力板是生物力學分析中不可或缺的工具，特別是在步態分析方面。它能夠記錄各種活動中地面反作用力和力矩的動力學，使其成為健康和康復領域中不可或缺的儀器。然而，傳統力板面臨著可及性和經濟限制，這促使人們在各個科學領域探索智能鞋墊作為一種可行的替代品。儘管智能鞋墊提供了經濟優勢和移動性，但以智能鞋墊取代力板的先前工作面臨著重大挑戰。如三軸地面反作用力測量的不準確性、信號分析的限制，以及對多樣化運動場景和配置的適應性不足等問題，影響了其可靠性。這些挑戰凸顯了改進模型和方法以提高準確性和適用性的必要性。針對這些問題，我的貢獻集中在改進智能鞋墊的計算和模擬模型上，採用先進技術克服以往方法的限制。我們的研究利用了ResNet回歸算法的能力，標誌著步態分析預測準確性的重大進步。實驗結果表明，在行走場景中，我們的深度學習模型在訓練期間達到了0.99的R2值和0.007的RMSE，而在測試階段則達到了0.84的R2和0.147的RMSE。同樣，在站立場景中，該模型在訓練期間達到了0.97的R2和0.036的RMSE，測試階段則報告了0.90的R2和0.090的RMSE。這些發現凸顯了深度學習方法在提高步態分析準確性和可靠性方面的重大潛力。ResNet回歸模型取得的性能指標主張生物力學分析采用一種新的方法，強調準確、可靠和可及的方法對研究和臨床應用的重要性。

The force plate is an essential tool in biomechanical analysis, particularly for the gait analysis. Its ability to record the dynamics of ground reaction forces and moments of force across various activities makes it an indispensable instrument in health and wellness. However, traditional force plates face accessibility and economic constraints, which has led to the exploration of smart insoles as a viable alternative in various scientific fields. Despite the economic advantages and mobility that smart insoles offer, prior work in replacing force plates with smart insoles has faced significant challenges. Issues such as inaccuracies in triaxial ground reaction force measurements, limitations in signal analysis, and a lack of adaptability to diverse movement scenarios and configurations have hindered their reliability. These challenges have underscored the necessity for improved models and methods to enhance accuracy and applicability. Addressing these issues, my contribution focuses on refining the calculation and simulation models for smart insoles, employing advanced techniques to overcome the limitations of previous approaches. Our research leveraged the capabilities of the ResNet Regression algorithm, marking a significant advancement in predictive accuracy for gait analysis. Empirical results demonstrated that for the walking scenario, our deep learning model achieved an R2 value of 0.99 and an RMSE of 0.007 during training, with the testing phase registering an R2 of 0.84 and an RMSE of 0.147. Similarly, the standing scenario saw the model attain an R2 of 0.97 with an RMSE of 0.036 during training, with the testing phase reporting an R2 of 0.90 and an RMSE of 0.090. These findings underscore the significant potential of sophisticated deep learning methodologies in enhancing gait analysis accuracy and reliability. The performance metrics achieved by the ResNet Regression model advocate for a renewed approach in biomechanical analyses, emphasizing the importance of accurate, reliable, and accessible methodologies for both research and clinical applications.

[1] Favre, J., Hayoz, M., Erhart-Hledik, J. C., and Andriacchi, T. P., 2012, “A Neural Network Model to Predict Knee Adduction Moment During Walking Based on Ground Reaction Force and Anthropometric Measurements,” J. Biomech., 45(4), pp. 692–698.
[2] Oh, S. E., Choi, A., and Mun, J. H., 2013, “Prediction of Ground Reaction Forces During Gait Based on Kinematics and a Neural Network Model,” J. Biomech., 46(14), pp. 2372–2380.
[3] Tao, W., Liu, T., Zheng, R., and Feng, H., 2012, “Gait Analysis Using Wearable Sensors,” Sensors, 12(2), pp. 2255–2283.
[4] M. C. Cramp, R. J. Greenwood, M. Gill, A. Lehmann, J.C. Rothwell, O. M. Scott, “Effectiveness of a community-based low intensity exercise programme for ambulatory stroke survivors,” Disability and Rehabilitation, vol.32, No.3 (2010), pp. 239-247.
[5] A.Srivastava, A. B. Taly, A. Gupta, S. Kumar, and T. Murali, “Post-stroke balance training: Role of force platform with visual feedback technique,” Journal of the Neurological Sciences, Vol.287, No.1-2 (2009), pp. 89-93.
[6] J. Z. Wu, S. S. Chiou, and C.S. Pan, “Analysis of musculoskeletal loadings in lower limbs during stilts walking in occupational activity,” Annals of Biomedical Engineering, Vol.37, No.6 (2009), pp. 1177-1189.
[7] Y. C. Lin, J. P. Walter, S. A. Banks, M. G. Pandy, and B. J. Fregly, “Simultaneous prediction of muscle and contact forces in the knee during gait,” Journal of Biomechanics, Vol.43, No.5 (2009), pp. 945-952.
[8] T. Liu, Y. Inoue, K. Shibata, Y. Hirota, and K. Shiojima,” A Mobile Force Plate System and Its Application to Quantitative Evaluation of Normal and Pathological Gait,” 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), (2010), pp. 272-277.
