The primary objectives of this project are to employ a comprehensive approach to the exploration of gait dynamics in individuals with osteoarthritis, focusing on the integration of three distinct sensors—IMU with 9-axis capabilities, FSR, and EMG. The IMU sensor will capture intricate details of joint movements, accelerations, and orientations during walking, providing a holistic understanding of gait dynamics. Concurrently, FSR sensors will offer valuable insights into the distribution of forces exerted on the feet during each step, aiding in the analysis of gait mechanics. Furthermore, the EMG sensors will measure muscle activation patterns, contributing essential information about the neuromuscular aspects of gait. Through the integration of these sensors, this project aims to enhance the precision and depth of gait analysis for individuals with osteoarthritis, potentially leading to a more comprehensive understanding of the condition's impact on movement patterns and offering valuable insights for therapeutic interventions and rehabilitation strategies.
2.1 Selection Criteria for the Sensors
The selection criteria for the IMU, FSR, and EMG sensors were based on their appropriateness for gait analysis in individuals with osteoarthritis. For the 9-axis IMU sensors, factors such as high precision and a wide measurement range, ensuring accurate capture of joint angles and motion dynamics during walking were prioritized. The selected IMU sensors offer a measurement range of ±2000 degrees per second for gyroscope data and ±16 g for accelerometer data, with a sampling rate of 1000 Hz. FSR sensors were selected based on sensitivity and durability, capable of measuring variable forces exerted during gait. The selected FSR sensors have a measurement range of 0 to 20 pounds and provide data at a sampling rate of 100 Hz. EMG sensors were chosen for their capacity to accurately record muscle activation patterns, considering factors like signal-to-noise ratio, ease of application, and compatibility with simultaneous use alongside IMU and FSR sensors. The chosen EMG sensors have a frequency response of 20-500 Hz, ensuring accurate representation of muscle activation patterns. Calibration procedures for all sensors involve standardizing baseline measurements and ensuring synchronization before each gait analysis session to maintain data accuracy and consistency across participants.
2.2 Hardware Description
2.2.1 9-axis IMU Sensor:
The 9-axis IMU sensor is a compact device incorporating accelerometers, gyroscopes, and magnetometers. It typically features a small form factor for easy attachment to the body, with tri axial accelerometers providing acceleration data, tri axial gyroscopes measuring angular velocity, and tri axial magnetometers capturing magnetic field information. The sensor is equipped with a microcontroller for onboard processing and communication capabilities.
2.2.2 FSR Sensor:
The Force-Sensitive Resistor (FSR) is a pressure-sensitive sensor designed to measure variations in force applied to its surface. These flexible sensors consist of conductive polymer material, and their resistance changes proportionally with the applied force. FSR sensors used in the project are embedded in insoles or attached to specific regions of the participant's footwear. They are connected to microcontrollers for data acquisition and transmission.
2.2.3 EMG Sensor:
The Electromyography (EMG) sensor is designed to measure electrical activity produced by muscles during contraction. Typically, EMG sensors feature surface electrodes that adhere to the skin above specific muscle groups. These electrodes pick up electrical signals generated by muscle cells. The EMG sensors are connected to amplifiers and signal conditioning circuits to capture and process muscle activity data. Microcontrollers facilitate data transmission to the central processing unit.
2.3 Hardware configuration
In the gait analysis setup, the placement of sensors on the subject is crucial for accurate data capture. The 9-axis IMU sensors are strategically positioned on anatomical landmarks, such as the lower limbs and pelvis, using specialized straps or adhesive patches. These IMU sensors are affixed to the shins, thighs, and lower back to capture precise three-dimensional motion data during walking. Force-sensitive resistor (FSR) sensors are embedded within insoles or attached to specific regions of the subject's footwear, allowing them to measure the distribution of ground reaction forces. Additionally, electromyography (EMG) sensors are strategically placed over relevant muscle groups, often on the quadriceps, hamstrings, and calf muscles, to record muscle activation patterns during gait.
