Using the Microsoft Kinect to Model the Environment of an Anthropomimetic Robot

2012 ◽  
Author(s):  
David Devereux ◽  
Bhargav Mitra ◽  
Owen Holland ◽  
Alan Diamond
Keyword(s):  
Microsoft Kinect

Arabic Dynamic Gestures Recognition Using Microsoft Kinect

2018 ◽  
Vol 10 (5) ◽  
pp. 140-159
Author(s):  
B. Hisham ◽  
A. Hamouda
Keyword(s):  
Microsoft Kinect ◽  
Dynamic Gestures

3D Object Recognition and Relative Localization using a 3D sensor Embedded on a Mobile Robot

2020 ◽  
Author(s):  
Gopi Krishna Erabati
Keyword(s):  
Pose Estimation ◽  
Autonomous Systems ◽  
Microsoft Kinect ◽  
Object Localization ◽  
Initial Alignment ◽  
Relative Localization ◽  
3D Data ◽  
Industry Automation ◽  
Ofthe Object ◽  
3D Descriptors

The technology in current research scenario is marching towards automation forhigher productivity with accurate and precise product development. Vision andRobotics are domains which work to create autonomous systems and are the keytechnology in quest for mass productivity. The automation in an industry canbe achieved by detecting interactive objects and estimating the pose to manipulatethem. Therefore the object localization ( i.e., pose) includes position andorientation of object, has profound ?significance. The application of object poseestimation varies from industry automation to entertainment industry and fromhealth care to surveillance. The objective of pose estimation of objects is verysigni?cant in many cases, like in order for the robots to manipulate the objects,for accurate rendering of Augmented Reality (AR) among others.This thesis tries to solve the issue of object pose estimation using 3D dataof scene acquired from 3D sensors (e.g. Kinect, Orbec Astra Pro among others).The 3D data has an advantage of independence from object texture and invarianceto illumination. The proposal is divided into two phases : An o?ine phasewhere the 3D model template of the object ( for estimation of pose) is built usingIterative Closest Point (ICP) algorithm. And an online phase where the pose ofthe object is estimated by aligning the scene to the model using ICP, providedwith an initial alignment using 3D descriptors (like Fast Point Feature Transform(FPFH)).The approach we develop is to be integrated on two di?erent platforms :1)Humanoid robot `Pyrene' which has Orbec Astra Pro 3D sensor for data acquisition,and 2)Unmanned Aerial Vehicle (UAV) which has Intel Realsense Euclidon it. The datasets of objects (like electric drill, brick, a small cylinder, cake box)are acquired using Microsoft Kinect, Orbec Astra Pro and Intel RealSense Euclidsensors to test the performance of this technique. The objects which are used totest this approach are the ones which are used by robot. This technique is testedin two scenarios, fi?rstly, when the object is on the table and secondly when theobject is held in hand by a person. The range of objects from the sensor is 0.6to 1.6m. This technique could handle occlusions of the object by hand (when wehold the object), as ICP can work even if partial object is visible in the scene.

scholarly journals Mining Camera Traces to Estimate Interactions Between Healthcare Workers and Patients

2020 ◽  
Vol 41 (S1) ◽  
pp. s12-s12
Author(s):  
D. M. Hasibul Hasan ◽  
Philip Polgreen ◽  
Alberto Segre ◽  
Jacob Simmering ◽  
Sriram Pemmaraju
Keyword(s):  
Time Of Day ◽  
Microsoft Kinect ◽  
Critical Parameters ◽  
Model Parameters ◽  
Patient Room ◽  
University Of Iowa ◽  
Velocity Change ◽  
Fine Grained ◽  
Visit Frequency ◽  
Contact Patterns

Background: Simulations based on models of healthcare worker (HCW) mobility and contact patterns with patients provide a key tool for understanding spread of healthcare-acquired infections (HAIs). However, simulations suffer from lack of accurate model parameters. This research uses Microsoft Kinect cameras placed in a patient room in the medical intensive care unit (MICU) at the University of Iowa Hospitals and Clinics (UIHC) to obtain reliable distributions of HCW visit length and time spent by HCWs near a patient. These data can inform modeling efforts for understanding HAI spread. Methods: Three Kinect cameras (left, right, and door cameras) were placed in a patient room to track the human body (ie, left/right hands and head) at 30 frames per second. The results reported here are based on 7 randomly selected days from a total of 308 observation days. Each tracked body may have multiple raw segments over the 2 camera regions, which we “stitch” up by matching features (eg, direction, velocity, etc), to obtain complete trajectories. Due to camera noise, in a substantial fraction of the frames bodies display unnatural characteristics including frequent and rapid directional and velocity change. We use unsupervised learning techniques to identify such “ghost” frames and we remove from our analysis bodies that have 20% or more “ghost” frames. Results: The heat map of hand positions (Fig. 1) shows that high-frequency locations are clustered around the bed and more to the patient’s right in accordance with the general medical practice of performing patient exams from their right. HCW visit frequency per hour (mean, 6.952; SD, 2.855) has 2 peaks, 1 during morning shift and 1 during the afternoon shift, with a distinct decrease after midnight. Figure 2 shows visit length (in minutes) distribution (mean, 1.570; SD, 2.679) being dominated by “check in visits” of <30 seconds. HCWs do not spend much time at touching distance from patients during short-length visits, and the fraction of time spent near the patient’s bed seems to increase with visit length up to a point. Conclusions: Using fine-grained data, this research extracts distributions of these critical parameters of HCW–patient interactions: (1) HCW visit length, (2) HCW visit frequency as a function of time of day, and (3) time spent by HCW within touching distance of patient as a function of visit length. To the best of our knowledge, we provide the first reliable estimates of these parameters.Funding: NoneDisclosures: None

