The significance and principle of gait measurement by plantar pressure distribution system
I. Significance of detection
Medical diagnosis and disease prevention
By analyzing the plantar pressure distribution, structural abnormalities such as flat feet and high arches can be identified, and the risk of diseases such as plantar fasciitis and diabetic foot ulcers can be assessed.
Dynamic gait data can reveal gait asymmetry or compensatory movements (such as inversion and inversion of the foot), and assist in early intervention of chain injuries such as scoliosis and knee degenerative lesions.
Sports performance optimization and injury prevention
Athletes can optimize landing methods (such as adjusting the forefoot or heel landing ratio) through dynamic pressure data to reduce sports injuries such as Achilles tendonitis and stress fractures.
Combined with parameters such as step frequency and stride length, develop personalized training plans to improve exercise efficiency and reduce joint load.
Rehabilitation therapy and orthopedic support
Provide rehabilitation progress assessment for patients with postoperative or chronic pain, such as adjusting gait training programs through pressure center offset data. Customized 3D printed insoles or orthopedic shoes can disperse the pressure of high-pressure areas (such as metatarsal heads) in a targeted manner to improve foot function.
Health management and product design
Fall risk warning for the elderly: evaluate balance ability through gait stability analysis (such as pressure center fluctuation amplitude).
Guide footwear companies to design functional shoes that conform to biomechanics, such as arch support or cushioning structure optimization.
2. Technical principles
Static and dynamic data acquisition
Static measurement: When the subject is standing, the pressure sensor array (such as embedded gait pads or insoles) records the pressure distribution in various areas of the sole of the foot to evaluate the arch shape and load-bearing balance.
Dynamic measurement: During walking or running, the sensor captures the dynamic changes of pressure over time (such as the movement trajectory of the pressure center) in real time and analyzes the mechanical characteristics of the gait cycle (support phase, swing phase).
Multi-dimensional parameter analysis
Pressure distribution: Generate a heat map to display the high-pressure area (such as the heel and forefoot) and low-pressure area (such as the arch) of the sole to determine abnormal foot structure or force imbalance.
Time integration and dynamic parameters: Calculate the pressure peak, impulse (integral of pressure and time) and ground reaction force to evaluate gait stability and impact load.
Data integration and visualization
Combined with a three-dimensional motion capture system or inertial sensor, synchronously obtain kinematic parameters such as joint angle, stride, and step frequency to build a multi-dimensional gait model.
Generate a visual report (such as a pressure curve chart and a gait cycle decomposition chart) through professional software to provide a quantitative decision-making basis for clinical or sports scenarios.
III. Core equipment and process
Hardware composition
Pressure sensing device: A high-density sensor array (such as piezoresistive or capacitive) is integrated into a gait mat, treadmill or smart insole, supporting high-frequency sampling (such as 100 frames per second).
Auxiliary equipment: Ground reaction force plate and inertial measurement unit (IMU) are used to synchronously collect dynamic and kinematic data.
Test process
Standardized test: The subject walks/runs at a specified speed, and the equipment records the pressure and motion data of the complete gait cycle.
Data analysis: Extract key parameters (such as arch index, pressure center trajectory) through algorithms, and output evaluation conclusions based on clinical experience or sports medicine standards.
The plantar pressure distribution system reveals gait abnormalities and injury risks from a biomechanical perspective through static and dynamic data fusion analysis.
Its core value lies in providing quantitative basis for medical diagnosis, sports optimization and health management. The technology relies on high-precision sensors and multi-dimensional data processing, and will further develop in the direction of portability and intelligence in the future.
