Assessment of Kinect V2 for elbow range of motion estimation in people with haemophilia using an angle correction model

Mateo, Fernando; Carrasco, Juan J.; Aguilar‐Rodríguez, Marta; Soria-Olivas, Emilio; Bonanad, Santiago; Querol, Felipe; Pérez‐Alenda, Sofía

doi:10.1111/hae.13744

Cited by 6 publications

(2 citation statements)

References 24 publications

(51 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The former requires depth camera devices, like Microsoft Kinect, 22 Intel RealSense, 23 and Leap Motion, 18 and have been evaluated in home care and telerehabilitation of the upper limb. 24 – 26 However, these devices may not be available for every patient. The other method could extract key points from a single image via general cameras or smartphones.…”

Section: Introductionmentioning

confidence: 99%

Automatic range of motion measurement via smartphone images for telemedicine examination of the hand

Fan

Wang

et al. 2023

Science Progress

View full text Add to dashboard Cite

Background Telemedicine support virtual consultations and evaluations in hand surgery for patients in remote areas during the COVID-19 era. However, traditional physical examination is challenging in telemedicine and it is inconvenient to manually measure the hand range of motion (ROM) from images or videos. Here, we propose an automatic method using the hand pose estimation technique, aiming to measure the hand ROM from smartphone images. Methods Twenty-eight healthy volunteers participated in the study. An eight-hand gestures measurement protocol and the Google MediaPipe Hands were used to analyze images and calculate the ROM automatically. Manual goniometry was also performed according to the guideline of the American Medical Association. The correlation between the automatic and manual methods was analyzed by the intraclass correlation coefficient and Pearson correlation coefficient. The clinical acceptance was testified using Bland-Altman plots. Results A total of 32 parameters of each hand were measured by both methods, and 1792 measurement results were compared. The mean difference between automatic and manual methods is −2.21 ± 9.29° in the angle measurement and 0.48 ± 0.48 cm in the distance measurement. The intraclass correlation coefficient of 75% of parameters was higher than 0.75, the Pearson correlation coefficient of 84% of parameters was over 0.6, and 40.6% of parameters reached well-accepted clinical agreements. Conclusions The proposed method provides a helpful protocol for automatic hand ROM measurement based on smartphone images and the MediaPipe Hands pose estimation technique. The automatic measurement is acceptable and comparable with existing methods, showing a possible application in the telemedicine examination of hand surgery.

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic range of motion measurement via smartphone images for telemedicine examination of the hand

Fan

Wang

et al. 2023

Science Progress

View full text Add to dashboard Cite

show abstract

“…Two physical therapy plans used to evaluate manual therapy-home exercises combined with educational courses-show that the two physical therapy plans could alleviate the elbow pain for hemophilia [20]. In [21], the ROM of the elbow joint was evaluated efficiently via a Microsoft Kinect v2 sensor with angle measurement calibration (for a contactless ROM measurement). In [22], researchers used a GoNet v2-combined computer with a Kinect sensor for the ROM measurement.…”

Section: Introductionmentioning

confidence: 99%

Development of Joint Activity Angle Measurement and Cloud Data Storage System

Tuan

Yeh

et al. 2022

Sensors

View full text Add to dashboard Cite

In this study, we developed a range of motion sensing system (ROMSS) to simulate the function of the elbow joint, with errors less than 0.76 degrees and 0.87 degrees in static and dynamic verification by the swinging and angle recognition modules, respectively. In the simulation process, the ɣ correlation coefficient of the Pearson difference between the ROMSS and the universal goniometer was 0.90, the standard deviations of the general goniometer measurements were between ±2 degrees and ±2.6 degrees, and the standard deviations between the ROMSS measurements were between ±0.5 degrees and ±1.6 degrees. With the ROMSS, a cloud database was also established; the data measured by the sensor could be uploaded to the cloud database in real-time to provide timely patient information for healthcare professionals. We also developed a mobile app for smartphones to enable patients and healthcare providers to easily trace the data in real-time. Historical data sets with joint activity angles could be retrieved to observe the progress or effectiveness of disease recovery so the quality of care could be properly assessed and maintained.

show abstract