A 3D-Printed Knee Wearable Goniometer with a Mobile-App Interface for Measuring Range of Motion and Monitoring Activities
Wearable technology has been developed in recent years to monitor biomechanical variables in less restricted environments and in a more affordable way than optical motion capture systems. This paper proposes the development of a 3D printed knee wearable goniometer that uses a Hall-effect sensor to m...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-01-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/3/763 |
| _version_ | 1797484901765742592 |
|---|---|
| author | Bryan Rivera Consuelo Cano Israel Luis Dante A. Elias |
| author_facet | Bryan Rivera Consuelo Cano Israel Luis Dante A. Elias |
| author_sort | Bryan Rivera |
| collection | DOAJ |
| description | Wearable technology has been developed in recent years to monitor biomechanical variables in less restricted environments and in a more affordable way than optical motion capture systems. This paper proposes the development of a 3D printed knee wearable goniometer that uses a Hall-effect sensor to measure the knee flexion angle, which works with a mobile app that shows the angle in real-time as well as the activity the user is performing (standing, sitting, or walking). Detection of the activity is done through an algorithm that uses the knee angle and angular speeds as inputs. The measurements of the wearable are compared with a commercial goniometer, and, with the Aktos-t system, a commercial motion capture system based on inertial sensors, at three speeds of gait (4.0 km/h, 4.5 km/h, and 5.0 km/h) in nine participants. Specifically, the four differences between maximum and minimum peaks in the gait cycle, starting with heel-strike, were compared by using the mean absolute error, which was between 2.46 and 12.49 on average. In addition, the algorithm was able to predict the three activities during online testing in one participant and detected on average 94.66% of the gait cycles performed by the participants during offline testing. |
| first_indexed | 2024-03-09T23:11:04Z |
| format | Article |
| id | doaj.art-bc48d9ccb99a4193ac3e8ed54f6c124c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T23:11:04Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-bc48d9ccb99a4193ac3e8ed54f6c124c2023-11-23T17:44:58ZengMDPI AGSensors1424-82202022-01-0122376310.3390/s22030763A 3D-Printed Knee Wearable Goniometer with a Mobile-App Interface for Measuring Range of Motion and Monitoring ActivitiesBryan Rivera0Consuelo Cano1Israel Luis2Dante A. Elias3Laboratory of Biomechanics and Applied Robotics, Pontificia Universidad Católica del Perú, Lima 15088, PeruLaboratory of Biomechanics and Applied Robotics, Pontificia Universidad Católica del Perú, Lima 15088, PeruLaboratory of Biomechanics and Applied Robotics, Pontificia Universidad Católica del Perú, Lima 15088, PeruLaboratory of Biomechanics and Applied Robotics, Pontificia Universidad Católica del Perú, Lima 15088, PeruWearable technology has been developed in recent years to monitor biomechanical variables in less restricted environments and in a more affordable way than optical motion capture systems. This paper proposes the development of a 3D printed knee wearable goniometer that uses a Hall-effect sensor to measure the knee flexion angle, which works with a mobile app that shows the angle in real-time as well as the activity the user is performing (standing, sitting, or walking). Detection of the activity is done through an algorithm that uses the knee angle and angular speeds as inputs. The measurements of the wearable are compared with a commercial goniometer, and, with the Aktos-t system, a commercial motion capture system based on inertial sensors, at three speeds of gait (4.0 km/h, 4.5 km/h, and 5.0 km/h) in nine participants. Specifically, the four differences between maximum and minimum peaks in the gait cycle, starting with heel-strike, were compared by using the mean absolute error, which was between 2.46 and 12.49 on average. In addition, the algorithm was able to predict the three activities during online testing in one participant and detected on average 94.66% of the gait cycles performed by the participants during offline testing.https://www.mdpi.com/1424-8220/22/3/763wearablegoniometerHall-effect sensoralgorithm |
| spellingShingle | Bryan Rivera Consuelo Cano Israel Luis Dante A. Elias A 3D-Printed Knee Wearable Goniometer with a Mobile-App Interface for Measuring Range of Motion and Monitoring Activities Sensors wearable goniometer Hall-effect sensor algorithm |
| title | A 3D-Printed Knee Wearable Goniometer with a Mobile-App Interface for Measuring Range of Motion and Monitoring Activities |
| title_full | A 3D-Printed Knee Wearable Goniometer with a Mobile-App Interface for Measuring Range of Motion and Monitoring Activities |
| title_fullStr | A 3D-Printed Knee Wearable Goniometer with a Mobile-App Interface for Measuring Range of Motion and Monitoring Activities |
| title_full_unstemmed | A 3D-Printed Knee Wearable Goniometer with a Mobile-App Interface for Measuring Range of Motion and Monitoring Activities |
| title_short | A 3D-Printed Knee Wearable Goniometer with a Mobile-App Interface for Measuring Range of Motion and Monitoring Activities |
| title_sort | 3d printed knee wearable goniometer with a mobile app interface for measuring range of motion and monitoring activities |
| topic | wearable goniometer Hall-effect sensor algorithm |
| url | https://www.mdpi.com/1424-8220/22/3/763 |
| work_keys_str_mv | AT bryanrivera a3dprintedkneewearablegoniometerwithamobileappinterfaceformeasuringrangeofmotionandmonitoringactivities AT consuelocano a3dprintedkneewearablegoniometerwithamobileappinterfaceformeasuringrangeofmotionandmonitoringactivities AT israelluis a3dprintedkneewearablegoniometerwithamobileappinterfaceformeasuringrangeofmotionandmonitoringactivities AT danteaelias a3dprintedkneewearablegoniometerwithamobileappinterfaceformeasuringrangeofmotionandmonitoringactivities AT bryanrivera 3dprintedkneewearablegoniometerwithamobileappinterfaceformeasuringrangeofmotionandmonitoringactivities AT consuelocano 3dprintedkneewearablegoniometerwithamobileappinterfaceformeasuringrangeofmotionandmonitoringactivities AT israelluis 3dprintedkneewearablegoniometerwithamobileappinterfaceformeasuringrangeofmotionandmonitoringactivities AT danteaelias 3dprintedkneewearablegoniometerwithamobileappinterfaceformeasuringrangeofmotionandmonitoringactivities |