scholarly journals Exploring the ‘active mechanisms’ for engaging rural-dwelling older men with dementia in a community technological initiative

2019 ◽  
Vol 40 (9) ◽  
pp. 1906-1938 ◽  
Author(s):  
Ben Hicks ◽  
Anthea Innes ◽  
Samuel R. Nyman
Keyword(s):  
Computer Game ◽  
Older Men ◽  
Community Dwelling ◽  
Microsoft Kinect ◽  
Participatory Action ◽  
Nintendo Wii ◽  
Traditional Health ◽  
People With Dementia ◽  
Health And Social Care ◽  
Field Notes

AbstractResearch has suggested ecopsychosocial initiatives can promote a sense of wellbeing and inclusion in people with dementia. However, few studies have elucidated the ‘active mechanisms’ whereby such initiatives can achieve these outcomes, so hindering their generalisability. This is particularly pertinent when seeking to support community-dwelling older men with dementia who are reluctant to engage with traditional health and social care initiatives. This paper reports on a study that drew from the principles of Participatory Action Research to explore the ‘active mechanisms’ of a technological initiative for older men (65+ years) with dementia in rural England. An individually tailored, male-only initiative, using off-the-shelf computer game technology (e.g. iPad, Nintendo Wii and Microsoft Kinect) was delivered over a nine-week period. Multiple qualitative methods were employed, including: focus groups, open interviews and extensive reflective field notes, to gather data from the perspective of 22 men, 15 care partners and five community volunteers. The data were analysed thematically and interpreted using a masculinity lens. Three mechanisms contributed to the initiative's success: the use of the technology, the male-only environment and the empowering approach adopted. The paper argues that initiatives aimed at community-dwelling older men with dementia would be advised to consider these gendered experiences and ensure participants can maximise their masculine capital when participating in them, by providing enabling activities, non-threatening environments and empowering approaches of delivery.

KArSL

2021 ◽  
Vol 20 (1) ◽  
pp. 1-19
Author(s):  
Ala Addin I. Sidig ◽  
Hamzah Luqman ◽  
Sabri Mahmoud ◽  
Mohamed Mohandes
Keyword(s):  
Deep Learning ◽  
Sign Language ◽  
Markov Models ◽  
Body Language ◽  
Arab Countries ◽  
Microsoft Kinect ◽  
Classification Model ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Arabic Sign Language

Sign language is the major means of communication for the deaf community. It uses body language and gestures such as hand shapes, lib patterns, and facial expressions to convey a message. Sign language is geography-specific, as it differs from one country to another. Arabic Sign language is used in all Arab countries. The availability of a comprehensive benchmarking database for ArSL is one of the challenges of the automatic recognition of Arabic Sign language. This article introduces KArSL database for ArSL, consisting of 502 signs that cover 11 chapters of ArSL dictionary. Signs in KArSL database are performed by three professional signers, and each sign is repeated 50 times by each signer. The database is recorded using state-of-art multi-modal Microsoft Kinect V2. We also propose three approaches for sign language recognition using this database. The proposed systems are Hidden Markov Models, deep learning images’ classification model applied on an image composed of shots of the video of the sign, and attention-based deep learning captioning system. Recognition accuracies of these systems indicate their suitability for such a large number of Arabic signs. The techniques are also tested on a publicly available database. KArSL database will be made freely available for interested researchers.

Recognizing Threat through Object and Gesture Recognition by Applying Convolutional Neural Network and Adaptive Boosting Algorithm Using Microsoft Kinect for Xbox One

2021 ◽  
Author(s):  
Roselito E. Tolentino ◽  
Janielle D. Antiporta ◽  
Carlo G. Coste ◽  
John Carlos H. Garro ◽  
Ariane Grace B. Hernandez ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gesture Recognition ◽  
Microsoft Kinect ◽  
Adaptive Boosting ◽  
Boosting Algorithm
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