Assessing the kinect’s capabilities to perform a time-based clinical test for fall risk assessment in older people

© 2014 IFIP International Federation for Information Processing. The Choice Stepping Reaction Time (CSRT) task is time-based clinical test that has shown to reliably predict falls in older adults. Its current mode of delivery involves the use of a custom-made dance mat device. This mat is a measurement tool that can reliably obtain step data to discriminate between fallers and non-fallers. One of the pitfalls of this test is that the technology in use still imposes an obstacle on the degree of freedom to be able to perform adaptive exercises suitable for the elderly. In this paper, we describe a Kinect-based system that measures stepping performance through the use of a hybrid version of the CSRT task. This study focuses on assessing this system’s capabilities to reliably measure a time-based clinical test of fall risk. Results showed a favorable correspondence and agreement between the two systems, suggesting that this platform could be potentially useful in the clinical practice

Development of a Real-Time, Simple and High-Accuracy Fall Detection System for Elderly Using 3-DOF Accelerometers

© 2018, King Fahd University of Petroleum & Minerals. Falls represent a major problem for the elderly people aged 60 or above. There are many monitoring systems which are currently available to detect the fall. However, there is a great need to propose a system which is of optimal effectiveness. In this paper, we propose to develop a low-cost fall detection system to precisely detect an event when an elderly person accidentally falls. The fall detection algorithm compares the acceleration with lower fall threshold and upper fall threshold values to accurately detect a fall event. The post-fall recognition module is the combination of posture recognition and vertical velocity estimation that has been added to our proposed method to enhance the performance and accuracy. In case of a fall, our device will transmit the location information to the contacts instantly via SMS and voice call. A smartphone application will ensure that the notifications are delivered to the elderly person’s relatives so that medical attention can be provided with minimal delay. The system was tested by volunteers and achieved 100% sensitivity and accuracy. This was confirmed by testing with public datasets and it also achieved the same percentage in sensitivity and accuracy as in our recorded datasets

Evaluation of Pose Tracking Accuracy in the First and Second Generations of Microsoft Kinect

Microsoft Kinect camera and its skeletal tracking capabilities have been embraced by many researchers and commercial developers in various applications of real-time human movement analysis. In this paper, we evaluate the accuracy of the human kinematic motion data in the first and second generation of the Kinect system, and compare the results with an optical motion capture system. We collected motion data in 12 exercises for 10 different subjects and from three different viewpoints. We report on the accuracy of the joint localization and bone length estimation of Kinect skeletons in comparison to the motion capture. We also analyze the distribution of the joint localization offsets by fitting a mixture of Gaussian and uniform distribution models to determine the outliers in the Kinect motion data. Our analysis shows that overall Kinect 2 has more robust and more accurate tracking of human pose as compared to Kinect 1.Comment: 10 pages, IEEE International Conference on Healthcare Informatics 2015 (ICHI 2015

Depth sensor based object detection using surface curvature

An object detection system finds objects from an image or video sequence of the real world. The good performance of object detection has been largely driven by the development of well-established robust feature sets. By using conventional color images as input, researchers have achieved major success. Recent dramatic advances in depth imaging technology triggered significant attention to revisit object detection problems using depth images as input. Using depth information, we propose a feature, Histogram of Oriented Curvature (HOC), designed specifically to capture local surface shape for object detection with depth sensor. We form the HOC feature as a concatenation of the local histograms of Gaussian curvature and mean curvature. The linear Support Vector Machine (SVM) is employed for the object detection task in this work. We evaluate our proposed HOC feature on two widely used datasets and compare the results with other well-known object detection methods applied on both RGB images and depth images. Our experimental results show that the proposed HOC feature generally outperform the HOG and HOGD features in object detection task, and can achieve similar or higher results compared with the state-of-the-art depth descriptor HONV on some object categories

Human gait identification using Kinect sensor

This study investigates a novel three-dimension gait recognition approach based on skeleton representation of motion by the cheap consumer level camera Kinect sensor. In this work, a new exemplification of human gait signature is proposed using the spatio-temporal variations in relative angles among various skeletal joints and changing of measured distance between limbs and land. These measurements are computed during one gait cycle. Further, we have created our own dataset based on Kinect sensor and extract two sets of dynamic features. Nearest Neighbors and Linear Discriminant Classifier (LDC) are used for classification. The results of the experiments show the proposed approach as an effective and human gait recognizer in comparison with current Kinect-based gait recognition methods

Quantitative Evaluation of the Microsoft Kinect for Use in an Upper Extremity Virtual Rehabilitation Environment

To be presented at the International Conference on Virtual Rehabilitation (ICVR), Philadelphia, PA, U.S.A., August 26-29, 2013.Low cost depth sensors could potentially allow for home-based care and rehabilitation using virtual systems. Currently, no publicly available and peer-reviewed assessment has been made on the accuracy of joint position data determined by the Microsoft KinectTM for assessment of upper extremity movements. We devised and validated clinically-based angle classifications for random arm movements in 3D-space, specifically, the shoulder joint flexion/extension angle, shoulder joint abduction/adduction angle, and 3-dimensional shoulder joint angle of 19 subjects at a distance of 2.0m using an eight camera Vicon Motion Capture system. Results show an average absolute error of these angle measurements not exceeding 10.0%

The Design and Evaluation of a Kinect-Based Postural Symmetry Assessment and Training System

abstract: The increased risk of falling and the worse ability to perform other daily physical activities in the elderly cause concern about monitoring and correcting basic everyday movement. In this thesis, a Kinect-based system was designed to assess one of the most important factors in balance control of human body when doing Sit-to-Stand (STS) movement: the postural symmetry in mediolateral direction. A symmetry score, calculated by the data obtained from a Kinect RGB-D camera, was proposed to reflect the mediolateral postural symmetry degree and was used to drive a real-time audio feedback designed in MAX/MSP to help users adjust themselves to perform their movement in a more symmetrical way during STS. The symmetry score was verified by calculating the Spearman correlation coefficient with the data obtained from Inertial Measurement Unit (IMU) sensor and got an average value at 0.732. Five healthy adults, four males and one female, with normal balance abilities and with no musculoskeletal disorders, were selected to participate in the experiment and the results showed that the low-cost Kinect-based system has the potential to train users to perform a more symmetrical movement in mediolateral direction during STS movement.Dissertation/ThesisMasters Thesis Electrical Engineering 201