As an important technical means of orthopedic diagnosis and treatment system, gait analysis equipment has been applied to the whole process management including disease assessment, surgical decision-making, and postoperative rehabilitation, which is specifically manifested in the following six aspects:
I. Diagnosis and pathological evaluation of orthopedic diseases
Gait analysis can accurately identify the characteristics of foot and ankle diseases by collecting kinematic parameters (such as joint angle and stride) and dynamic data (such as plantar pressure distribution).
For example, patients with high arches show concentrated heel pressure and insufficient forefoot support, while flat feet show abnormal dynamic trajectory with arch collapse and calcaneal valgus. In knee osteoarthritis, the equipment can quantitatively detect abnormal impact force during initial landing and torque imbalance at the end of the support phase, providing biomechanical basis for the mechanism of cartilage degeneration.
II. Optimization of surgical plan and effect evaluation
In unicompartmental knee arthroplasty (UKA), gait analysis can compare parameters such as gait symmetry and knee flexion angle before and after surgery, objectively evaluate prosthesis adaptability and joint function recovery, and has more dynamic monitoring advantages than traditional imaging evaluation.
Wearable devices can also capture the changes in cadence and stride length after high tibial osteotomy in real time, and assist in correcting surgical details such as osteotomy angle.
3. Dynamic guidance for rehabilitation treatment
For patients undergoing joint replacement, fracture fixation, etc., the device monitors the activation status of muscles such as quadriceps femoris through electromyographic sensors, and combines the spatiotemporal parameters in the gait cycle (such as the proportion of single-limb support time) to develop personalized rehabilitation plans.
Studies have shown that training based on real-time gait feedback can increase rehabilitation efficiency by 30%. For patients with foot and ankle deformity correction, dynamic pressure distribution data can guide the biomechanical adaptation and adjustment of orthopedic braces.
4. Pain management and complication prevention
Through animal model gait analysis, it was found that changes in parameters such as shortened grounding time and force attenuation of the affected limb after autologous bone transplantation can quantitatively evaluate the degree of pain, providing evaluation criteria for the development of sustained-release materials for local anesthetics.
In elderly orthopedic patients, the device can identify fall risk indicators such as increased gait variation coefficient at an early stage, and reduce the incidence of secondary fractures through gait retraining.
5. Technological innovation promotes clinical application
The new wearable device integrates multimodal sensing systems such as pressure sensors and inertial measurement units (IMUs) to achieve continuous gait monitoring in outpatient settings.
Compared with traditional laboratory equipment, its data acquisition frequency is increased to more than 200Hz, and the error rate is controlled within 5%, which significantly improves the convenience and data reliability of orthopedic bedside testing.
6. Special case management
In complex cases such as cerebral palsy secondary to clubfoot and abnormal gait after spinal cord injury, the three-dimensional gait analysis system can quantitatively evaluate muscle compensation patterns and provide cross-domain data support for orthopedic-rehabilitation multidisciplinary joint treatment.
For posture problems such as X-shaped legs and long and short legs, the equipment can accurately measure abnormal coronal joint torque and assist in formulating osteotomy or physical correction plans.
At present, gait analysis technology is developing in the direction of intelligence and miniaturization. Its application in orthopedics has evolved from a single diagnostic tool to a decision support system throughout the entire diagnosis and treatment cycle.
With the continuous improvement of sensor accuracy and the in-depth application of machine learning algorithms, predictive intervention and precision treatment of orthopedic diseases are expected to be achieved in the future.
