Effectiveness of Gamification in Knee Replacement Rehabilitation: Protocol for a Randomized Controlled Trial With a Qualitative Approach.

BACKGROUND: Exergames can provide encouraging exercise options. Currently, there is limited evidence regarding home-based exergaming in the postoperative phase of total knee replacement (TKR). OBJECTIVE: This study aimed to investigate the effects of a 4-month postoperative home-based exergame intervention with an 8-month follow-up on physical function and symptoms among older persons undergoing TKR compared with home exercise using a standard protocol. In addition, a concurrent embedded design of a mixed methods study was used by including a qualitative component within a quantitative study of exergame effects. METHODS: This was a dual-center, nonblinded, two-arm, parallel group randomized controlled trial with an embedded qualitative approach. This study aimed to recruit 100 patients who underwent their first unilateral TKR (aged 60-75 years). Participants were randomized to the exergame or standard home exercise arms. Participants followed a custom-made exergame program independently at their homes daily for 4 months. The primary outcomes at 4 months were function and pain related to the knee using the Oxford Knee Score questionnaire and mobility using the Timed Up and Go test. Other outcomes, in addition to physical function, symptoms, and disability, were game user experience, exercise adherence, physical activity, and satisfaction with the operated knee. Assessments were performed at the preoperative baseline and at 2, 4, and 12 months postoperatively. Exergame adherence was followed from game computers and using a structured diary. Self-reported standard exercise was followed for 4 months of intervention and physical activity was followed for 12 months using a structured diary. Qualitative data on patients' perspectives on rehabilitation and exergames were collected through laddering interviews at 4 and 12 months. RESULTS: This study was funded in 2018. Data collection began in 2019 and was completed in January 2022. The COVID-19 pandemic caused an unavoidable situation in the study for recruitment, data collection, and statistical analysis. As of November 2020, a total of 52 participants had been enrolled in the study. Primary results are expected to be published by the end of 2022. CONCLUSIONS: Our study provides new knowledge on the effects of postoperative exergame intervention among older patients with TKR. In addition, this study provides a new understanding of gamified postoperative rehabilitation, home exercise adherence, physical function, and physical activity among older adults undergoing TKR. TRIAL REGISTRATION: ClinicalTrials.gov NCT03717727; https://clinicaltrials.gov/ct2/show/NCT03717727. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/38434.

A New Augmented Reality System for Calculating Social Distancing between Children at School

Social distancing is one of the most important ways to prevent many diseases, especially the respiratory system, where the latest internationally spread is coronavirus disease, and it will not be the last. The spreading of this pandemic has become a major threat to human life, especially to the elderly and people suffering from chronic diseases. During the Corona pandemic, medical authorities were keen to control the spread through social distancing and monitoring it in markets, universities, and schools. This monitoring was mostly used to estimate the distance with the naked eye and interfere with estimating the distance on the observer only. In this study, a computer application was designed to monitor social distancing in closed areas, especially in schools and kindergartens, using a fast, effective and unobtrusive technique for children. In addition to this system, we use augmented reality to help to determine the location of violation of social distancing. This system was tested, and the results were accurate exceeding 98.5%.

A Remote Rehabilitation and Evaluation System Based on Azure Kinect

In response to the shortage, uneven distribution, and high cost of rehabilitation resources in the context of the COVID-19 pandemic, we developed a low-cost, easy-to-use remote rehabilitation system that allows patients to perform rehabilitation training and receive real-time guidance from doctors at home. The proposed system uses Azure Kinect to capture motions with an error of just 3% compared to professional motion capture systems. In addition, the system pro-vides an automatic evaluation function of rehabilitation training, including evaluation of motion angles and trajectories. After acquiring the user's 3D mo-tions, the system synchronizes the 3D motions to the virtual human body model in Unity with an average error of less than 1%, which gives the user a more intuitive and interactive experience. After a series of evaluation experiments, we verified the usability, con-venience, and high accuracy of the system, finally con-cluding that the system can be used in practical rehabilitation applications. © Fuji Technology Press Ltd.

IoT-Based Touchless Remote Control for Robotic Arm Using Kinect

Due to the current labor shortage situation, combined with the spread of COVID-19, the researchers came up with the idea of developing a contactless remote robotic arm system based on IoT. This research focuses on developing prototypes of remote control three-axis robotic arm via the Internet that can be applied in industrial, medical, and other applications. Abiding by the new normal situation, the Kinect sensor control input, a device capable of receiving commands from human gestures without touching, is used to alleviate the spread of the virus. From the development and experiment, it can be shown that the developed artifact can receive commands from human gestures to remotely control the robotic arm via the Internet in accordance with the intended purpose. © 2021 IEEE.