[9] O’Donovan, K. J., Kamnik, R., O’Keeffe, D. T., and Lyons, G. M., 2007, “An Inertial and Magnetic Sensor Based Technique for Joint Angle Measurement,” J. Biomech., 40(12), pp. 2604–2611.
[10] Abdul Razak, A. H., Zayegh, A., Begg, R. K., and Wahab, Y., 2012, “Foot Smart insoles Measurement System: A Review,” Sensors, 12(7), pp. 9884–9912.
[11] Muro-de-la-Herran, A., Garcia-Zapirain, B., and Mendez-Zorrilla, A., 2014, “Gait Analysis Methods: An Overview of Wearable and Non-Wearable Systems, Highlighting Clinical Applications,” Sensors, 14(2), pp. 3362–3394.
[12] Liu, T., Inoue, Y., and Shibata, K., 2010, “A Wearable Ground Reaction Force Sensor System and Its Application to the Measurement of Extrinsic Gait Variability,” Sensors, 10(11), pp. 10240–10255.
[13] Liu, T., Inoue, Y., and Shibata, K., 2010, “A Wearable Force Plate System for the Continuous Measurement of Triaxial Ground Reaction Force in Biomechanical Applications,” Meas. Sci. Technol., 21(8), p. 085804.
[14] Hori, N., Newton, R. U., Kawamori, N., McGuigan, M. R., Kraemer, W. J., and Nosaka, K., 2009, “Reliability of Performance Measurements Derived From Ground Reaction Force Data During Countermovement Jump and the Influence of Sampling Frequency,” J. Strength Cond. Res., 23(3), pp. 874–882.
[15] Nikooyan, A. A., and Zadpoor, A. A., 2012, “Effects of Muscle Fatigue on the Ground Reaction Force and Soft-Tissue Vibrations During Running: A Model Study,” IEEE Trans. Biomed. Eng., 59(3), pp. 797–804
[16] Giakas, G., Baltzopoulos, V., Dangerfield, P. H., Dorgan, J. C., and Dalmira, S., 1996, “Comparison of Gait Patterns Between Healthy and Scoliotic Patients Using Time and Frequency Domain Analysis of Ground Reaction Forces,” Spine, 21(19), pp. 2235–2242.
[17] Bruyneel, A. V., Chavet, P., Bollini, G., Allard, P., Berton, E., and Mesure, S., 2009, “Dynamical Asymmetries in Idiopathic Scoliosis During Forward and Lateral Initiation Step,” Eur. Spine J., 18(2), pp. 188–195.
[18] Savelberg, H. H. C. M., and de Lange, A. L. H., 1999, “Assessment of the Horizontal, Fore-Aft Component of the Ground Reaction Force From Insole Pressure Patterns by Using Artificial Neural Networks,” Clin. Biomech., 14(8), pp. 585–592.
[19] Miller, N. H., 2011, “Idiopathic Scoliosis: Cracking the Genetic Code and What Does It Mean,” J. Pediatr. Orthop., 31(Suppl. 1), pp. S49–S52.
[20] Fregly, B. J., Reinbolt, J. A., Rooney, K. L., Mitchell, K. H., and Chmielewski, T. L., 2007, “Design of Patient-Specific Gait Modifications for Knee Osteoarthritis Rehabilitation,” IEEE Trans. Biomed. Eng., 54(9), pp. 1687–1695.
[21] Houck, J., Kneiss, J., Bukata, S. V., and Puzas, J. E., 2011, “Analysis of Vertical Ground Reaction Force Variables During a Sit to Stand Task in Participants Recovering From a Hip Fracture,” Clin. Biomech., 26(5), pp. 470–476.
[22] Shin, K. Y., Rim, Y. H., Kim, Y. S., Kim, H. S., Han, J. W., Choi, C. H., Lee, K. S., and Mun, J. H., 2010, “A Joint Normalcy Index to Evaluate Patients With Gait Pathologies in the Functional Aspects of Joint Mobility,” J. Mech. Sci. Technol., 24(9), pp. 1901–1909.
[23] Rouhani, H., Favre, J., Crevoisier, X., and Aminian, K., 2010, “Ambulatory Assessment of 3D Ground Reaction Force Using Smart insoles Distribution,” Gait Posture, 32(3), pp. 311–316.
[24] Forner Cordero, A., Koopman, H. J. F. M., and van der Helm, F. C. T., 2004, “Use of Pressure Insoles to Calculate the Complete Ground Reaction Forces,” J. Biomech., 37(9), pp. 1427–1432.
[25] Ardestani, M. M., Zhang, X., Wang, L., Lian, Q., Liu, Y., He, J., Li, D., and Jin, Z., 2014, “Human Lower Extremity Joint Moment Prediction: A Wavelet Neural Network Approach,” Expert Syst. Appl., 41(9), pp. 4422–4433.
[26] Subasi, A., 2007, “EEG Signal Classification Using Wavelet Feature Extraction and a Mixture of Expert Model,” Expert Syst. Appl., 32(4), pp. 1084–1093.
[27] Esen, H., Ozgen, F., Esen, M., and Sengur, A., 2009, “Artificial Neural Network and Wavelet Neural Network Approaches for Modelling of a Solar Air Heater,” Expert Syst. Appl., 36(8), pp. 11240–11248.
