Results 71 to 80 of about 22,118 (175)

Printed Wearable Sweat Rate Sensor for Continuous In Situ Perspiration Measurement

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
A wireless wearable sweat rate sensor system is presented, featuring digital 3D direct‐write printing on a flexible substrate with microfluidic layers for continuous, real‐time monitoring. Printed encapsulated metal electrodes are used for capacitance measurements, achieving high sensitivity (0.01 μL min−1) while maintaining a compact and lightweight ...
Mohammad Shafiqul Islam, Sangwon Cha, Md Farhad Hassan, Wenxin Cai, Tahsin Sejat Saniat, Cedar Rose Leach, Yasser Khan   +6 more
wiley   +1 more source

A multimodal EMG and IMU dataset for assessing the quality of exercises designed for spatially constrained environments. [PDF]

Data Brief
Kotolová V, Hejda J, Sokol M, Volf P, Leová L, Kleníková E, Robenhauptová A, Vychová Z, Mautner V, Hsu WC, Lin YJ, Sugiarto T, Rozložník M, Kutílek P.   +13 more
europepmc   +1 more source

Human‐in‐the‐Loop Object Segmentation for 3D Gaussian Splatting via Finger‐based VR Interface

Advanced Intelligent Systems, EarlyView.
This study introduces a human‐in‐the‐loop segmentation framework for 3D Gaussian Splatting that integrates real‐time optimization with intuitive VR‐based finger prompting. Compared with existing automatic, learning‐based methods, it achieves significantly higher accuracy and reduced segmentation time.
Yongseok Lee, Hyunreal Park, Hyunsu Kim, Harim Ji, Dongho Yee, Dongjun Lee   +5 more
wiley   +1 more source

Clinical reasoning in intensive care: Insights from novice and expert physiotherapists. [PDF]

S Afr J Physiother
Kariem H, Ennion L, Karachi F, Gamiet S, Hess D.   +4 more
europepmc   +1 more source

ParamNet: A Physics‐Guided Deep Learning Framework for Intelligent Self‐Inversion of Vacuum Optical Levitation Systems

Advanced Intelligent Systems, EarlyView.
A physics‐guided deep learning framework, ParamNet, is introduced for the intelligent self‐inversion of vacuum optical tweezers. By fuzing dual‐branch time–frequency features with physical dynamical constraints, it achieves high‐accuracy calibration of trap parameters from short‐window, low‐frequency trajectories, outperforming traditional methods ...
Qi Zheng, Wenfeng Fan, Feng Li, Jian Wu, Wei Quan   +4 more
wiley   +1 more source

TrainTracks - federated learning for reproducible research on sensitive medical data. [PDF]

BMC Med Inform Decis Mak
Elwes M, Jaberansary M, Tang FK, Aswendt M, Beyan O, Kutafina E.   +5 more
europepmc   +1 more source

Soft Active Electromyography Interface for Machine Learning‐Enabled Silent Speech Recognition

Advanced Intelligent Systems, EarlyView.
A soft, hand‐worn electromyography interface enables intent‐driven silent speech recognition without continuous facial attachment. The device integrates liquid‐metal interconnects, a transparent flexible circuit, and elastomer encapsulation with a fingertip electrode that contacts perioral muscles only on demand.
Yuta Kurotaki, Shusuke Yamakoshi, Reitaro Yoshida, Yutaka Isoda, Tamami Takano, Yuji Isano, Yusuke Miyake, Kentaro Kuribayashi, Hiroki Ota   +8 more
wiley   +1 more source

Lower limb and feet wound image dataset. [PDF]

Data Brief
Nayeem MD, Rifat MSH, Nisita NJ, Islam MM, Rahman MS.   +4 more
europepmc   +1 more source

Integrating Reinforcement Learning With Explainable Artificial Intelligence for Real‐Time Clinical Decision Support in Dynamic Healthcare Environments

Advanced Intelligent Systems, EarlyView.
A hybrid Reinforcement Learning–Explainable AI framework integrates SHAP and LIME explanations directly into a Deep Q‐Network inference loop for real‐time ICU decision support. Trained on 18 142 mechanically ventilated stays from the eICU database, the system attains 93.0% decision accuracy, 20% fewer errors than RL alone, and a 91% clinician trust ...
Jannatul Ferdaus Disha, MD Faiaz Zaman Dehan, Kazi Md. Tanvir Anzum   +2 more
wiley   +1 more source

Multimodal dataset on glucose interpretation, treatment decisions and smartwatch visualisation for type 1 diabetes. [PDF]

Sci Data
Kongdee R, Parsia B, Thabit H, Harper S.
europepmc   +1 more source