Development of visualization algorithm for appropriate compression tempo in cardiopulmonary resuscitation training system

Cardiopulmonary resuscitation (CPR) is the most effective quick responsive method to avoid the risk of death due to a heart attack. However, no practical CPR education has been established regarding the citizens' correct posture while performing CPR. Herein, we developed a training system using an Azure Kinect DK sensor camera to visualise the upper and lower body posture while performing the CPR operation from both the front and side. Assuming its use under COVID-19 restrictions, we implemented a noncontact voice-activated interface and functionality for accurately detecting system' pressure. For evaluating the training system, after using it for the airport and life insurance employees training, a comparative investigation was conducted to determine whether the system's applicability for training. As per B business office experiment, when the number of data items was n=44, a correlation coefficient with a strong correlation of 0.662 was obtained. For regression analysis performed on B business office, total posture score and compression frequency significantly differed (significant probability mathrm{P}lt 0.001 and significance level 5%). © 2022 IEEE.

Autonomous object detection and tracking robot using Kinect v2

Object detection and tracking are one of the key features of a robust autonomous mobile robot, allowing it to navigate places and avoid obstacles. The Mobile robotics market and proliferation has been growing and the Covid-19 era has added another boost to this area where more and more interest is being drawn to the autonomous capabilities of these machines. In this paper we propose a hardware based model to detect and track objects based on color. We propose robust object detection and tracking with minimum environmental constraints to improve accuracy using our algorithm, and capable of behaving well in unknown environmental conditions. At the end of the analysis, the robot was able to detect the object and track it well. We also show frequency analysis, compression and error analysis of the underlying technique. Experimental outcomes verify improved accuracy of our algorithm.

Pilot Feasibility Study of a Multi-View Vision Based Scoring Method for Cervical Dystonia.

Abnormal movement of the head and neck is a typical symptom of Cervical Dystonia (CD). Accurate scoring on the severity scale is of great significance for treatment planning. The traditional scoring method is to use a protractor or contact sensors to calculate the angle of the movement, but this method is time-consuming, and it will interfere with the movement of the patient. In the recent outbreak of the coronavirus disease, the need for remote diagnosis and treatment of CD has become extremely urgent for clinical practice. To solve these problems, we propose a multi-view vision based CD severity scale scoring method, which detects the keypoint positions of the patient from the frontal and lateral images, and finally scores the severity scale by calculating head and neck motion angles. We compared the Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) subscale scores calculated by our vision based method with the scores calculated by a neurologist trained in dyskinesia. An analysis of the correlation coefficient was then conducted. Intra-class correlation (ICC)(3,1) was used to measure absolute accuracy. Our multi-view vision based CD severity scale scoring method demonstrated sufficient validity and reliability. This low-cost and contactless method provides a new potential tool for remote diagnosis and treatment of CD.

Implementation of Remote Control of a Robot Arm by Hand Gestures

Recently, the coronavirus pandemic has led to a new normal lifestyle, and reducing contact with humans by 70% is necessary in order for preventing infection. Therefore, in order to reduce the chances of contact between humans to the greatest degree possible, we propose a prototype method for remotely controlling a robot arm using human hand gestures in a non-contact manner. By multithread processing of involving hand gesture recognition using a Kinect device, and conversion between the camera coordinate system and the robot coordinate system, smooth operation of the robot arm is realized by asynchronous communication using exclusive control (mutex class in C++) and queue buffering (queue class on C++). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Application of Somatosensory Camera in Robot Remote Experimental Teaching

This article proposes a method to remotely control the robot in the laboratory through the Kinect camera to solve the impact of the Covid-19 epidemic on the laboratory teaching experience which allows users to remotely control robots through their own body movements to understand the principles of robots. It is used to solve the problem of fewer students willing to participate in robot remote education. In this study, the Azure Kinect DK camera was used to collect the motion posture of the upper limbs of the human body. The Kinect camera calculates the frames of human arm joints' motion. The control system calculates the direction of motion of each joint of the human body based on the quaternion by mapping the heterogeneous human joints with the robot joints. Make the posture of the human arm swing correspond to the posture of the robot's movement. Thus, the robot in the laboratory can be driven remotely through Azure Kinect DK. By using the method described in this article, students use the camera's motion capture system to remotely manipulate the robot to grab some simple objects. Through the method described in this research, students can carry out some simple operations on the robots in the laboratory from remote. So it is convenient for students to understand the basic principles of robots and achieve the purpose of better remote experimental teaching. At the same time, students can get practical application of motor servo control, ergonomics, physical simulation engine, digital twin system, etc. © 2022 IEEE.

Kinect v2-Assisted Semi-Automated Method to Assess Upper Limb Motor Performance in Children.

