研究目的
To develop a wearable inertial measurement system and its associated spatiotemporal gait analysis algorithm for obtaining quantitative measurements and exploring clinical indicators from the spatiotemporal gait patterns for patients with stroke or Parkinson’s disease.
研究成果
The proposed wearable inertial measurement system and its associated spatiotemporal gait analysis algorithm effectively extracted gait parameters for patients with stroke or Parkinson’s disease. The system demonstrated potential as a tool for monitoring therapeutic efficacy and diagnosing gait disorders in these patients.
研究不足
The study was limited to analyzing gait patterns in the sagittal plane of the ankle joint. Future studies could extend this to include knee and hip angles for a more comprehensive evaluation of gait pathology in coronal and horizontal planes.
1:Experimental Design and Method Selection:
The study involved the development of a wearable inertial measurement system and a spatiotemporal gait analysis algorithm. The algorithm includes procedures for inertial signal acquisition, signal preprocessing, gait phase detection, and ankle range of motion estimation. A complementary filter was used to integrate accelerations and angular velocities to reduce integration errors.
2:Sample Selection and Data Sources:
24 participants, including stroke patients, Parkinson’s disease patients, and healthy controls, were recruited. They walked along a straight line of 10 m at normal speed while wearing the inertial measurement system.
3:List of Experimental Equipment and Materials:
The wearable system included a microcontroller (ATmega 328), a triaxial accelerometer and gyroscope (MPU-6050), and an RF wireless transmission module (nRF24L01).
4:1).
Experimental Procedures and Operational Workflow:
4. Experimental Procedures and Operational Workflow: Participants mounted the system on their foot and walked along a 10 m straight line. Their walking recordings were collected and analyzed using the proposed algorithm.
5:Data Analysis Methods:
The data was analyzed to extract gait parameters such as stride time, walking time, stride length, stride frequency, stride velocity, stride cadence, stance time, swing time, and ankle range of motion.
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