2.4 Data collection
The data collection procedure involves several systematic steps to ensure the comprehensive capture of biomechanical parameters associated with osteoarthritis. Participants, after providing informed consent, are outfitted with the sensors. Before each session, a thorough calibration process is implemented to standardize baseline measurements and synchronize sensor data. Participants are then guided through a series of walking trials on a designated pathway. The data acquisition system, comprising microcontrollers connected to each sensor, collects real-time information during these trials. These microcontrollers serve as the interface between the sensors and the data processing unit. Participants are instructed to walk at various speeds and, in some cases, perform specific tasks to simulate daily activities. Throughout the gait analysis session, participant’s joint angles, accelerations, angular velocities, ground reaction forces, and muscle activation patterns are continuously recorded. Care is taken to ensure that the participant’s natural gait patterns are captured, minimizing any potential alterations due to conscious adjustments. To enhance accuracy, multiple trials are conducted for each participant. The collected data is subsequently transmitted to a central processing unit for synchronization, aggregation, and storage. Post-session, participants may undergo a debriefing to address any concerns or queries. This detailed data collection procedure aims to provide a robust dataset for the exploration and analysis of gait dynamics, offering valuable insights into the biomechanical aspects of osteoarthritis and aiding in the development of effective diagnostic and rehabilitative strategies.
2.5 Gait analysis approach
The project aims to measure a comprehensive set of biomechanical gait parameters using IMU, FSR, and EMG sensors. The walking conditions during the gait analysis sessions involve participants engaging in both normal walking and variations of walking speeds to simulate real-world scenarios. Participants are instructed to walk at comfortable, slow, and fast speeds to capture the dynamic changes in gait parameters associated with different walking intensities. In addition to varying speeds, participants may be asked to perform additional tasks during the gait analysis, such as walking on inclines or uneven surfaces, to assess the adaptability and stability of their gait in challenging conditions. These tasks aim to mimic daily activities and provide a more comprehensive evaluation of gait dynamics in individuals with osteoarthritis. The inclusion of diverse walking conditions and additional tasks enhances the ecological validity of the gait analysis, ensuring that the obtained data reflects a broad spectrum of real-world scenarios and contributes to a more nuanced understanding of the impact of osteoarthritis on gait.
2.6 Sensor fusion
The data from the 9-axis IMU sensor will undergo a multi-step processing and fusion procedure to extract comprehensive information about motion in three dimensions. Initially, raw accelerometer, gyroscope, and magnetometer data will be collected at a high sampling rate during gait analysis sessions. Calibration procedures will be implemented to correct for sensor biases and ensure accurate measurements. Subsequently, sensor fusion algorithms, such as sensor fusion filters (e.g., complementary filter, Kalman filter), will be applied to integrate the individual data streams from the accelerometer, gyroscope, and magnetometer. This fusion process enhances the accuracy and robustness of the motion data, compensating for the inherent strengths and weaknesses of each sensor modality. The integrated data will then be transformed into meaningful kinematic parameters, including joint angles, angular velocities, and accelerations in three dimensions. Special attention will be given to mitigating sensor drift and noise through the fusion process, ensuring that the obtained motion information accurately represents the intricate dynamics of lower limb movements during walking. This comprehensive and integrated dataset from the 9-axis IMU sensor will serve as a foundation for analyzing gait dynamics in individuals with osteoarthritis, providing valuable insights into biomechanical alterations and facilitating a more nuanced understanding of the impact of this condition on motion in three dimensions.
2.7 Data integration
The data integration process involves combining information from the IMU, FSR, and EMG sensors to achieve a comprehensive analysis of gait dynamics. Initially, the synchronized data streams from each sensor modality, encompassing joint angles from the 9-axis IMU, ground reaction forces from the FSR sensors, and muscle activation patterns from the EMG sensors, will be collected during gait analysis sessions. To ensure temporal alignment, timestamps associated with each data point will be cross-referenced. The integration process will involve merging these diverse datasets into a unified representation of the participant's gait cycle. Algorithms and methodologies for sensor fusion will be employed to harmonize the temporal and spatial dimensions of the data, providing a holistic view of biomechanical interactions during walking. The integrated dataset will enable the exploration of relationships between joint kinematics, ground reaction forces, and muscle activity, unveiling intricate details about gait dynamics in individuals with osteoarthritis. Through this comprehensive analysis, the project aims to contribute valuable insights into the multifaceted aspects of gait alterations associated with osteoarthritis and inform the development of targeted diagnostic and rehabilitative strategies.