[28] Liu, T., Inoue, Y., Shibata, K., Hirota, Y., & Shiojima, K. (2010). A mobile force plate system and its application to quantitative evaluation of normal and pathological gait. 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.
[29] ] T. Liu, Y. Inoue and K. Shibata, "A wearable force plate system to successively measure multi-axial ground reaction force for gait analysis," 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO), Guilin, China, 2009, pp. 836-841.
[30] Adachi, W., Tsujiuchi, N., Koizumi, T., Aikawa, M., Shiojima, K., Tsuchiya, Y., & Inoue, Y. (2011). Development of walking analysis system consisting of mobile force plate and motion sensor. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[31] Tan, A. M., Fuss, F. K., Weizman, Y., & Troynikov, O. (2015). Development of a smart insole for medical and sports purposes. Procedia Engineering, 112, 152-156.
[32] Tan, A. M., Fuss, F. K., Weizman, Y., Woudstra, Y., & Troynikov, O. (2015). Design of low cost smart insole for real time measurement of plantar pressure. Procedia Technology, 20, 117-122.
[33] Tan, Adin & Fuss, Franz & Weizman, Yehuda & Azari, Michael. (2015). Centre of Pressure Detection and Analysis with a High-resolution and Low-cost Smart Insole. Procedia Engineering. 112. 146-151. 10.1016/j.proeng.2015.07.190.
[34] Weizman, Yehuda & Tan, Adin & Fuss, Franz. (2019). Benchmarking Study of the Forces and Centre of Pressure derived from a novel Smart-Insole against an Existing Pressure Measuring Insole and Force Plate. Measurement. 142. 10.1016/j.measurement.2019.03.023.
[35] Chou L-W, Shen J-H, Lin H-T, Yang Y-T, Hu W-P. A Study on the Influence of Number/Distribution of Sensing Points of the Smart Insoles on the Center of Pressure Estimation for the Internet of Things Applications. Sustainability. 2021; 13(5):2934.
[36] L. Cai and Y. Zhu, “The challenges of data quality and data quality assessment in the big data era,” Data science journal, vol. 14, 2015.
[37] C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, “Understanding deep learning (still) requires rethinking generalization,” Communications of the ACM, vol. 64, no. 3, pp. 107–115, 2021.
[38] D. Arpit, S. Jastrzundefinedbski, N. Ballas, D. Krueger, E. Bengio, M. S. Kanwal, T. Maharaj, A. Fischer, A. Courville, Y. Bengio, and S. LacosteJulien, “A closer look at memorization in deep networks,” in Proceedings of the 34th International Conference on Machine Learning - Volume 70, ser. ICML’17. JMLR.org, 2017, p. 233–242.
[39] K. Huang, H.-G. Stratigopoulos, and S. Mir, “Fault diagnosis of analog circuits based on machine learning,” in 2010 Design, Automation & Test in Europe Conference & Exhibition (DATE 2010). IEEE, 2010, pp. 1761–1766.
[40] R. Fu, Y. Huang, and P. V. Singh, “Crowds, lending, machine, and bias,” Information Systems Research, vol. 32, no. 1, pp. 72–92, 2021.
[41] B. Frenay and M. Verleysen, “Classification in the presence of label ´ noise: a survey,” IEEE transactions on neural networks and learning systems, vol. 25, no. 5, pp. 845–869, 2013.
[42] X. Zhu and X. Wu, “Class noise vs. attribute noise: A quantitative study,” Artificial intelligence review, vol. 22, no. 3, pp. 177–210, 2004.
[43] W. Shin, J.-W. Ha, S. Li, Y. Cho, H. Song, and S. Kwon, “Which strategies matter for noisy label classification? insight into loss and uncertainty,” arXiv e-prints, pp. arXiv–2008, 2020.
[44] J. Cao, S. Kwong, and R. Wang, “A noise-detection based adaboost algorithm for mislabeled data,” Pattern Recognition, vol. 45, no. 12, pp. 4451–4465, 2012.
[45] Scheres, S. H., & Chen, S. (2012). Prevention of overfitting in cryo-EM structure determination. Nature methods, 9(9), 853-854.
[46] Ying, X. (2019, February). An overview of overfitting and its solutions. In Journal of physics: Conference series (Vol. 1168, p. 022022).
[47] Paris G., Robilliard D., Fonlupt C. (2004) Exploring Overfitting in Genetic Programming. Artificial Evolution, International Conference, Evolution Artificielle, Ea 2003, Marseilles, France, October. DBLP, pp.267-277.
[48] Paris G., Robilliard D., Fonlupt C. (2004) Exploring Overfitting in Genetic Programming. Artificial Evolution, International Conference, Evolution Artificielle, Ea 2003, Marseilles, France, October. DBLP, pp.267-277.
[49] Hara, K., Saitoh, D., & Shouno, H. (2016). Analysis of dropout learning regarded as ensemble learning. In Artificial Neural Networks and Machine Learning–ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II 25 (pp. 72-79).
[50] Cross, R. (1999). Standing, walking, running, and jumping on a force plate. American Journal of Physics, 67(4), 304-309.
[51] Xu, W., Huang, M. C., Amini, N., Liu, J. J., He, L., & Sarrafzadeh, M. (2012, June). Smart insole: A wearable system for gait analysis. In Proceedings of the 5th international conference on pervasive technologies related to assistive environments (pp. 1-4).
