Design of a low-cost force insoles to estimate ground reaction forces during human gait
Nelson E. Guevara,
Carlos F. Rengifo,
Yamir H. Bolaños,
Daniel A. Fernández,
Wilson A. Sierra,
Luis E. Rodríguez
Affiliations
Nelson E. Guevara
Research Group of Automation, Universidad del Cauca, Colombia; Department of Electronic, Instrumentation and Control, Universidad del Cauca, Colombia; Faculty of Engineering and Natural Science, Corporación Universitaria Autónoma del Cauca, Colombia; Corresponding author at: Department of Electronic, Instrumentation and Control, Universidad del Cauca, Colombia.
Carlos F. Rengifo
Research Group of Automation, Universidad del Cauca, Colombia; Department of Electronic, Instrumentation and Control, Universidad del Cauca, Colombia
Yamir H. Bolaños
Faculty of Engineering and Natural Science, Corporación Universitaria Autónoma del Cauca, Colombia
Daniel A. Fernández
Research Group of Automation, Universidad del Cauca, Colombia; Department of Electronic, Instrumentation and Control, Universidad del Cauca, Colombia; Faculty of Engineering and Natural Science, Corporación Universitaria Autónoma del Cauca, Colombia
Wilson A. Sierra
Faculty of Biomedical Engineering, Escuela Colombiana de Ingeniería Julio Garavito, Colombia
Luis E. Rodríguez
Faculty of Biomedical Engineering, Escuela Colombiana de Ingeniería Julio Garavito, Colombia
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.
