scholarly journals Development of a Kinect Rehabilitation System

2013 ◽  
Vol 9 (S8) ◽  
pp. 38 ◽  
Author(s):  
João Couto Soares ◽  
Ágata Vieira ◽  
Octavian Postolache ◽  
Joaquim Gabriel
Keyword(s):  
Real Time ◽  
Serious Games ◽  
Microsoft Kinect ◽  
Visual Environment ◽  
Kinect Sensor ◽  
Game Engine ◽  
Kinect Camera ◽  
Home Based ◽  
Rehabilitation System

Abstractâ??Microsoft Kinect camera has been used in serious games applications, like for rehabilitation purposes, almost since it became available in the market. This article presents a clinical view regarding home-based physiotherapy for patients that suffered a stroke and details on the development of the rehabilitation system - Kinect-RehabPlay. This system uses the Kinect sensor together with the Unity3D game engine software to create the animation and visual environment. Currently, it is able to track, recording and comparing movements (doctor versus patient), and adjust the game configuration in real-time.

scholarly journals Real Time Apnoea Monitoring of Children Using the Microsoft Kinect Sensor: A Pilot Study

Sensors ◽  
2017 ◽  
Vol 17 (2) ◽  
pp. 286 ◽  
Author(s):  
Ali Al-Naji ◽  
Kim Gibson ◽  
Sang-Heon Lee ◽  
Javaan Chahl
Keyword(s):  
Pilot Study ◽  
Real Time ◽  
Microsoft Kinect ◽  
Kinect Sensor ◽  
Microsoft Kinect Sensor

Motion Performance Measurement using the Microsoft Kinect Sensor

2013 ◽  
Vol 2 (4) ◽  
pp. 28-37 ◽  
Author(s):  
Maliheh Fakhar ◽  
Saeed Behzadipour ◽  
Amir Mobini
Keyword(s):  
Performance Measurement ◽  
Healthy Subject ◽  
Microsoft Kinect ◽  
Upper Body ◽  
Kinect Sensor ◽  
Performance Indices ◽  
Kinematic Data ◽  
Home Based ◽  
Motion Performance ◽  
Microsoft Kinect Sensor

In this study, motion performance indices based on the kinematics of upper body have been presented and compared to be used in a home-based rehabilitation device. Microsoft Kinect sensor is used to extract and calculate such indices. A set of experiments has been designed and carried out in which, kinematic data of three patients has been recorded. Finally, the selected indices have been calculated, and the results were compared with those of a healthy subject.

scholarly journals Microsoft Kinect-Based Artificial Perception System for Control of Functional Electrical Stimulation Assisted Grasping

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Matija Štrbac ◽  
Slobodan Kočović ◽  
Marko Marković ◽  
Dejan B. Popović
Keyword(s):  
Electrical Stimulation ◽  
Real Time ◽  
Functional Electrical Stimulation ◽  
Microsoft Kinect ◽  
Hand Tracking ◽  
Human Visual Perception ◽  
Electrical Therapy ◽  
Kinect Camera ◽  
Perception System ◽  
Vision Algorithm

We present a computer vision algorithm that incorporates a heuristic model which mimics a biological control system for the estimation of control signals used in functional electrical stimulation (FES) assisted grasping. The developed processing software acquires the data from Microsoft Kinect camera and implements real-time hand tracking and object analysis. This information can be used to identify temporal synchrony and spatial synergies modalities for FES control. Therefore, the algorithm acts as artificial perception which mimics human visual perception by identifying the position and shape of the object with respect to the position of the hand in real time during the planning phase of the grasp. This artificial perception used within the heuristically developed model allows selection of the appropriate grasp and prehension. The experiments demonstrate that correct grasp modality was selected in more than 90% of tested scenarios/objects. The system is portable, and the components are low in cost and robust; hence, it can be used for the FES in clinical or even home environment. The main application of the system is envisioned for functional electrical therapy, that is, intensive exercise assisted with FES.