[52] McDonald, G. C. (2009). Ridge regression. Wiley Interdisciplinary Reviews: Computational Statistics, 1(1), 93-100.
[53] Pérez-Rodríguez, P., Gianola, D., González-Camacho, J. M., Crossa, J., Manès, Y., & Dreisigacker, S. (2012). Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat. G3: Genes| Genomes| Genetics, 2(12), 1595-1605.
[54] Su, X., Yan, X., & Tsai, C. L. (2012). Linear regression. Wiley Interdisciplinary Reviews: Computational Statistics, 4(3), 275-294.
[55] Stolk, P. C., Jacobs, C. M. J., Moors, E. J., Hensen, A., Velthof, G. L., & Kabat, P. (2009). Significant non-linearity in nitrous oxide chamber data and its effect on calculated annual emissions. Biogeosciences Discussions, 6(1), 115-141.
[56] Wang, B., Yan, Z., Lu, J., Zhang, G., & Li, T. (2018, July). Explore uncertainty in residual networks for crowds flow prediction. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE.
[57] Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
[58] Sagheer, A., & Kotb, M. (2019). Time series forecasting of petroleum production using deep LSTM recurrent networks. Neurocomputing, 323, 203-213.
[59] Masum, S., Liu, Y., & Chiverton, J. (2018). Multi-step time series forecasting of electric load using machine learning models. In Artificial Intelligence and Soft Computing: 17th International Conference, ICAISC 2018, Zakopane, Poland, June 3-7, 2018, Proceedings, Part I 17 (pp. 148-159). Springer International Publishing.
[60] Zhou, Z. H. (2021). Machine learning. Springer Nature.
[61] Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758).
[62] Darwin, D., Christian, D., Chandra, W., & Nababan, M. (2022). Comparison of Decision Tree and Linear Regression Algorithms in the Case of Spread Prediction of COVID-19 in Indonesia. Journal of Computer Networks, Architecture and High Performance Computing, 4(1), 1-12. Descion tree linear reg
[63] Ibrahim, Z., & Rusli, D. (2007, September). Predicting students’ academic performance: comparing artificial neural network, decision tree and linear regression. In 21st Annual SAS Malaysia Forum, 5th September
[64] Shahriar, S. A., Kayes, I., Hasan, K., Hasan, M., Islam, R., Awang, N. R., ... & Salam, M. A. (2021). Potential of ARIMA-ANN, ARIMA-SVM, DT and CatBoost for atmospheric PM2. 5 forecasting in Bangladesh. Atmosphere, 12(1), 100.
[65] Hedyehzadeh, Mohammadreza and Pu, Jiantao and Leilizadeh, Shadi and Gezer, Sinem and Dresser, Christian and Beeche, Cameron Alexander, A Comparison of Deep and Conventional Regression Methods for MRI-Based Estimation of Survival Time in GBM Patients
[66] Amirkhalili, Y. S., Aghsami, A., & Jolai, F. (2020). Comparison of Time Series ARIMA Model and Support Vector Regression. International Journal of Hybrid Information Technology, 13(1), 7-18.
[67] Xiang, Y., Chen, Q., Su, Z., Zhang, L., Chen, Z., Zhou, G., ... & Cheng, Y. (2022). Deep learning and hyperspectral images based tomato soluble solids content and firmness estimation. Frontiers in Plant Science, 13, 860656.
[68] Ahmad, M. S., Adnan, S. M., Zaidi, S., & Bhargava, P. (2020). A novel support vector regression (SVR) model for the prediction of splice strength of the unconfined beam specimens. Construction and building materials, 248, 118475.
[69] Balogun, A. L., & Tella, A. (2022). Modelling and investigating the impacts of climatic variables on ozone concentration in Malaysia using correlation analysis with random forest, decision tree regression, linear regression, and support vector regression. Chemosphere, 299, 134250.
[70] Campanella, Gabriele & Rajanna, Arjun & Corsale, Lorraine & Schüffler, Peter & Yagi, Yukako & Fuchs, Thomas. (2017). Towards Machine Learned Quality Control: A Benchmark for Sharpness Quantification in Digital Pathology. Computerized Medical Imaging and Graphics. 65. 10.1016/j.compmedimag.2017.09.001.
[71] Zhao, H., Li, J., Yuan, Q., Lin, L., Yue, L., & Xu, H. (2022). Downscaling of soil moisture products using deep learning: Comparison and analysis on Tibetan Plateau. Journal of Hydrology, 607, 127570.
[72] Kane, M. J., Price, N., Scotch, M., & Rabinowitz, P. (2014). Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks. BMC bioinformatics, 15(1), 1-9.
[73] Kelleher, J. D. (2019). Deep learning. MIT press.
[74] Elfahham, Y. (2019). Estimation and prediction of construction cost index using neural networks, time series, and regression. Alexandria Engineering Journal, 58(2), 499-506
[75] Singh, S., Jaishi, H. P., Tiwari, R. P., & Tiwari, R. C. (2017). Time series analysis of soil radon data using multiple linear regression and artificial neural network in seismic precursory studies. Pure and Applied Geophysics, 174, 2793-2802.
[76] Yip, H. L., Fan, H., & Chiang, Y. H. (2014). Predicting the maintenance cost of construction equipment: Comparison between general regression neural network and Box–Jenkins time series models. Automation in Construction, 38, 30-38.
