1. Introduction

2. Literature Survey

• Studies emphasize the importance of proactive foot health monitoring in various populations, including athletes, individuals with diabetes, and the elderly, highlighting the significance of early detection and prevention of foot-related complications.
• Research on Force-Sensitive Resistor (FSR) sensors and Inertial Measurement Unit (IMU) sensors provides insights into their capabilities in measuring foot pressure distribution and capturing foot movement, orientation, and gait patterns.
• Additionally, investigations into wearable technology in healthcare underscore the potential for integrating wearable sensors into rehabilitation, diagnostics, and chronic disease management.

3. Methodology

3.2. Sensor Selection

Thoroughly evaluating various Force-Sensitive Resistor (FSR) and Inertial Measurement Unit (IMU) sensors based on technical specifications, accuracy, and suitability for foot health monitoring, ensuring optimal sensor performance.

Force Sensitive Resistors (FSRs) are sensors that detect changes in resistance in response to applied force or pressure. They consist of a conductive polymer material whose resistance decreases as force is applied to its surface. Initially, the conductive particles within the polymer are separated, resulting in high resistance. However, when pressure is exerted on the FSR, the particles come into closer contact, creating more conductive pathways, and causing a decrease in resistance. This change in resistance is proportional to the magnitude of the applied force, allowing FSRs to provide continuous feedback on pressure levels. This working principle enables FSRs to be used in various applications such as touch sensing, force measurement, and pressure mapping, including their integration into the Smart Insole prototype for monitoring foot biomechanics and gait analysis.

An Inertial Measurement Unit (IMU) functions as a sensor system capable of measuring and reporting accelerations, angular velocities, and sometimes magnetic fields, typically using a combination of accelerometers, gyroscopes, and magnetometers. Accelerometers detect changes in linear acceleration along multiple axes, gyroscopes measure rotational speed around those axes, and magnetometers track changes in magnetic field strength. These sensors work together to provide comprehensive motion sensing capabilities, enabling IMUs to track orientation, velocity, and gravitational forces in three-dimensional space. IMUs are widely used in various applications, including navigation systems, robotics, virtual reality, and motion tracking. In the Smart Insole project, the ADXL345 accelerometer, an integral component of the IMU, is employed to monitor foot orientation and movement dynamics, contributing to the analysis of gait patterns and foot biomechanics.

Figure 1. Force Sensitive Resistor (FSR).

Figure 1. Force Sensitive Resistor (FSR).

Figure 2. Inertial Measurement Unit (IMU).

Figure 2. Inertial Measurement Unit (IMU).

4. Development and Execution

4.1. Analyzing the Gait Cycle

The gait cycle, also known as the walking or locomotion cycle, encompasses the sequence of movements involved in human walking. It consists of two main phases: the stance phase and the swing phase. During the stance phase, the foot makes contact with the ground, beginning with the heel-strike as the initial point of contact, followed by the midstance where the foot bears the body’s weight, and concluding with the toe-off as the foot pushes off from the ground to initiate the swing phase. The swing phase involves the leg swinging forward to advance the body’s position, characterized by the swing-through motion of the foot until the next heel-strike occurs.

Figure 3. Gait Cycle.

Figure 3. Gait Cycle.

4.2. Integration of FSRs:

The integration of Force Sensitive Resistors (FSRs) within the Smart Insole prototype is strategically designed to capture pressure distribution across key areas of the foot during various phases of the gait cycle. With one FSR positioned at the heel, one at the midfoot, and one at the forefoot (toes), the system aims to provide comprehensive insight into foot biomechanics and pressure dynamics.

Figure 4. Integration of FSRs.

Figure 4. Integration of FSRs.

Placing an FSR at the heel allows for the detection of initial ground contact during the heel-strike phase of the gait cycle. This sensor captures the distribution of pressure as weight is transferred from the heel to the midfoot and forefoot during the subsequent phases of stance and propulsion. The FSR at the midfoot provides additional data on pressure distribution as the foot rolls forward and redistributes weight from the heel to the forefoot. Finally, positioning an FSR at the forefoot, near the toes, enables the measurement of pressure exerted during toe-off, the final phase of the gait cycle where the foot pushes off from the ground.

By integrating FSRs at these strategic locations, the Smart Insole prototype can effectively monitor pressure variations across the entire foot, offering valuable insights into gait dynamics, foot loading patterns, and potential areas of imbalance or discomfort. This comprehensive pressure mapping capability enhances the utility of the Smart Insole for applications in healthcare, sports performance analysis, and rehabilitation, enabling personalized monitoring and optimization of foot biomechanics.

4.3. Circuit Development:

The development of the circuit for integrating three Force Sensitive Resistors (FSRs), three 10k ohm resistors, and an Arduino Uno R3 microcontroller unit (MCU) is a pivotal step in creating the Smart Insole prototype for monitoring foot biomechanics. The circuit design aims to accurately measure pressure distribution across the foot and facilitate data acquisition for subsequent analysis.

