Presentation on theme: "Optimization of Networked Smart Shoe for Gait Analysis using Heuristic Algorithms with Automated Thresholding Nantawat Pinkam, Advisor: Dr. Itthisek Nilkhamhang."— Presentation transcript:
Optimization of Networked Smart Shoe for Gait Analysis using Heuristic Algorithms with Automated Thresholding Nantawat Pinkam, Advisor: Dr. Itthisek Nilkhamhang Academic Year 2012 Problem Statement Gait analyzer can be done by other methods such as image processing but it needs large equipment and the operation cost is high. This project aim to improve the mobility of gait analyzer by using smart shoe with embedded sensors and equip with XBee RF module. State Transition Approach Each gait phase is represented as a state. The transition conditions are shown as Table 1. The transition diagram is shown as Figure 3. Abstract This work proposes a novel decision system for segregation of five normal gait phases (stance, heel-off, swing 1, swing 2 and heel-strike) by using a real-time wireless smart shoe. The classification takes four force sensitive resistors (FSR) to measure force underneath the foot together with an inertia measurement unit (IMU) that is attached at the back of the shoe. IMU gives magnitude of acceleration and inclination angle of the foot with respect to the ground. Data acquisition is collected through XBee wireless network protocol in order to be processed serially by a computer. Threshold-based state transition theorem is used to distinguish gait phases based on received data. Gait phases are determined by marker tracking using image processing while a user performs walking indoor stationary on treadmill. Thresholds for state transition theorem are optimized by genetic algorithm (GA) and compare with particle swarm optimization (PSO). This method will be experimented and verified by a person who has a normal walking gait cycle. Smart Shoe Construction Force Sensitive Resistors (FSRs), Inertia Measurement Unit (IMU) and XBee RF module are placed on smart shoe as shown in Figure 2. Result and Discussion Accuracy of optimized state transition algorithm compares with similar methods without optimization. The optimization is the factor that raises the accuracy of gait phase detection. Normal Gait Cycle The normal gait, which consumes minimal energy and enables the smoothest walking motion, involves unique patterns called gait phases. In this work, the normal gait is assumed to have 5 gait phases: stance, heel-off, swing 1, swing 2, and heel-strike whose flow is shown as Figure 1. Figure 1: Normal Gait Cycle Figure 2: Sensors Placement on Smart Shoe Table 1: State Transition Conditions Figure 3: State Transition Diagram Genetic Algorithm To find suitable thresholds for transition event, the optimization tool is needed. Genetic algorithm (GA) is a population-based optimization model that uses selection and recombination to generate new sample points in a search space. Table 2: Accuracy Comparison Conclusion A gait analyzer can be constructed using wireless smart shoe by combining FSRs, IMU and XBee. The thresholds of transition is tuned with GA/PSO. Ground truth is obtained from markers tracking. The final accuracy is 96.12%. Particle Swarm Optimization Another method to find suitable thresholds. Particle swarm optimization (PSO) is stochastic population based global optimization algorithm inspired by social behavior of fish schooling or bird flocking. Gait Phase Determination using Image Processing To determine the ground truth for optimization process. Image processing is used to extract gait phases from the video. Markers is attached to the lower limb joints for tracking as shown in Figure 4. Figure 4: Gait Determination using Image Processing Electronics and Communication Engineering Programs, School of Information Computer and Communication Technology (ICT), Sirindhorn International Institute of Technology, Thammasat University, THAILAND
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