3D pose estimation for laparoscopic instruments: A single camera and a planar mirror approach

Visual technologies have an indispensable role in safety-critical applications, where tasks must often be performed through teleoperation. Due to the lack of stereoscopic and motion parallax depth cues in conventional images, alignment tasks pose a significant challenge to remote operation. In this context, machine vision can provide mission-critical information to augment the operator’s perception. In this paper, we propose a retro-reflector marker-based teleoperation aid to be used in hostile remote handling environments. The system computes the remote manipulator’s position with respect to the target using a set of one or two low-resolution cameras attached to its wrist. We develop an end-to-end pipeline of calibration, marker detection, and pose estimation, and extensively study the performance of the overall system. The results demonstrate that we have successfully engineered a retro-reflective marker from materials that can withstand the extreme temperature and radiation levels of the environment. Furthermore, we demonstrate that the proposed maker-based approach provides robust and reliable estimates and significantly outperforms a previous stereo-matching-based approach, even with a single camera.

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Gait analysis for early Parkinson’s disease detection based on deep learning

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2019-0003 ◽

2019 ◽

Vol 5 (1) ◽

pp. 9-12

Author(s):

Jyothsna Kondragunta ◽

Christian Wiede ◽

Gangolf Hirtz

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Deep Learning ◽

Gait Analysis ◽

Pose Estimation ◽

Early Identification ◽

Human Gait ◽

3D Pose Estimation ◽

Wearable Technologies ◽

Advantages And Disadvantages

AbstractBetter handling of neurological or neurodegenerative disorders such as Parkinson’s Disease (PD) is only possible with an early identification of relevant symptoms. Although the entire disease can’t be treated but the effects of the disease can be delayed with proper care and treatment. Due to this fact, early identification of symptoms for the PD plays a key role. Recent studies state that gait abnormalities are clearly evident while performing dual cognitive tasks by people suffering with PD. Researches also proved that the early identification of the abnormal gaits leads to the identification of PD in advance. Novel technologies provide many options for the identification and analysis of human gait. These technologies can be broadly classified as wearable and non-wearable technologies. As PD is more prominent in elderly people, wearable sensors may hinder the natural persons movement and is considered out of scope of this paper. Non-wearable technologies especially Image Processing (IP) approaches captures data of the person’s gait through optic sensors Existing IP approaches which perform gait analysis is restricted with the parameters such as angle of view, background and occlusions due to objects or due to own body movements. Till date there exists no researcher in terms of analyzing gait through 3D pose estimation. As deep leaning has proven efficient in 2D pose estimation, we propose an 3D pose estimation along with proper dataset. This paper outlines the advantages and disadvantages of the state-of-the-art methods in application of gait analysis for early PD identification. Furthermore, the importance of extracting the gait parameters from 3D pose estimation using deep learning is outlined.

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