory usage or clock speed.
Riku Immonen +2 more
wiley +1 more source
Tiny Machine Learning Environment: Enabling Intelligence on Constrained Devices [PDF]
, 2023 Running machine learning algorithms (ML) on constrained devices at the extreme edge of the network is problematic due to the computational overhead of ML algorithms, available resources on the embedded platform, and application budget (i.e., real-time ...
Sakr, F
core +5 more sources
Exploring opportunities in TinyML [PDF]
, 2022 Internet of Things (IoT) has acquired useful and powerful advances thanks to the Machine Learning (ML) implementations. But the implementation of Machine Learning in IoT devices with data centers has some serious problems (data privacy, network ...
Rubio Serrano, Juan Diego
core +2 more sources
TinyReptile: TinyML with Federated Meta-Learning [PDF]
, 2023 Tiny machine learning (TinyML) is a rapidly growing field aiming to democratize machine learning (ML) for resource-constrained microcontrollers (MCUs). Given the pervasiveness of these tiny devices, it is inherent to ask whether TinyML applications can ...
Runkler, Thomas A. +2 more
core +1 more source
TyBox: an automatic design and code-generation toolbox for TinyML incremental on-device learning [PDF]
, 2023 Incremental on-device learning is one of the most relevant and interesting challenges in the field of Tiny Machine Learning (TinyML). Indeed, differently from traditional TinyML solutions where the training is typically carried out on the Cloud and ...
Massimo Pavan +7 more
core +1 more source
Gait Stride Length Estimation Using Embedded Machine Learning
Sensors, 2023 Introduction. Spatiotemporal gait parameters, e.g., gait stride length, are measurements that are classically derived from instrumented gait analysis. Today, different solutions are available for gait assessment outside the laboratory, specifically for ...
Joeri R. Verbiest +5 more
doaj +1 more source
Convolutional Neural Network-Based Low-Powered Wearable Smart Device for Gait Abnormality Detection
IoT, 2023 Gait analysis is a powerful technique that detects and identifies foot disorders and walking irregularities, including pronation, supination, and unstable foot movements.
Sanjeev Shakya +2 more
doaj +1 more source
TinyML: Adopting tiny machine learning in smart cities
Journal of Autonomous Intelligence<div><p>Since Tiny machine learning (TinyML) is a quickly evolving subject, it is crucial that internet of things (IoT) devices be able to communicate with one another for the sake of stability and future development. TinyML is a rapidly growing subfield at the intersection of computer science, software engineering, and machine learning ...
Norah N. Alajlan, Dina M. Ibrahim
openaire +1 more source
IEEE AccessCuffless continuous noninvasive blood pressure (cNIBP) monitoring based on photoplethysmography (PPG) has enjoyed great success through a wealth of high-performing machine learning (ML) algorithms.
Nour Faris Ali +3 more
doaj +1 more source