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Motion

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Video-Based Human Activity Recognition Using Deep Learning Approaches.

Sensors (Basel, Switzerland)
Due to its capacity to gather vast, high-level data about human activity from wearable or stationary sensors, human activity recognition substantially impacts people's day-to-day lives. Multiple people and things may be seen acting in the video, disp...

A patient-specific deep learning framework for 3D motion estimation and volumetric imaging during lung cancer radiotherapy.

Physics in medicine and biology
. Respiration introduces a constant source of irregular motion that poses a significant challenge for the precise irradiation of thoracic and abdominal cancers. Current real-time motion management strategies require dedicated systems that are not ava...

Eye-Gaze Controlled Wheelchair Based on Deep Learning.

Sensors (Basel, Switzerland)
In this paper, we design a technologically intelligent wheelchair with eye-movement control for patients with ALS in a natural environment. The system consists of an electric wheelchair, a vision system, a two-dimensional robotic arm, and a main cont...

Artificial Intelligence Distinguishes Pathological Gait: The Analysis of Markerless Motion Capture Gait Data Acquired by an iOS Application (TDPT-GT).

Sensors (Basel, Switzerland)
Distinguishing pathological gait is challenging in neurology because of the difficulty of capturing total body movement and its analysis. We aimed to obtain a convenient recording with an iPhone and establish an algorithm based on deep learning. From...

An End-to-End Dynamic Posture Perception Method for Soft Actuators Based on Distributed Thin Flexible Porous Piezoresistive Sensors.

Sensors (Basel, Switzerland)
This paper proposes a method for accurate 3D posture sensing of the soft actuators, which could be applied to the closed-loop control of soft robots. To achieve this, the method employs an array of miniaturized sponge resistive materials along the so...

PMotion: an advanced markerless pose estimation approach based on novel deep learning framework used to reveal neurobehavior.

Journal of neural engineering
The evaluation of animals' motion behavior has played a vital role in neuromuscular biomedical research and clinical diagnostics, which reflects the changes caused by neuromodulation or neurodamage. Currently, the existing animal pose estimation meth...

SequenceMorph: A Unified Unsupervised Learning Framework for Motion Tracking on Cardiac Image Sequences.

IEEE transactions on pattern analysis and machine intelligence
Modern medical imaging techniques, such as ultrasound (US) and cardiac magnetic resonance (MR) imaging, have enabled the evaluation of myocardial deformation directly from an image sequence. While many traditional cardiac motion tracking methods have...

Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection From Point Clouds.

IEEE transactions on pattern analysis and machine intelligence
Previous works for LiDAR-based 3D object detection mainly focus on the single-frame paradigm. In this paper, we propose to detect 3D objects by exploiting temporal information in multiple frames, i.e., point cloud videos. We empirically categorize th...

Trocar localisation for robot-assisted vitreoretinal surgery.

International journal of computer assisted radiology and surgery
PURPOSE: Robot-assisted vitreoretinal surgery provides precise and consistent operations on the back of the eye. To perform this safely, knowledge of the surgical instrument's remote centre of motion (RCM) and the location of the insertion point into...

Stochastic momentum methods for non-convex learning without bounded assumptions.

Neural networks : the official journal of the International Neural Network Society
Stochastic momentum methods are widely used to solve stochastic optimization problems in machine learning. However, most of the existing theoretical analyses rely on either bounded assumptions or strong stepsize conditions. In this paper, we focus on...