[77] Bandyopadhyay, G., & Chattopadhyay, S. (2007). Single hidden layer artificial neural network models versus multiple linear regression model in forecasting the time series of total ozone. International Journal of Environmental Science & Technology, 4, 141-149.
[78] Hu, Q. P., Xie, M., Ng, S. H., & Levitin, G. (2007). Robust recurrent neural network modeling for software fault detection and correction prediction. Reliability Engineering & System Safety, 92(3), 332-340.
[79] Wang, J., Wang, J., Fang, W., & Niu, H. (2016). Financial time series prediction using elman recurrent random neural networks. Computational intelligence and neuroscience, 2016.
[80] Zhang, Y., Wang, X., & Tang, H. (2019). An improved Elman neural network with piecewise weighted gradient for time series prediction. Neurocomputing, 359, 199-208.
[81] Tealab, A. (2018). Time series forecasting using artificial neural networks methodologies: A systematic review. Future Computing and Informatics Journal, 3(2), 334-340.
[82] Karaci, A., Yaprak, H., ÖZKARACA, O., Demir, I., & ŞİMŞEK, O. (2019). Estimating the properties of ground-waste-brick mortars using DNN and ANN. CMES-Computer Modeling in Engineering & Sciences, 118(1).
[83] Merkel, G. D., Povinelli, R. J., & Brown, R. H. (2017). Deep neural network regression for short-term load forecasting of natural gas. In 37th Annual International Symposium on Forecasting (pp. 246-255).
[84] Shadman Roodposhti, M., Aryal, J., Lucieer, A., & Bryan, B. A. (2019). Uncertainty assessment of hyperspectral image classification: Deep learning vs. random forest. Entropy, 21(1), 78.
[85] Qi, J., Du, J., Siniscalchi, S. M., & Lee, C. H. (2019). A theory on deep neural network based vector-to-vector regression with an illustration of its expressive power in speech enhancement. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 27(12), 1932-1943.
[86] Kim, J. K., Lv, Z., Park, D., & Chang, M. C. (2022). Practical machine learning model to predict the recovery of motor function in patients with stroke. European Neurology, 85(4), 273-279.
[87] Al-Hajj, R., Assi, A., & Fouad, M. M. (2017, November). A predictive evaluation of global solar radiation using recurrent neural models and weather data. In 2017 IEEE 6th international conference on renewable energy research and applications (ICRERA) (pp. 195-199). IEEE.
[88] Ramakrishnan, N., & Soni, T. (2018, December). Network traffic prediction using recurrent neural networks. In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 187-193). IEEE.
[89] Liu, Y., Xu, S., Kobori, S., Hashimoto, S., & Kawaguchi, T. (2021, May). Time-Delay Temperature Control System Design based on Recurrent Neural Network. In 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) (pp. 820-825). IEEE.
[90] Pascanu, R., Mikolov, T., & Bengio, Y. (2013, May). On the difficulty of training recurrent neural networks. In International conference on machine learning (pp. 1310-1318).
[91] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
[92] Masum, S., Liu, Y., & Chiverton, J. (2018). Multi-step time series forecasting of electric load using machine learning models. In Artificial Intelligence and Soft Computing: 17th International Conference, ICAISC 2018, Zakopane, Poland, June 3-7, 2018, Proceedings, Part I 17 (pp. 148-159). Springer International Publishing.
[93] Masum, S., Liu, Y., & Chiverton, J. (2018). Multi-step time series forecasting of electric load using machine learning models. In Artificial Intelligence and Soft Computing: 17th International Conference, ICAISC 2018, Zakopane, Poland, June 3-7, 2018, Proceedings, Part I 17 (pp. 148-159). Springer International Publishing.
[94] Zhou, K., Wang, W. Y., Hu, T., & Wu, C. H. (2020, September). Comparison of time series forecasting based on statistical ARIMA model and LSTM with attention mechanism. In Journal of physics: conference series (Vol. 1631, No. 1, p. 012141). IOP Publishing.
[95] López, G., & Arboleya, P. (2022). Short-term wind speed forecasting over complex terrain using linear regression models and multivariable LSTM and NARX networks in the Andes Mountains, Ecuador. Renewable Energy, 183, 351-368.
[96] Phaladisailoed, T., & Numnonda, T. (2018, July). Machine learning models comparison for bitcoin price prediction. In 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE) (pp. 506-511). IEEE.
[97] Oksuz, I., & Ugurlu, U. (2019). Neural network based model comparison for intraday electricity price forecasting. Energies, 12(23), 4557.
[98] Liu, B., Fu, C., Bielefield, A., & Liu, Y. Q. (2017). Forecasting of Chinese primary energy consumption in 2021 with GRU artificial neural network. Energies, 10(10), 1453.
[99] Hasnain, M., Pasha, M. F., Lim, C. H., & Ghan, I. (2019). Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) (pp. 96-105). IEEE.
[100] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.
[101] Koprinska, I., Wu, D., & Wang, Z. (2018, July). Convolutional neural networks for energy time series forecasting. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
[102] Mehtab, S., Sen, J., & Dasgupta, S. (2020, November). Robust analysis of stock price time series using CNN and LSTM-based deep learning models. In 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 1481-1486). IEEE.
