In this paper, a simple platform for exoskeleton booster is designed and built. The sensing device uses static torque sensor for real-time signal acquisition and feedback. In order to remove signal impurities, this paper designs simple and effective impurity filtering circuits and algorithms. The filtered sensor signal was analyzed by unit root test to determine its stability, and the initial model was determined by ACF and PACF order method. According to the characteristics of the sensor signals of this system, the MWDAR model is verified by the model order and the short-term sliding window size. Using the existing MWDAR model, the prediction value is low. Because of the low prediction accuracy, this paper proposes to introduce particle filter algorithm to optimize, and design a new sensor signal time series prediction algorithm, and simulate it through MATLAB software. Verify the effectiveness of the designed algorithm. Due to the characteristics of the force sensor, the dynamic response frequency is significantly lower than the human neural response frequency. At the same time, after the particle optimization algorithm is used, the calculation amount is increased, which makes the prediction delay. In response to these problems, this paper uses frequency doubling technology, which can double the dynamic response frequency of the booster sensing system, thus providing a basis for accurate, real-time signal prediction.

Yanli Zhang. Wearable Robot Sensor Signal Prediction Algorithm Analysis and Study based on Particle Filtering. International Journal of Multimedia Computing (2022), Vol. 3, Issue 3: 14-32. https://doi.org/10.38007/IJMC.2022.030302.

[1] Y. Wen, Ping Li, Jin Yang and Min Zheng, "Detecting and evaluating the signals of wirelessly interrogational passive SAW resonator sensors," in IEEE Sensors Journal, vol. 4, no. 6, pp. 828-836, Dec. 2004. DOI:10.1109/JSEN.2004.837493

[2] S. N. Daskalakis, G. Goussetis, S. D. Assimonis, M. M. Tentzeris and A. Georgiadis, "A uW Backscatter-Morse-Leaf Sensor for Low-Power Agricultural Wireless Sensor Networks," in IEEE Sensors Journal, vol. 18, no. 19, pp. 7889-7898, 1 Oct.1, 2018. DOI:10.1109/JSEN.2018.2861431

[3] R. Carlos et al., "Web-based sensor streaming wearable for respiratory monitoring applications," SENSORS, 2011 IEEE, Limerick, 2011, pp. 901-903. DOI:10.1109/ICSENS.2011.6127168

[4] M. Abu-Saude and B. I. Morshed, "Accessing Differential Measures with a Conjugate Coil-Pair for Wireless Resistive Analog Passive (WRAP) ECG Sensors," 2018 IEEE International Conference on Electro/Information Technology (EIT), Rochester, MI, 2018, pp. 0887-0891.

[5] S. G. Hong, N. S. Kim, C. S. Pyo and W. W. Kim, "Hybrid Sensor Module and Data Processing Using Low-Power Wakeup in WSN," 2010 Fourth International Conference on Sensor Technologies and Applications, Venice, 2010, pp. 191-195.

[6] R. Khorshidi, F. Shabaninia, M. Vaziri and S. Vadhva, "Kalman-Particle Filter Used for Particle Swarm Optimization of Economic Dispatch Problem," 2012 IEEE Global Humanitarian Technology Conference, Seattle, WA, 2012, pp. 220-223.

[7] M. Li, L. Yuan and W. Du, "Unscented Particle Filtering with Particle Swarm Optimization for Estimating Nonlinear System," 2010 Third International Symposium on Electronic Commerce and Security, Guangzhou, 2010, pp. 79-83.

[8] J. Zhu, X. Wang and Q. Fang, "The Improved Particle Filter Algorithm Based on Weight Optimization," 2013 International Conference on Information Science and Cloud Computing Companion, Guangzhou, 2013, pp. 351-356.

[9] R. R. Manikandan, A. Kumar and B. Amrutur, "A Digital Frequency Multiplication Technique for Energy Efficient Transmitters," in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 23, no. 4, pp. 781-785, April 2015. DOI:10.1109/TVLSI.2014.2315232

[10] S. Ok, K. Chung, J. Koo and C. Kim, "An Antiharmonic, Programmable, DLL-Based Frequency Multiplier for Dynamic Frequency Scaling," in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 18, no. 7, pp. 1130-1134, July 2010.