Improvement of human postural stability criterion using ZMP simplification and optimization algorithms

This paper presents a simplified Zero Moment Point algorithm for fall detection using inertial MEMS sensors. Based on body posture and stability judgment optimization system, we realized real-time detection and analysis of the falls. The system can be used in moving people airbag system that can prevent a major part of the induced fracture of the elderly from falls. Hardware includes the nodes that can monitor the movement of the feet and waist, and can detect normal motions and falls in real-time. The node is made mainly by Micro Inertial Measurement Unit (µIMUs). In this system, the detection algorithm is composed of three sub algorithms. The algorithms have also been developed and applied to Zigbee network and the fall detection. The system is based on data analysis and design, in order to choose the best means for monitoring the human movement, we verified the performance of the algorithm for different performance parts. Through the use of different combinations of segments of human pattern recognition algorithm, sub comparative performance was compared. Wearable motion capture device was used to acquire motion data. Based on this analysis, the system is designed to monitor a combination of movement, and the simplified zero moment point optimization algorithm has also been confirmed.