AIMC Topic: Wrist

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A Deep Automated Skeletal Bone Age Assessment Model with Heterogeneous Features Learning.

Journal of medical systems
Skeletal bone age assessment is a widely used standard procedure in both disease detection and growth prediction for children in endocrinology. Conventional manual assessment methods mainly rely on personal experience in observing X-ray images of lef...

Deep neural network improves fracture detection by clinicians.

Proceedings of the National Academy of Sciences of the United States of America
Suspected fractures are among the most common reasons for patients to visit emergency departments (EDs), and X-ray imaging is the primary diagnostic tool used by clinicians to assess patients for fractures. Missing a fracture in a radiograph often ha...

Sleep stage classification based on multi-level feature learning and recurrent neural networks via wearable device.

Computers in biology and medicine
BACKGROUND: Automatic sleep stage classification is essential for long-term sleep monitoring. Wearable devices show more advantages than polysomnography for home use. In this paper, we propose a novel method for sleep staging using heart rate and wri...

Real-time, simultaneous myoelectric control using a convolutional neural network.

PloS one
The evolution of deep learning techniques has been transformative as they have allowed complex mappings to be trained between control inputs and outputs without the need for feature engineering. In this work, a myoelectric control system based on con...

Classifier Personalization for Activity Recognition Using Wrist Accelerometers.

IEEE journal of biomedical and health informatics
Intersubject variability in accelerometer-based activity recognition may significantly affect classification accuracy, limiting a reliable extension of methods to new users. In this paper, we propose an approach for personalizing classification rules...

Assessing Wrist Movement With Robotic Devices.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Robotic devices have been proposed to meet the rising need for high intensity, long duration, and goal-oriented therapy required to regain motor function after neurological injury. Complementing this application, exoskeletons can augment traditional ...

Wrist sensor-based tremor severity quantification in Parkinson's disease using convolutional neural network.

Computers in biology and medicine
Tremor is a commonly observed symptom in patients of Parkinson's disease (PD), and accurate measurement of tremor severity is essential in prescribing appropriate treatment to relieve its symptoms. We propose a tremor assessment system based on the u...

Monitoring stress with a wrist device using context.

Journal of biomedical informatics
Being able to detect stress as it occurs can greatly contribute to dealing with its negative health and economic consequences. However, detecting stress in real life with an unobtrusive wrist device is a challenging task. The objective of this study ...

Performance comparison between wrist and chest actigraphy in combination with heart rate variability for sleep classification.

Computers in biology and medicine
The concurrent usage of actigraphy and heart rate variability (HRV) for sleep efficiency quantification is still matter of investigation. This study compared chest (CACT) and wrist (WACT) actigraphy (actigraphs positioned on chest and wrist, respecti...

Biomechanical Reactions of Exoskeleton Neurorehabilitation Robots in Spastic Elbows and Wrists.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Spasticity is an important factor in designing wearable and lightweight exoskeleton neurorehabilitation robots. The proposed study evaluates biomechanical reactions of an exoskeleton robot to spasticity and establishes relevant guidelines for robot d...