[103] Doppalapudi, S., Qiu, R. G., & Badr, Y. (2021). Lung cancer survival period prediction and understanding: Deep learning approaches. International Journal of Medical Informatics, 148, 104371.
[104] Sayeed, A., Choi, Y., Eslami, E., Lops, Y., Roy, A., & Jung, J. (2020). Using a deep convolutional neural network to predict 2017 ozone concentrations, 24 hours in advance. Neural Networks, 121, 396-408.
[105] Bai, S., Kolter, J. Z., & Koltun, V. (2018). Trellis networks for sequence modeling. arXiv preprint arXiv:1810.06682.
[106] Wang, H., & Zhang, Z. (2022, May). TATCN: time series prediction model based on time attention mechanism and TCN. In 2022 IEEE 2nd international conference on computer communication and artificial intelligence (CCAI) (pp. 26-31). IEEE.
[107] Sofka, M., Milletari, F., Jia, J., & Rothberg, A. (2017). Fully convolutional regression network for accurate detection of measurement points. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings 3 (pp. 258-266). Springer International Publishing.
[108] Jiang, D., Hu, G., Qi, G., & Mazur, N. (2021). A fully convolutional neural network-based regression approach for effective chemical composition analysis using near-infrared spectrosCOPy in cloud. Journal of Artificial Intelligence and Technology, 1(1), 74-82.
[109] Weng, S., Yuan, H., Zhang, X., Li, P., Zheng, L., Zhao, J., & Huang, L. (2020). Deep learning networks for the recognition and quantitation of surface-enhanced Raman spectrosCOPy. Analyst, 145(14), 4827-4835.
[110] Qi, X., Liao, R., Liu, Z., Urtasun, R., & Jia, J. (2018). Geonet: Geometric neural network for joint depth and surface normal estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 283-291). VGG-baseline
[111] Zhang, J., Yang, G., Sun, L., Zhou, C., Zhou, X., Li, Q., ... & Guo, J. (2021). Shrimp egg counting with fully convolutional regression network and generative adversarial network. Aquacultural Engineering, 94, 102175.
[112] Shi, W., Pang, E., Wu, Q., & Lin, F. (2020). Brain tumor segmentation using dense channels 2D U-Net and multiple feature extraction network. In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part I 5 (pp. 273-283). Springer International Publishing.
[113] Barbole, D. K., & Jadhav, P. M. (2022, January). Grape yield prediction using deep learning regression model. In 2022 International Conference for Advancement in Technology (ICONAT) (pp. 1-6). IEEE.
[114] Bukar, A. M., & Ugail, H. (2017). Automatic age estimation from facial profile view. IET Computer Vision, 11(8), 650-655.
[115] Valença, J., Mukhandi, H., Araújo, A. G., Couceiro, M. S., & Júlio, E. (2022). Benchmarking for Strain Evaluation in CFRP Laminates Using Computer Vision: Machine Learning versus Deep Learning. Materials, 15(18), 6310.
[116] Fu, Y. (2020). Fruit freshness grading using deep learning (Master’s Thesis). New Zealand: Auckland University of Technology.
[117] Wang, Q., Yang, D., Li, Z., Zhang, X., & Liu, C. (2020). Deep regression via multi-channel multi-modal learning for pneumonia screening. IEEE Access, 8, 78530-78541.
[118] Aljazaeri, M., Bazi, Y., AlMubarak, H., & Alajlan, N. (2020, October). Faster R-CNN and DenseNet regression for glaucoma detection in retinal fundus images. In 2020 2nd International Conference on Computer and Information Sciences (ICCIS) (pp. 1-4). IEEE.
[119] Otálora, S., Atzori, M., Andrearczyk, V., & Müller, H. (2018). Image magnification regression using densenet for exploiting histopathology open access content. In Computational Pathology and Ophthalmic Medical Image Analysis: First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16-20, 2018, Proceedings 5 (pp. 148-155).
[120] hen D, Hu F, Nian G, Yang T. Deep Residual Learning for Nonlinear Regression. Entropy. 2020 resnet vs traditional
[121] Xu, Zhenyi & Cao, Yang & Kang, Yu. (2019). Deep Spatiotemporal Residual Early-late Fusion Network for City Region Vehicle Emission Pollution Prediction. Neurocomputing. 355. 10.1016/j.neucom.2019.04.040. resnet vs GRU,ANN,ARIMA
[122] Huang, K. (2021, April). Traffic agent movement prediction using resnet-based model. In 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP) (pp. 136-139). IEEE. Resnet vs VGG
[123] Lathuilière, Stéphane, et al. "A comprehensive analysis of deep regression." IEEE transactions on pattern analysis and machine intelligence 42.9 (2019): 2065-2081. Resnet vs VGG
[124] Zhang, Z. (2021, January). Resnet-based model for autonomous vehicles trajectory prediction. In 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) (pp. 565-568). IEEE. Resnet vs vgg
[125] Ding, X., Lin, Z., He, F., Wang, Y., & Huang, Y. (2018, April). A deeply-recursive convolutional network for crowd counting. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1942-1946). Resnetvs FCN
[126] Chen, L., & Luo, X. (2023). Tensor distribution regression based on the 3D conventional neural networks. IEEE/CAA Journal of Automatica Sinica, 10(7), 1628-1630. Resnet vs CNN
[127] Kaur, R., Levy, J., Motl, R. W., Sowers, R., & Hernandez, M. E. (2023). Deep Learning for Multiple Sclerosis Differentiation Using Multi-Stride Dynamics in Gait. IEEE Transactions on Biomedical Engineering. Resnet TCN
[128] Ajala, S., Muraleedharan Jalajamony, H., Nair, M., Marimuthu, P., & Fernandez, R. E. (2022). Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force. Scientific Reports, 12(1), 1-17. Resnet vs google net
[129] Bukar, A. M., & Ugail, H. (2017). Automatic age estimation from facial profile view. IET Computer Vision, 11(8), 650-655. Resnet vs googlenet
[130] Li, H., Yang, W., Zhang, X., Wei, X., & Xu, X. (2022). A ResNet-Based Method for Complex Channel Interpretation in Seismic Volumes. IEEE Geoscience and Remote Sensing Letters, 19, 1-5. Resnet vs unet
[131] Siddiqui, H., Rattani, A., Kisku, D. R., & Dean, T. (2020, December). Al-based bmi inference from facial images: An application to weight monitoring. In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 1101-1105). IEEE.resnet desnet
[132] Choi, H. S., An, K., & Kang, M. (2019). Regression with residual neural network for vanishing point detection. Image and Vision Computing, 91, 103797.
