Results 71 to 80 of about 793 (174)
A TinyML Wearable System for Real-Time Cardio-Exercise Tracking
Engineering ProceedingsCardiovascular exercise strengthens the heart and improves circulation, but most people struggle to fit regular workouts into their day. Short bursts of vigorous activity, sometimes called exercise snacks, can raise the heart rate and deliver meaningful ...
Timothy Malche
doaj +1 more source
A Wearable System for Real-Time Fall Detection on Resource-Constrained Devices
Future InternetIn this study, we propose a wearable fall detection system that combines wearable sensors, TinyML model, and IoT-based communication for real-time monitoring and detection of falls. The system is designed for resource-constrained IoT devices where memory,
Timothy Malche +6 more
doaj +1 more source
Empowering IoT security: deploying TinyML ensemble techniques for cyberattack detection
Scientific AfricanAs the Internet of Things (IoT) grows and devices connect, protecting IoT networks from vulnerabilities is crucial. Intrusion detection systems (IDS) that use machine learning (ML) techniques are vital for increasing security and preventing unauthorized ...
Abderahmane Hamdouchi, Ali Idri
doaj +1 more source
Efficient human activity recognition on edge devices using DeepConv LSTM architectures
Scientific ReportsDriven by the rapid development of the Internet of Things (IoT), deploying deep learning models on resource-constrained hardware has become an increasingly critical challenge, which has propelled the emergence of TinyML as a viable solution.
Haotian Zhou +4 more
doaj +1 more source
Real-Time RSSI-Based Indoor Localization Using a Two-Stage TinyML Architecture on Edge Devices
IEEE AccessIndoor positioning is a critical component of navigation and control in automated guided vehicles (AGVs) and autonomous mobile robots (AMRs). Wi-Fi RSSI-based localization provides a cost-effective solution for indoor environments; however, its ...
Ahmet Gurkan Yuksek
doaj +1 more source
Discover ElectronicsThe integration of Tiny Machine Learning (TinyML) algorithms into CubeSatInternet of Things (IoT) platforms presents a transformative opportunity for autonomous space-based sensing and decision-making.
Mfonobong Uko +4 more
doaj +1 more source
IEEE Open Journal of the Communications SocietyVehicular Ad Hoc Networks (VANETs) face resource constraints, high node mobility, and stringent latency requirements, especially in safety-critical applications such as collision avoidance, path planning, and emergency braking.
Muhammad Ali +5 more
doaj +1 more source
TinyML Enhances CubeSat Mission Capabilities
2026 ACM/IEEE 17th International Conference on Cyber-Physical Systems (ICCPS)Earth observation (EO) missions traditionally rely on transmitting raw or minimally processed imagery from satellites to ground stations for computationally intensive analysis. This paradigm is infeasible for CubeSat systems due to stringent constraints on the onboard embedded processors, energy availability, and communication bandwidth.
Luigi Capogrosso, Michele Magno
openaire +2 more sources
TinyML keyword spotting demo running under FreeRTOS, showing real-time ML inference on Cortex-M devices.
openaire +1 more source
HybridTrust: on-device federated learning with crypto-agile security for legacy and quantum-safe medical devices. [PDF]
Sci RepKhan UH +5 more
europepmc +1 more source