The circuit configuration employs a voltage divider setup for each FSR, comprising the FSR and a 10k ohm resistor connected in series between the supply voltage (VCC) and ground. The voltage output from the junction between the FSR and resistor is proportional to the resistance of the FSR, which varies with applied pressure. This varying voltage is then fed into the analog input pins of the Arduino Uno R3 for analog-to-digital conversion.

Figure 5. Schematic View of Circuit.

Figure 5. Schematic View of Circuit.

Through this arrangement, each FSR’s pressure-dependent resistance is converted into a corresponding analog voltage signal, which the Arduino Uno R3 reads and processes. The MCU interprets these voltage signals and translates them into meaningful pressure readings, allowing for real-time monitoring of foot pressure distribution.

5. Integration with Arduino Ide

5.1. Establishing FSR Connectivity Using Arduino IDE

Integrating Force Sensitive Resistors (FSRs) with Arduino IDE is a crucial step in the development of the Smart Insole prototype, facilitating real-time monitoring of foot pressure distribution. The process begins with configuring the Arduino Uno R3 microcontroller unit (MCU) to interface with the FSRs and acquire analog sensor data.

In the Arduino IDE, a dedicated sketch is developed to initialize analog input pins corresponding to the FSR connections. Each FSR is connected to a specific analog input pin, along with a 10k ohm resistor forming a voltage divider circuit. This configuration ensures accurate measurement of voltage variations across the FSRs in response to applied pressure.

Figure 6. IDE Code for FSR Connectivity.

Figure 6. IDE Code for FSR Connectivity.

These calibrated pressure readings are then processed and transmitted for visualization and analysis, laying the foundation for comprehensive monitoring of foot biomechanics using the Smart Insole prototype.

5.2. Linking ADXL345 Via Arduino IDE

In the Arduino IDE, a dedicated sketch is developed to initialize the communication interface and configure the necessary registers within the ADXL345 sensor module. This includes setting up parameters such as data rate, measurement range, and power modes to optimize sensor performance for the specific application requirements.

The Arduino IDE sketch further includes routines for reading accelerometer data from the ADXL345 sensor module, parsing the raw sensor readings, and converting them into meaningful units such as acceleration along three axes (x, y, z). These calibrated acceleration values are then processed and utilized for monitoring foot orientation, detecting movement patterns, and analyzing gait dynamics within the Smart Insole prototype.

Figure 7. IDE Code for ADXL345 Connectivity.

Figure 7. IDE Code for ADXL345 Connectivity.

Through the seamless integration of ADXL345 connectivity within the Arduino IDE environment, the Smart Insole prototype becomes a powerful tool for assessing foot biomechanics, enhancing sports performance analysis, and facilitating rehabilitation practices. This linkage between the ADXL345 accelerometer and Arduino IDE forms the backbone of the Smart Insole’s sensor system, enabling comprehensive monitoring and analysis of human movement.

6. Results

6.1. Results on Arduino Serial Monitor for FSRs

Upon integration with the Arduino Uno R3 microcontroller unit (MCU) and configuration within the Arduino IDE, the Force Sensitive Resistors (FSRs) provide real-time data output on the Arduino Serial Monitor. As pressure is applied to the FSRs embedded within the Smart Insole prototype, corresponding analog voltage values are captured and transmitted via the analog input pins of the Arduino Uno R3. These voltage values are converted into digital readings using the built-in analog-to-digital converter (ADC) of the MCU.

Figure 8. Serial Monitor Readings of FSRs.

Figure 8. Serial Monitor Readings of FSRs.

On the Arduino Serial Monitor, users can observe the continuous stream of pressure readings from each FSR, providing insights into foot pressure distribution during various phases of the gait cycle. Through this visualization, users can monitor changes in pressure patterns, detect anomalies, and assess foot biomechanics in real time, enhancing the utility of the Smart Insole for gait analysis and performance optimization.

6.2. Results on Arduino Serial Monitor for ADXL345:

The integration of the ADXL345 accelerometer with the Arduino Uno R3 MCU enables real-time data output on the Arduino Serial Monitor. The accelerometer continuously measures acceleration along three axes (x, y, z) and transmits raw sensor readings to the MCU via the I2C or SPI communication protocol. On the Arduino Serial Monitor, users can observe the real-time acceleration values corresponding to each axis, providing insights into foot orientation and movement dynamics. Changes in acceleration readings reflect variations in foot position, velocity, and acceleration during the gait cycle. By visualizing these data on the Arduino Serial Monitor, users can monitor foot movement patterns, detect deviations from normal gait, and assess balance and stability in real time.

