» Articles » PMID: 24996956

Systematic Review of Kinect Applications in Elderly Care and Stroke Rehabilitation

Overview
Publisher Biomed Central
Date 2014 Jul 6
PMID 24996956
Citations 90
Authors
Affiliations
Soon will be listed here.
Abstract

In this paper we present a review of the most current avenues of research into Kinect-based elderly care and stroke rehabilitation systems to provide an overview of the state of the art, limitations, and issues of concern as well as suggestions for future work in this direction. The central purpose of this review was to collect all relevant study information into one place in order to support and guide current research as well as inform researchers planning to embark on similar studies or applications. The paper is structured into three main sections, each one presenting a review of the literature for a specific topic. Elderly Care section is comprised of two subsections: Fall detection and Fall risk reduction. Stroke Rehabilitation section contains studies grouped under Evaluation of Kinect's spatial accuracy, and Kinect-based rehabilitation methods. The third section, Serious and exercise games, contains studies that are indirectly related to the first two sections and present a complete system for elderly care or stroke rehabilitation in a Kinect-based game format. Each of the three main sections conclude with a discussion of limitations of Kinect in its respective applications. The paper concludes with overall remarks regarding use of Kinect in elderly care and stroke rehabilitation applications and suggestions for future work. A concise summary with significant findings and subject demographics (when applicable) of each study included in the review is also provided in table format.

Citing Articles

Navigating Through Innovation in Elderly's Health: A Scoping Review of Digital Health Interventions.

Hirmas-Adauy M, Castillo-Laborde C, Awad C, Jasmen A, Mattoli M, Molina X Public Health Rev. 2025; 45:1607756.

PMID: 39749218 PMC: 11693459. DOI: 10.3389/phrs.2024.1607756.


Diagnosis of Autism in Children Based on their Gait Pattern and Movement Signs Using the Kinect Sensor.

Yazdi S, Janghorbani A, Maleki A J Med Signals Sens. 2024; 14:29.

PMID: 39600983 PMC: 11592996. DOI: 10.4103/jmss.jmss_19_24.


Mitigating Trunk Compensatory Movements in Post-Stroke Survivors through Visual Feedback during Robotic-Assisted Arm Reaching Exercises.

Lee S, Song W Sensors (Basel). 2024; 24(11).

PMID: 38894119 PMC: 11174622. DOI: 10.3390/s24113331.


The Application of Virtual Reality in Shoulder Surgery Rehabilitation.

Nam J, Koh Y, Chung S, Kim P, Jang J, Park J Cureus. 2024; 16(4):e58280.

PMID: 38752078 PMC: 11094526. DOI: 10.7759/cureus.58280.


Predictive trajectory estimation during rehabilitative tasks in augmented reality using inertial sensors.

Hunt C, Sharma A, Osborn L, Kaliki R, Thakor N IEEE Biomed Circuits Syst Conf. 2024; 2018.

PMID: 38501114 PMC: 10947724. DOI: 10.1109/biocas.2018.8584805.


References
1.
GARDNER M, Robertson M, Campbell A . Exercise in preventing falls and fall related injuries in older people: a review of randomised controlled trials. Br J Sports Med. 2000; 34(1):7-17. PMC: 1724164. DOI: 10.1136/bjsm.34.1.7. View

2.
Geffken D, Cushman M, Burke G, Polak J, Sakkinen P, Tracy R . Association between physical activity and markers of inflammation in a healthy elderly population. Am J Epidemiol. 2001; 153(3):242-50. DOI: 10.1093/aje/153.3.242. View

3.
Dobkin B . Clinical practice. Rehabilitation after stroke. N Engl J Med. 2005; 352(16):1677-84. PMC: 4106469. DOI: 10.1056/NEJMcp043511. View

4.
Tinetti M, Speechley M, Ginter S . Risk factors for falls among elderly persons living in the community. N Engl J Med. 1988; 319(26):1701-7. DOI: 10.1056/NEJM198812293192604. View

5.
Staiano A, Calvert S . The promise of exergames as tools to measure physical health. Entertain Comput. 2013; 2(1):17-21. PMC: 3562354. DOI: 10.1016/j.entcom.2011.03.008. View