[133] Borovykh, A., Bohte, S., & Oosterlee, C. W. (2018). Dilated convolutional neural networks for time series forecasting. Journal of Computational Finance, Forthcoming.
[134] Qi, W., & Chelmis, C. (2023). Noisy Label Detection and Counterfactual Correction. IEEE Transactions on Artificial Intelligence.
[135] Qi, W., & Chelmis, C. (2022, December). Robust Learning with Noisy Label Detection and Counterfactual Correction. In 2022 IEEE International Conference on Big Data (Big Data) (pp. 1324-1329). IEEE.
[136] Ghosh, A., Kumar, H., & Sastry, P. S. (2017, February). Robust loss functions under label noise for deep neural networks. In Proceedings of the AAAI conference on artificial intelligence (Vol. 31, No. 1).
[137] Malach, E., & Shalev-Shwartz, S. (2017). Decoupling" when to update" from" how to update". Advances in neural information processing systems, 30.
[138] C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, “Understanding deep learning requires rethinking generalization,” arXiv preprint 11 arXiv:1611.03530, 2016.
[139] A. More, “Survey of resampling techniques for improving classification performance in unbalanced datasets,” arXiv preprint arXiv:1608.06048, 2016.
[140] Y. Cui, Y. Song, C. Sun, A. Howard, and S. Belongie, “Large scale fine-grained categorization and domain-specific transfer learning,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 4109–4118.
[141] B. Han, Q. Yao, X. Yu, G. Niu, M. Xu, W. Hu, I. Tsang, and M. Sugiyama, “Co-teaching: Robust training of deep neural networks with extremely noisy labels,” arXiv preprint arXiv:1804.06872, 2018.
[142] X. Yu, B. Han, J. Yao, G. Niu, I. Tsang, and M. Sugiyama, “How does disagreement help generalization against label corruption?” in International Conference on Machine Learning. PMLR, 2019, pp. 7164–7173.
[143] K. Nishi, Y. Ding, A. Rich, and T. Hollerer, “Augmentation strategies for learning with noisy labels,” arXiv preprint arXiv:2103.02130, 2021.
[144] Olmin, A., & Lindsten, F. (2022, May). Robustness and reliability when training with noisy labels. In International Conference on Artificial Intelligence and Statistics (pp. 922-942).
[145] Ribeiro, R. P., & Moniz, N. (2020). Imbalanced regression and extreme value prediction. Machine Learning, 109, 1803-1835.
[146] Steininger, M., Kobs, K., Davidson, P., Krause, A., & Hotho, A. (2021). Density-based weighting for imbalanced regression. Machine Learning, 110, 2187-2211.
[147] Stocksieker, S., Pommeret, D., & Charpentier, A. (2023). Data Augmentation for Imbalanced Regression. arXiv preprint arXiv:2302.09288.
[148] Li, Z., Kamnitsas, K., & Glocker, B. (2020). Analyzing overfitting under class imbalance in neural networks for image segmentation. IEEE transactions on medical imaging, 40(3), 1065-1077.
[149] Gupta, G. K., & Sharma, D. K. (2022, March). A review of overfitting solutions in smart depression detection models. In 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 145-151).
[150] Srivastava N, Hinton G, Krizhevsky A, et al. (2014) Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1):1929-1958.
[151] Warde-Farley D, Goodfellow I J, Courville A, et al. (2013) An empirical analysis of dropout in piecewise linear networks. Computer Science.
[152] Polikar R.: Ensemble Based Systems in Decision Making. IEEE Circuits and Systems Magazine, 6(3). (2006)
[153] Dietterich, T.G.: Machine learning research: Four current directions. AI Magazine 18(4) pp. 97–136. (1997)
[154] Breiman, L.: Bagging predictors, Machine Learning 24 (2), pp. 123-140. (1996)
[155] Schapire, R. E., Freund, Y., Bartlett, P., Lee, W. S.: Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics 26(5), pp 1651–1686. (1998)
[156] Freund, Y., Schapire, R.E.: A Decision-Theoritic Generalization of on-line Learning and an Application to Boosting. (1995)
[157] Latash, M. L. (2012a). Exemplary behaviors. In Fundamentals of Motor Control (pp. 211–259). Elsevier. DOI:10.1016/b978-0-12-415956-3.00011-7.
