Smart wearable monitoring system based on multi-type sensors for motion recognition

Motion monitoring systems are often designed and researched to detect the movement of human lower limbs, and play an important role in the field of exoskeleton control. However, current wearable devices can still be improved to be more convenient or accurate in motion recognition. In this work, a comfortable smart wearable gait monitoring system was designed and tested. Inertial measurement units (IMUs) and flexible membrane compression sensors were implemented, integrated to a comfortable sport pant and insoles of both feet, respectively. Data acquisition module was designed, while software with user interface for data collection and storage was realized based on LABVIEW. Experiments were conducted to evaluate the recognition performance of the smart wearable gait monitoring system among nine common actions. Results show that the combined data set of IMUs and compression sensor provided by the system can highly improve classification performance. Based on the self-designed sensing network and the K-nearest neighbor machine learning algorithm, the recognition rate of nine motion patterns can reach as high as 99.96%, showing that the multi-channel wearable gait monitoring system is more effective for motion detection and prediction compared to that with single-type sensors.

Automated summary

A comfortable smart wearable gait monitoring system was designed and tested, utilizing IMUs and flexible membrane compression sensors. By combining the data set from multiple channels with the K-nearest neighbor machine learning algorithm, the recognition rate of motion patterns was significantly improved, demonstrating the effectiveness of a multi-channel wearable gait monitoring system compared to single-type sensors.