Human activity recognition and analysis based on FPGA microboard three-axis accelerometer

• Main Author: Han, Yating
• Other Authors: Goh Wang Ling
• Format: Theses and Dissertations
• Language: English
• Published: 2018
• Subjects: DRNTU::Engineering::Electrical and electronic engineering
• Online Access: http://hdl.handle.net/10356/73111
• Institution: Nanyang Technological University

Human behavior monitoring technology is widely used in medical rehabilitation, health care and human-computer interaction. Human activity recognition based on accelereometer is a new research field of human behavior monitoring. Compared to the human activity recognition method based on vision, it offers numerous advantages such as anti-interference ability, portability, data acquisition free, etc. However, the existing wearable human behavior monitoring system requires a large number of nodes, large power consumption, and poor endurance. Therefore, it is of great significance to design a new wearable human body activity monitoring device that is practical and convenient to use. This dissertation describes the human activity recognition and analysis based on the FPGA (Field Programmable Gate Array) Spartan-6 LX9 Microboard and ADXL345 three-axis accelerometer. In order to reduce the power consumption, MicroBlaze processor embedded in the FPGA Microboard is used to control the three-axis accelerometer. The ability to distinguish slow walking, normal walking and fast walking is dealt with using the proposed walking pattern recognition algorithm based on wavelet energy and IQR (Inter-Quartile Range). Analysis showed that the proposed extraction method can achieve an average recognition rate of 100%, which truly validate the proposed feature extraction method. A recognition algorithm based on standard deviation, skewness, kurtosis and correlation coefficients is also proposed to identity 5 walk pattems, i.e., standing, walking, running, going upstairs and going downstairs. By comparing the K-NN (K-Nearest-Neighbor) and SVM (Support Vector Machines) classifier models, it was found that when using SVM for classification, the behavior recognition rate of standing, walking, running can reached 100%. In other words, human behavior recognition based on three-axis accelerometer is an important research field of activity recognition. It has wide application prospect for wearable human body detection device.