scholarly journals Frailty Level Classification of the Community Elderly Using Microsoft Kinect-Based Skeleton Pose: A Machine Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4017
Author(s):  
Ghasem Akbari ◽  
Mohammad Nikkhoo ◽  
Lizhen Wang ◽  
Carl P. C. Chen ◽  
Der-Sheng Han ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
The Elderly ◽  
Microsoft Kinect ◽  
Classification Model ◽  
Support Vector ◽  
Learning Approach ◽  
Kinect Sensor ◽  
Increased Risk ◽  
Machine Learning Approach

Frailty is one of the most important geriatric syndromes, which can be associated with increased risk for incident disability and hospitalization. Developing a real-time classification model of elderly frailty level could be beneficial for designing a clinical predictive assessment tool. Hence, the objective of this study was to predict the elderly frailty level utilizing the machine learning approach on skeleton data acquired from a Kinect sensor. Seven hundred and eighty-seven community elderly were recruited in this study. The Kinect data were acquired from the elderly performing different functional assessment exercises including: (1) 30-s arm curl; (2) 30-s chair sit-to-stand; (3) 2-min step; and (4) gait analysis tests. The proposed methodology was successfully validated by gender classification with accuracies up to 84 percent. Regarding frailty level evaluation and prediction, the results indicated that support vector classifier (SVC) and multi-layer perceptron (MLP) are the most successful estimators in prediction of the Fried’s frailty level with median accuracies up to 97.5 percent. The high level of accuracy achieved with the proposed methodology indicates that ML modeling can identify the risk of frailty in elderly individuals based on evaluating the real-time skeletal movements using the Kinect sensor.

Real-Time Human Action Recognition Using Deep Learning Architecture

2021 ◽  
Author(s):  
Souhila Kahlouche ◽  
Mahmoud Belhocine ◽  
Abdallah Menouar
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Action Recognition ◽  
Spatial Data ◽  
Short Term Memory ◽  
Human Action Recognition ◽  
Human Action ◽  
Microsoft Kinect ◽  
Kinect Camera ◽  
Public Datasets

In this work, efficient human activity recognition (HAR) algorithm based on deep learning architecture is proposed to classify activities into seven different classes. In order to learn spatial and temporal features from only 3D skeleton data captured from a “Microsoft Kinect” camera, the proposed algorithm combines both convolution neural network (CNN) and long short-term memory (LSTM) architectures. This combination allows taking advantage of LSTM in modeling temporal data and of CNN in modeling spatial data. The captured skeleton sequences are used to create a specific dataset of interactive activities; these data are then transformed according to a view invariant and a symmetry criterion. To demonstrate the effectiveness of the developed algorithm, it has been tested on several public datasets and it has achieved and sometimes has overcome state-of-the-art performance. In order to verify the uncertainty of the proposed algorithm, some tools are provided and discussed to ensure its efficiency for continuous human action recognition in real time.

scholarly journals KinectFusion for Faces: Real-Time 3D Face Tracking and Modeling Using a Kinect Camera for a Markerless AR System

2014 ◽  
Vol 4 (2) ◽  
pp. 1
Author(s):  
Márcio Cerqueira de Farias Macedo ◽  
Antônio Lopes Apolinário Jr. ◽  
Antonio Carlos dos Santos Souza
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
Initial Guess ◽  
Face Tracking ◽  
Head Pose Estimation ◽  
Kinect Sensor ◽  
Original Algorithm ◽  
Kinect Camera ◽  
3D Face Tracking ◽  
The Face

In this paper we present an extension to the KinectFusion algorithm that allows a robust real-time face tracking and modeling using the Microsoft’s Kinect sensor. This is achieved changing two steps of the original algorithm: pre-processing and tracking. In the former, we use a real-time face detection algorithm to segment the face from the rest of the image. In the latter, we use a real-time head pose estimation to give a new initial guess to the Iterative Closest Point (ICP) algorithm when it fails and an algorithm to solve occlusion.Our approach is evaluated in a markerless augmented reality (MAR) system. We show that this approach can reconstruct faces and handle more face pose changes and variations than the original KinectFusion’s tracking algorithm. In addition, we show that the realism of the system is enhanced as we solve the occlusion problem efficiently at shader level.

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