Estimating Vertical Ground Reaction Force during Walking Using a Single Inertial Sensor
The vertical ground reaction force (vGRF) and its passive and active peaks are important gait parameters and of great relevance for musculoskeletal injury analysis and prevention, the detection of gait abnormities, and the evaluation of lower-extremity prostheses. Most currently available methods to...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/15/4345 |
| _version_ | 1797560313768312832 |
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| author | Xianta Jiang Christopher Napier Brett Hannigan Janice J. Eng Carlo Menon |
| author_facet | Xianta Jiang Christopher Napier Brett Hannigan Janice J. Eng Carlo Menon |
| author_sort | Xianta Jiang |
| collection | DOAJ |
| description | The vertical ground reaction force (vGRF) and its passive and active peaks are important gait parameters and of great relevance for musculoskeletal injury analysis and prevention, the detection of gait abnormities, and the evaluation of lower-extremity prostheses. Most currently available methods to estimate the vGRF require a force plate. However, in real-world scenarios, gait monitoring would not be limited to a laboratory setting. This paper reports a novel solution using machine learning algorithms to estimate the vGRF and the timing and magnitude of its peaks from data collected by a single inertial measurement unit (IMU) on one of the lower limb locations. Nine volunteers participated in this study, walking on a force plate-instrumented treadmill at various speeds. Four IMUs were worn on the foot, shank, distal thigh, and proximal thigh, respectively. A random forest model was employed to estimate the vGRF from data collected by each of the IMUs. We evaluated the performance of the models against the gold standard measurement of the vGRF generated by the treadmill. The developed model achieved a high accuracy with a correlation coefficient, root mean square error, and normalized root mean square error of 1.00, 0.02 body weight (BW), and 1.7% in intra-participant testing, and 0.97, 0.10 BW, and 7.15% in inter-participant testing, respectively, for the shank location. The difference between the reference and estimated passive force peak values was 0.02 BW and 0.14 BW with a delay of −0.14% and 0.57% of stance duration for the intra- and inter-participant testing, respectively; the difference between the reference and estimated active force peak values was 0.02 BW and 0.08 BW with a delay of 0.45% and 1.66% of stance duration for the intra- and inter-participant evaluation, respectively. We concluded that vertical ground reaction force can be estimated using only a single IMU via machine learning algorithms. This research sheds light on the development of a portable wearable gait monitoring system reporting the real-time vGRF in real-life scenarios. |
| first_indexed | 2024-03-10T17:57:46Z |
| format | Article |
| id | doaj.art-3ffc480d73134c67a743bb5edbf7ed92 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T17:57:46Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3ffc480d73134c67a743bb5edbf7ed922023-11-20T09:04:26ZengMDPI AGSensors1424-82202020-08-012015434510.3390/s20154345Estimating Vertical Ground Reaction Force during Walking Using a Single Inertial SensorXianta Jiang0Christopher Napier1Brett Hannigan2Janice J. Eng3Carlo Menon4Menrva Research Group, Schools of Mechatronic Systems & Engineering Science, Simon Fraser University, Metro Vancouver, BC V3T 0A3, CanadaMenrva Research Group, Schools of Mechatronic Systems & Engineering Science, Simon Fraser University, Metro Vancouver, BC V3T 0A3, CanadaMenrva Research Group, Schools of Mechatronic Systems & Engineering Science, Simon Fraser University, Metro Vancouver, BC V3T 0A3, CanadaDepartment of Physical Therapy, University of British Columbia, Vancouver, BC V6T 1Z3, CanadaMenrva Research Group, Schools of Mechatronic Systems & Engineering Science, Simon Fraser University, Metro Vancouver, BC V3T 0A3, CanadaThe vertical ground reaction force (vGRF) and its passive and active peaks are important gait parameters and of great relevance for musculoskeletal injury analysis and prevention, the detection of gait abnormities, and the evaluation of lower-extremity prostheses. Most currently available methods to estimate the vGRF require a force plate. However, in real-world scenarios, gait monitoring would not be limited to a laboratory setting. This paper reports a novel solution using machine learning algorithms to estimate the vGRF and the timing and magnitude of its peaks from data collected by a single inertial measurement unit (IMU) on one of the lower limb locations. Nine volunteers participated in this study, walking on a force plate-instrumented treadmill at various speeds. Four IMUs were worn on the foot, shank, distal thigh, and proximal thigh, respectively. A random forest model was employed to estimate the vGRF from data collected by each of the IMUs. We evaluated the performance of the models against the gold standard measurement of the vGRF generated by the treadmill. The developed model achieved a high accuracy with a correlation coefficient, root mean square error, and normalized root mean square error of 1.00, 0.02 body weight (BW), and 1.7% in intra-participant testing, and 0.97, 0.10 BW, and 7.15% in inter-participant testing, respectively, for the shank location. The difference between the reference and estimated passive force peak values was 0.02 BW and 0.14 BW with a delay of −0.14% and 0.57% of stance duration for the intra- and inter-participant testing, respectively; the difference between the reference and estimated active force peak values was 0.02 BW and 0.08 BW with a delay of 0.45% and 1.66% of stance duration for the intra- and inter-participant evaluation, respectively. We concluded that vertical ground reaction force can be estimated using only a single IMU via machine learning algorithms. This research sheds light on the development of a portable wearable gait monitoring system reporting the real-time vGRF in real-life scenarios.https://www.mdpi.com/1424-8220/20/15/4345IMUground reaction forcegait analysiswalking |
| spellingShingle | Xianta Jiang Christopher Napier Brett Hannigan Janice J. Eng Carlo Menon Estimating Vertical Ground Reaction Force during Walking Using a Single Inertial Sensor Sensors IMU ground reaction force gait analysis walking |
| title | Estimating Vertical Ground Reaction Force during Walking Using a Single Inertial Sensor |
| title_full | Estimating Vertical Ground Reaction Force during Walking Using a Single Inertial Sensor |
| title_fullStr | Estimating Vertical Ground Reaction Force during Walking Using a Single Inertial Sensor |
| title_full_unstemmed | Estimating Vertical Ground Reaction Force during Walking Using a Single Inertial Sensor |
| title_short | Estimating Vertical Ground Reaction Force during Walking Using a Single Inertial Sensor |
| title_sort | estimating vertical ground reaction force during walking using a single inertial sensor |
| topic | IMU ground reaction force gait analysis walking |
| url | https://www.mdpi.com/1424-8220/20/15/4345 |
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