The interruption of rehabilitation activities caused by the COVID-19 lockdown has significant health negative consequences for the population with physical disabilities. Thus, measuring the range of motion (ROM) using remotely taken photographs, which are then sent to specialists for formal assessment, has been recommended. Currently, low-cost Kinect motion capture sensors with a natural user interface are the most feasible implementations for upper limb motion analysis. An active range of motion (AROM) measuring system based on a Kinect v2 sensor for upper limb motion analysis using Fugl-Meyer Assessment (FMA) scoring is described in this paper. Two test groups of children, each having eighteen participants, were analyzed in the experimental stage, where upper limbs' AROM and motor performance were assessed using FMA. Participants in the control group (mean age of 7.83 ± 2.54 years) had no cognitive impairment or upper limb musculoskeletal problems. The study test group comprised children aged 8.28 ± 2.32 years with spastic hemiparesis. A total of 30 samples of elbow flexion and 30 samples of shoulder abduction of both limbs for each participant were analyzed using the Kinect v2 sensor at 30 Hz. In both upper limbs, no significant differences (p < 0.05) in the measured angles and FMA assessments were observed between those obtained using the described Kinect v2-based system and those obtained directly using a universal goniometer. The measurement error achieved by the proposed system was less than ±1° compared to the specialist's measurements. According to the obtained results, the developed measuring system is a good alternative and an effective tool for FMA assessment of AROM and motor performance of upper limbs, while avoiding direct contact in both healthy children and children with spastic hemiparesis.

A Low-Cost Telerehabilitation Paradigm for Bimanual Training

The COVID-19 pandemic has transformed daily life, as individuals must reduce contacts among each other to prevent the spread of the disease. Consequently, patients’ access to outpatient rehabilitation care was curtailed and their prospect for recovery has been compromised. Telerehabilitation has the potential to provide these patients with equally efficacious therapy in their homes. Using commercial gaming devices with embedded motion sensors, data on movement can be collected toward objective assessment of motor performance, followed by training and documentation of progress. Herein, we present a low-cost telerehabilitation system dedicated to bimanual exercise, wherein the healthy arm drives movements of the affected arm. In the proposed setting, a patient manipulates a dowel embedded with a sensor in front of a Microsoft Kinect sensor. In order to provide an engaging environment for the exercise, the dowel is interfaced with a personal computer, to serve as a controller. The patient’s gestures are translated into actions in a custom-made citizen-science project. Along with the system, we introduce an algorithm for classification of the bimanual movements, whose inner workings are detailed in terms of the procedures performed for dimensionality reduction, feature extraction, and movement classification. We demonstrate the feasibility of our system on eight healthy subjects, offering support to the validity of the algorithm. These preliminary findings set forth the development of precise motion analysis algorithms in affordable home-based rehabilitation.

The Virtual Kinect

The COVID-19 pandemic impacted researches that depended on making tests on patients and teams that had to be divided to avoid crowding in laboratories. Sharing equipment is no longer a simple strategy;the acquisition of extra equipment is beyond the means of many researchers. Research with Kinect V2 applied to body tracking suffers from sanitary restrictions, product discontinuation and limited access to newer sensors (like the Azure Kinect). Kinect V2 is an RGB-D sensor with many applications in health, ergonomics, sports, games and other areas. That is why a lot of research is still under development with it. Because of the applicability of Kinect V2 on research and the current acquisition limitations, Virtual Kinect (VK) was created. VK is an open-source solution that enables the programmer to code using Kinect SDK functions and test it without a Kinect V2 sensor or a previous recording. It simulates the behavior of a Kinect V2 sensor through MediaPipe's Pose estimation, providing RGB image and joint tracking information, correlating the joints of the two devices. This correlation is possible due to the proximity of the devices' joint estimates. The VK was made to be simple and practical, so that its use only depends on the DLL exchange and the use of an ordinary RGB camera. © 2021 ACM.

A Body Tracking-Based Low-Cost Solution for Monitoring Workers' Hygiene Best Practices during Pandemics.

Since its beginning at the end of 2019, the pandemic spread of the severe acute respiratory syndrome coronavirus 2 (Sars-CoV-2) caused more than one million deaths in only nine months. The threat of emerging and re-emerging infectious diseases exists as an imminent threat to human health. It is essential to implement adequate hygiene best practices to break the contagion chain and enhance society preparedness for such critical scenarios and understand the relevance of each disease transmission route. As the unconscious hand-face contact gesture constitutes a potential pathway of contagion, in this paper, the authors present a prototype system based on low-cost depth sensors able to monitor in real-time the attitude towards such a habit. The system records people's behavior to enhance their awareness by providing real-time warnings, providing for statistical reports for designing proper hygiene solutions, and better understanding the role of such route of contagion. A preliminary validation study measured an overall accuracy of 91%. A Cohen's Kappa equal to 0.876 supports rejecting the hypothesis that such accuracy is accidental. Low-cost body tracking technologies can effectively support monitoring compliance with hygiene best practices and training people in real-time. By collecting data and analyzing them with respect to people categories and contagion statistics, it could be possible to understand the importance of this contagion pathway and identify for which people category such a behavioral attitude constitutes a significant risk.