Figure 9. Serial Monitor Readings of AXDL345.

Figure 9. Serial Monitor Readings of AXDL345.

Through the seamless integration of ADXL345 connectivity within the Arduino IDE environment, the Smart Insole prototype becomes a powerful tool for assessing foot biomechanics, enhancing sports performance analysis, and facilitating rehabilitation practices. This linkage between the ADXL345 accelerometer and Arduino IDE forms the backbone of the Smart Insole’s sensor system, enabling comprehensive monitoring and analysis of human movement.

7. Result Visualization Using Processing Application

7.1. Visualizing FSR Readings Via Processing

For Force Sensitive Resistors (FSRs), the Processing application generated visual pressure profiles, providing intuitive representations of pressure distribution across the foot. Areas of high pressure, such as the heel or forefoot, were depicted with distinct colors or intensity gradients, enabling users to identify pressure hotspots and assess foot loading patterns during various phases of the gait cycle. The visualization was obtained for different gait patterns such as:

• No pressure moment
• Heel strike
• Toe off
• Stance phase
• Flat feet

Figure 10. No Pressure Moment.

Figure 10. No Pressure Moment.

Figure 11. Heel Strike.

Figure 11. Heel Strike.

Figure 12. Stance Phase.

Figure 12. Stance Phase.

Figure 13. Toe Off.

Figure 13. Toe Off.

Figure 14. Flat Foot Detected.

Figure 14. Flat Foot Detected.

7.2. Visualizing FSR Readings Via Processing

For the ADXL345 accelerometer, the Processing application visualized foot orientation and movement dynamics using graphical representations such as line plots or 3D animations. Changes in acceleration along different axes were depicted in real time, allowing users to monitor foot movement patterns, detect deviations from normal gait, and assess balance and stability.

Figure 15. ADXL345 at idle condition.

Figure 15. ADXL345 at idle condition.

Figure 16. ADXL345 at operating condition.

Figure 16. ADXL345 at operating condition.

8. Annexure

Figure 17. Actual Mock-up of Smart Insole Using FSR and ADXL345.

Figure 17. Actual Mock-up of Smart Insole Using FSR and ADXL345.

9. Conclusion and Future Scope

References

1. Tao, J. , Dong, M., Li, L. et al. Real-time pressure mapping smart insole system based on a controllable vertical pore dielectric layer. Microsyst Nanoeng 2020, 6, 62. [Google Scholar] [CrossRef] [PubMed]
2. Mi Zhou, Wenbin Jiang, Jinfeng Wang, Design and manufacture of intelligent fabric-based insoles for disease prevention by monitoring plantar pressure. Materials Today Communications 2023, 37, 107646. [CrossRef]
3. Yin He, Meixia Lin, Xiaoyun Wang, Kexin Liu, Hao Liu, Tianhong He, Peng Zhou, Textile-film sensors for a comfortable intelligent pressure-sensing insole. Measurement 2021, 184, 109943. [CrossRef]
4. Maryam Hajizadeh, Allison L. Clouthier, Marshall Kendall, Ryan B. Graham, Predicting vertical and shear ground reaction forces during walking and jogging using wearable plantar pressure insoles. Gait & Posture. 2023, 104, 90–96. [CrossRef]
5. Katie E. Chatwin, Caroline A. Abbott, Satyan M. Rajbhandari, Prabhav N. Reddy, Frank L. Bowling, Andrew J.M. Boulton, Neil D. Reeves, An intelligent insole system with personalised digital feedback reduces foot pressures during daily life: An 18-month randomised controlled trial. Diabetes Research and Clinical Practice 2021, 181, 109091. [CrossRef]
6. Soumya K. Manna, M.A. Hannan Bin Azhar, Ann Greace, Optimal locations and computational frameworks of FSR and IMU sensors for measuring gait abnormalities. Heliyon 2023, 9, e15210. [CrossRef]
7. Qi Zhang, Yu Lu Wang, Yun Xia, Xuee Wu, Timothy Vernon Kirk, Xiao Dong Chen, A low-cost and highly integrated sensing insole for plantar pressure measurement. Sensing and Bio-Sensing Research 2019, 26, 100298. [CrossRef]
8. Ibrahim Almuteb, Rui Hua, Ya Wang, Smart insoles review (2008-2021): Applications, potentials, and future. Smart Health 2022, 25, 100301. [CrossRef]
9. Paixão, L.M. , de Morais, M.E., Bublitz, F.M., Bezerra, K.C.T., Franco, C.I.F. (2022). The Use of Smart Insoles for Gait Analysis: A Systematic Review. In: Machado, J., Soares, F., Trojanowska, J., Ottaviano, E. (eds) Innovations in Mechanical Engineering. icieng 2021. Lecture Notes in Mechanical Engineering. Springer, Cham. [CrossRef]