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. 2024 Sep 27:20:e00589.
doi: 10.1016/j.ohx.2024.e00589. eCollection 2024 Dec.

Design of a low-cost force insoles to estimate ground reaction forces during human gait

Nelson E Guevara  1   2   3 , Carlos F Rengifo  1   2 , Yamir H Bolaños  3 , Daniel A Fernández  1   2   3 , Wilson A Sierra  4 , Luis E Rodríguez  4
Affiliations

Design of a low-cost force insoles to estimate ground reaction forces during human gait

Nelson E Guevara et al. HardwareX. .

Abstract

This paper proposes a low-cost electronic system for estimating ground reaction forces (GRF) during human gait. The device consists of one master node and two slave nodes. The master node sends instructions to slave nodes that sample and store data from two force insoles located at the participant's feet. These insoles are equipped with 14 piezo-resistive FlexiForce A301 sensors (FSR). The slave nodes are attached to the ankles and feet of each participant. Subsequently, the start command is transmitted through the master node, which is connected to the USB port of a personal computer (PC). Once the walking session is completed, the information obtained by the slave nodes can be downloaded by accessing the access point generated by these devices through Wi-Fi communication. The GRF estimation system was validated with force platforms (BTS Bioengineering P6000, Italy), giving on average a fit measure equal to 68 . 71 % ± 4 . 80 % in dynamic situations. Future versions of this device are expected to increase this fit by using machine learning models.

Keywords: 3D printing; ESP32 microcontrollers; Force insoles; Ground reaction forces; Low-cost; Piezo-resistive sensors.

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Figures

None
Graphical abstract
Fig. 1
Fig. 1
General scheme of a GRFMS.
Fig. 2
Fig. 2
FlexiForce A301 sensor.
Fig. 3
Fig. 3
3D shoe design. (a) Insole design and (b) coatings design.
Fig. 4
Fig. 4
Conditioning circuit.
Fig. 5
Fig. 5
Control circuit.
Fig. 6
Fig. 6
Print circuit boards. (a) Control PCB and (b) Conditioning PCB.
Fig. 7
Fig. 7
Distribution of sensors.
Fig. 8
Fig. 8
Recommended circuit for FlexiForce sensor. The operational amplifier is a MCP6004 of Microchip™.
Fig. 9
Fig. 9
Shimadzu press.
Fig. 10
Fig. 10
Statistical results. (a) FSR0’s exponential regression, (b) FSR9’s exponential regression, (c) FSR10’s exponential regression, (d) FSR13’s exponential regression and (e) Linear and Exponencial models.
Fig. 11
Fig. 11
Electronics components.
Fig. 12
Fig. 12
Sensor Connections. (a) Sensor attachment, (b) Connection of the sensors to the conditioning PCB, (c) Labels on conditioning PCB and (d) Connection between Control PCB and Conditioning PCB.
Fig. 13
Fig. 13
Components of a slave node without coating.
Fig. 14
Fig. 14
(a) Programming control PCB and (b) Coating and insole.
Fig. 15
Fig. 15
GRFMS prototype. (a) Lateral view and (b) Frontal view.
Fig. 16
Fig. 16
GRFMS configuration. (a) Turn on slave node, (b) Connect node master to USB port, (c) Generated Wi-Fi network and (d) Enter IP address.
Fig. 17
Fig. 17
Serial communication parameters.
Fig. 18
Fig. 18
Web interface. (a) Home section and (b) Download section.
Fig. 19
Fig. 19
MATLAB desktop application.
Fig. 20
Fig. 20
Motion analysis laboratory. Escuela Colombiana de Ingeniería Julio Garavito.
Fig. 21
Fig. 21
Subject 1 equipped with force insoles.
Fig. 22
Fig. 22
Distribution of platforms of force in the laboratory. (a) Diagram of force platform distribution and (b) Force platforms.
Fig. 23
Fig. 23
GRF of participants 1, 2, 3 and 4.
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