Design of an Artificial Neural Network Algorithm for a Low-Cost Insole Sensor to Estimate the Ground Reaction Force (GRF) and Calibrate the Center of Pressure (CoP)
As an alternative to high-cost shoe insole pressure sensors that measure the insole pressure distribution and calculate the center of pressure (CoP), researchers developed a foot sensor with FSR sensors on the bottom of the insole. However, the calculations for the center of pressure and ground reac...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/18/12/4349 |
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| author | Ho Seon Choi Chang Hee Lee Myounghoon Shim Jong In Han Yoon Su Baek |
| author_facet | Ho Seon Choi Chang Hee Lee Myounghoon Shim Jong In Han Yoon Su Baek |
| author_sort | Ho Seon Choi |
| collection | DOAJ |
| description | As an alternative to high-cost shoe insole pressure sensors that measure the insole pressure distribution and calculate the center of pressure (CoP), researchers developed a foot sensor with FSR sensors on the bottom of the insole. However, the calculations for the center of pressure and ground reaction force (GRF) were not sufficiently accurate because of the fundamental limitations, fixed coordinates and narrow sensing areas, which cannot cover the whole insole. To address these issues, in this paper, we describe an algorithm of virtual forces and corresponding coordinates with an artificial neural network (ANN) for low-cost flexible insole pressure measurement sensors. The proposed algorithm estimates the magnitude of the GRF and the location of the foot plantar CoP. To compose the algorithm, we divided the insole area into six areas and created six virtual forces and the corresponding coordinates. We used the ANN algorithm with the input of magnitudes of FSR sensors, 1st and 2nd derivatives of them to estimate the virtual forces and coordinates. Eight healthy males were selected for data acquisition. They performed an experiment composed of the following motions: standing with weight shifting, walking with 1 km/h and 2 km/h, squatting and getting up from a sitting position to a standing position. The ANN for estimating virtual forces and corresponding coordinates was fitted according to those data, converted to c script, and downloaded to a microcontroller for validation experiments in real time. The results showed an average RMSE the whole experiment of 31.154 N for GRF estimation and 8.07 mm for CoP calibration. The correlation coefficients of the algorithm were 0.94 for GRF, 0.92 and 0.76 for the X and Y coordinate respectively. |
| first_indexed | 2024-04-11T22:52:02Z |
| format | Article |
| id | doaj.art-ab9fbb9cbddb472d88ee2c3d5c871974 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:52:02Z |
| publishDate | 2018-12-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-ab9fbb9cbddb472d88ee2c3d5c8719742022-12-22T03:58:33ZengMDPI AGSensors1424-82202018-12-011812434910.3390/s18124349s18124349Design of an Artificial Neural Network Algorithm for a Low-Cost Insole Sensor to Estimate the Ground Reaction Force (GRF) and Calibrate the Center of Pressure (CoP)Ho Seon Choi0Chang Hee Lee1Myounghoon Shim2Jong In Han3Yoon Su Baek4Motion Control Laboratory, School of Mechanical Engineering, Yonsei University, Seoul 03722, KoreaMotion Control Laboratory, School of Mechanical Engineering, Yonsei University, Seoul 03722, KoreaMotion Control Laboratory, School of Mechanical Engineering, Yonsei University, Seoul 03722, KoreaMotion Control Laboratory, School of Mechanical Engineering, Yonsei University, Seoul 03722, KoreaMotion Control Laboratory, School of Mechanical Engineering, Yonsei University, Seoul 03722, KoreaAs an alternative to high-cost shoe insole pressure sensors that measure the insole pressure distribution and calculate the center of pressure (CoP), researchers developed a foot sensor with FSR sensors on the bottom of the insole. However, the calculations for the center of pressure and ground reaction force (GRF) were not sufficiently accurate because of the fundamental limitations, fixed coordinates and narrow sensing areas, which cannot cover the whole insole. To address these issues, in this paper, we describe an algorithm of virtual forces and corresponding coordinates with an artificial neural network (ANN) for low-cost flexible insole pressure measurement sensors. The proposed algorithm estimates the magnitude of the GRF and the location of the foot plantar CoP. To compose the algorithm, we divided the insole area into six areas and created six virtual forces and the corresponding coordinates. We used the ANN algorithm with the input of magnitudes of FSR sensors, 1st and 2nd derivatives of them to estimate the virtual forces and coordinates. Eight healthy males were selected for data acquisition. They performed an experiment composed of the following motions: standing with weight shifting, walking with 1 km/h and 2 km/h, squatting and getting up from a sitting position to a standing position. The ANN for estimating virtual forces and corresponding coordinates was fitted according to those data, converted to c script, and downloaded to a microcontroller for validation experiments in real time. The results showed an average RMSE the whole experiment of 31.154 N for GRF estimation and 8.07 mm for CoP calibration. The correlation coefficients of the algorithm were 0.94 for GRF, 0.92 and 0.76 for the X and Y coordinate respectively.https://www.mdpi.com/1424-8220/18/12/4349center of pressure (CoP)force sensing resistor (FSR)ground reaction force (GRF)artificial neural network (ANN) |
| spellingShingle | Ho Seon Choi Chang Hee Lee Myounghoon Shim Jong In Han Yoon Su Baek Design of an Artificial Neural Network Algorithm for a Low-Cost Insole Sensor to Estimate the Ground Reaction Force (GRF) and Calibrate the Center of Pressure (CoP) Sensors center of pressure (CoP) force sensing resistor (FSR) ground reaction force (GRF) artificial neural network (ANN) |
| title | Design of an Artificial Neural Network Algorithm for a Low-Cost Insole Sensor to Estimate the Ground Reaction Force (GRF) and Calibrate the Center of Pressure (CoP) |
| title_full | Design of an Artificial Neural Network Algorithm for a Low-Cost Insole Sensor to Estimate the Ground Reaction Force (GRF) and Calibrate the Center of Pressure (CoP) |
| title_fullStr | Design of an Artificial Neural Network Algorithm for a Low-Cost Insole Sensor to Estimate the Ground Reaction Force (GRF) and Calibrate the Center of Pressure (CoP) |
| title_full_unstemmed | Design of an Artificial Neural Network Algorithm for a Low-Cost Insole Sensor to Estimate the Ground Reaction Force (GRF) and Calibrate the Center of Pressure (CoP) |
| title_short | Design of an Artificial Neural Network Algorithm for a Low-Cost Insole Sensor to Estimate the Ground Reaction Force (GRF) and Calibrate the Center of Pressure (CoP) |
| title_sort | design of an artificial neural network algorithm for a low cost insole sensor to estimate the ground reaction force grf and calibrate the center of pressure cop |
| topic | center of pressure (CoP) force sensing resistor (FSR) ground reaction force (GRF) artificial neural network (ANN) |
| url | https://www.mdpi.com/1424-8220/18/12/4349 |
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