Wearable Inertial Sensor System Towards Daily Human Kinematic Gait Analysis: Benchmarking Analysis to MVN BIOMECH
This paper presents a cost- and time-effective wearable inertial sensor system, the InertialLAB. It includes gyroscopes and accelerometers for the real-time monitoring of 3D-angular velocity and 3D-acceleration of up to six lower limbs and trunk segment and sagittal joint angle up to six joints. Ine...
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doaj-69f8d255899f4064be74712f2fd88d052020-11-25T03:25:11ZengMDPI AGSensors1424-82202020-04-01202185218510.3390/s20082185Wearable Inertial Sensor System Towards Daily Human Kinematic Gait Analysis: Benchmarking Analysis to MVN BIOMECHJoana Figueiredo0Simão P. Carvalho1João Paulo Vilas-Boas2Luís M. Gonçalves3Juan C. Moreno4Cristina P. Santos5Center for MicroElectroMechanical Systems (CMEMS), Industrial Electronics Department, University of Minho, Guimarães 4800-058, PortugalCenter for MicroElectroMechanical Systems (CMEMS), Industrial Electronics Department, University of Minho, Guimarães 4800-058, PortugalFaculty of Sport, CIFI2D, and Porto Biomechanics Laboratory (LABIOMEP), University of Porto, Porto 4200-450, PortugalCenter for MicroElectroMechanical Systems (CMEMS), Industrial Electronics Department, University of Minho, Guimarães 4800-058, PortugalNeural Rehabilitation Group, Cajal Institute, Spanish National Research Council, Madrid 28002, SpainCenter for MicroElectroMechanical Systems (CMEMS), Industrial Electronics Department, University of Minho, Guimarães 4800-058, PortugalThis paper presents a cost- and time-effective wearable inertial sensor system, the InertialLAB. It includes gyroscopes and accelerometers for the real-time monitoring of 3D-angular velocity and 3D-acceleration of up to six lower limbs and trunk segment and sagittal joint angle up to six joints. InertialLAB followed an open architecture with a low computational load to be executed by wearable processing units up to 200 Hz for fostering kinematic gait data to third-party systems, advancing similar commercial systems. For joint angle estimation, we developed a trigonometric method based on the segments’ orientation previously computed by fusion-based methods. The validation covered healthy gait patterns in varying speed and terrain (flat, ramp, and stairs) and including turns, extending the experiments approached in the literature. The benchmarking analysis to MVN BIOMECH reported that InertialLAB provides more reliable measures in stairs than in flat terrain and ramp. The joint angle time-series of InertialLAB showed good waveform similarity (>0.898) with MVN BIOMECH, resulting in high reliability and excellent validity. User-independent neural network regression models successfully minimized the drift errors observed in InertialLAB’s joint angles (NRMSE < 0.092). Further, users ranked InertialLAB as good in terms of usability. InertialLAB shows promise for daily kinematic gait analysis and real-time kinematic feedback for wearable third-party systems.https://www.mdpi.com/1424-8220/20/8/2185inertial sensorsgait analysishuman daily motion analysissensor fusionwearable sensors |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Joana Figueiredo Simão P. Carvalho João Paulo Vilas-Boas Luís M. Gonçalves Juan C. Moreno Cristina P. Santos |
spellingShingle |
Joana Figueiredo Simão P. Carvalho João Paulo Vilas-Boas Luís M. Gonçalves Juan C. Moreno Cristina P. Santos Wearable Inertial Sensor System Towards Daily Human Kinematic Gait Analysis: Benchmarking Analysis to MVN BIOMECH Sensors inertial sensors gait analysis human daily motion analysis sensor fusion wearable sensors |
author_facet |
Joana Figueiredo Simão P. Carvalho João Paulo Vilas-Boas Luís M. Gonçalves Juan C. Moreno Cristina P. Santos |
author_sort |
Joana Figueiredo |
title |
Wearable Inertial Sensor System Towards Daily Human Kinematic Gait Analysis: Benchmarking Analysis to MVN BIOMECH |
title_short |
Wearable Inertial Sensor System Towards Daily Human Kinematic Gait Analysis: Benchmarking Analysis to MVN BIOMECH |
title_full |
Wearable Inertial Sensor System Towards Daily Human Kinematic Gait Analysis: Benchmarking Analysis to MVN BIOMECH |
title_fullStr |
Wearable Inertial Sensor System Towards Daily Human Kinematic Gait Analysis: Benchmarking Analysis to MVN BIOMECH |
title_full_unstemmed |
Wearable Inertial Sensor System Towards Daily Human Kinematic Gait Analysis: Benchmarking Analysis to MVN BIOMECH |
title_sort |
wearable inertial sensor system towards daily human kinematic gait analysis: benchmarking analysis to mvn biomech |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-04-01 |
description |
This paper presents a cost- and time-effective wearable inertial sensor system, the InertialLAB. It includes gyroscopes and accelerometers for the real-time monitoring of 3D-angular velocity and 3D-acceleration of up to six lower limbs and trunk segment and sagittal joint angle up to six joints. InertialLAB followed an open architecture with a low computational load to be executed by wearable processing units up to 200 Hz for fostering kinematic gait data to third-party systems, advancing similar commercial systems. For joint angle estimation, we developed a trigonometric method based on the segments’ orientation previously computed by fusion-based methods. The validation covered healthy gait patterns in varying speed and terrain (flat, ramp, and stairs) and including turns, extending the experiments approached in the literature. The benchmarking analysis to MVN BIOMECH reported that InertialLAB provides more reliable measures in stairs than in flat terrain and ramp. The joint angle time-series of InertialLAB showed good waveform similarity (>0.898) with MVN BIOMECH, resulting in high reliability and excellent validity. User-independent neural network regression models successfully minimized the drift errors observed in InertialLAB’s joint angles (NRMSE < 0.092). Further, users ranked InertialLAB as good in terms of usability. InertialLAB shows promise for daily kinematic gait analysis and real-time kinematic feedback for wearable third-party systems. |
topic |
inertial sensors gait analysis human daily motion analysis sensor fusion wearable sensors |
url |
https://www.mdpi.com/1424-8220/20/8/2185 |
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