[158] Latash, M. L. (2012b). Methods in motor control studies. In Fundamentals of Motor Control (pp. 285–321). Elsevier. DOI:10.1016/b978-0-12-415956-3.00013-0.
[159] Savadkoohi, M., Oladunni, T., & Thompson, L. A. (2021). Deep neural networks for human’s fall-risk prediction using force-plate time series signal. Expert systems with applications, 182, 115220.
[160] Rasmussen, C. E., & Williams, C. K. (2006). Gaussian processes for machine learning (Vol. 1, p. 159). Cambridge, MA: MIT press.
[161] Arslan, M., Guzel, M., Demirci, M., & Ozdemir, S. (2019, September). SMOTE and gaussian noise based sensor data augmentation. In 2019 4th International Conference on Computer Science and Engineering (UBMK) (pp. 1-5). IEEE.
[162] Yuqing, W. A. N. G., Wenkai, L. U., JinLin, L., Meng, Z., & YongKang, M. (2019). Random seismic noise attenuation based on data augmentation and CNN. Chinese Journal of Geophysics, 62(1), 421-433.
[163] Dong, H., Wang, J., Wu, X., Zhou, M., & Lü, J. (2023). Gaussian Noise Data Augmentation-Based Delay Prediction for High-Speed Railways. IEEE Intelligent Transportation Systems Magazine.
[164] Huang, J., Qu, L., Jia, R., & Zhao, B. (2019). O2u-net: A simple noisy label detection approach for deep neural networks. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 3326-3334).
[165] Cheng, H., Zhu, Z., Li, X., Gong, Y., Sun, X., & Liu, Y. (2020). Learning with instance-dependent label noise: A sample sieve approach. arXiv preprint arXiv:2010.02347.
[166] Pleiss, G., Zhang, T., Elenberg, E., & Weinberger, K. Q. (2020). Identifying mislabeled data using the area under the margin ranking. Advances in Neural Information Processing Systems, 33, 17044-17056.
[167] Shin, W., Ha, J. W., Li, S., Cho, Y., Song, H., & Kwon, S. (2020). Which strategies matter for noisy label classification? Insight into loss and uncertainty. arXiv preprint arXiv:2008.06218.
[168] Han, B., Yao, Q., Yu, X., Niu, G., Xu, M., Hu, W., ... & Sugiyama, M. (2018). Co-teaching: Robust training of deep neural networks with extremely noisy labels. Advances in neural information processing systems, 31.
[169] Cao, J., Kwong, S., & Wang, R. (2012). A noise-detection based AdaBoost algorithm for mislabeled data. Pattern Recognition, 45(12), 4451-4465.
[170] Zhang, C., Bengio, S., Hardt, M., Recht, B., & Vinyals, O. (2021). Understanding deep learning (still) requires rethinking generalization. Communications of the ACM, 64(3), 107-115.
[171] Arpit, D., Jastrzębski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M. S., ... & Lacoste-Julien, S. (2017, July). A closer look at memorization in deep networks. In International conference on machine learning (pp. 233-242). PMLR.
[172] Song, H., Kim, M., Park, D., Shin, Y., & Lee, J. G. (2022). Learning from noisy labels with deep neural networks: A survey. IEEE Transactions on Neural Networks and Learning Systems.
[173] Müller, N. M., & Markert, K. (2019, July). Identifying mislabeled instances in classification datasets. In 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
[174] Qi, W., & Chelmis, C. (2022, December). Robust Learning with Noisy Label Detection and Counterfactual Correction. In 2022 IEEE International Conference on Big Data (Big Data) (pp. 1324-1329). IEEE.
[175] Qi, W., & Chelmis, C. (2023). Noisy Label Detection and Counterfactual Correction. IEEE Transactions on Artificial Intelligence.
[176] P. Chen, B. B. Liao, G. Chen, and S. Zhang, “Understanding and utilizing deep neural networks trained with noisy labels,” in International Conference on Machine Learning. PMLR, 2019, pp. 1062–1070.
[177] B. Han, Q. Yao, X. Yu, G. Niu, M. Xu, W. Hu, I. Tsang, and M. Sugiyama, “Co-teaching: Robust training of deep neural networks with extremely noisy labels,” arXiv preprint arXiv:1804.06872, 2018
[178] N. M. Muller and K. Markert, “Identifying mislabeled instances in clas- ¨ sification datasets,” in 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019, pp. 1–8.
[179] Q. Fan, L. Brown, and J. Smith, “A closer look at faster R-CNN for vehicle detection,” in Proc. IEEE Intell. Veh. Symp., 2016, pp. 124–129
[180] Z. Fu, Y. Chen, H. Yong, R. Jiang, L. Zhang, and X. Hua, “Foreground gating and background refining network for surveillance object detection,” IEEE Trans. Image Process., vol. 28, no. 12, pp. 6077–6090, Dec. 2019
[181] A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? The kitti vision benchmark suite,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2012, pp. 3354–3361.
[182] X. Dai, “Hybridnet: A fast vehicle detection system for autonomous driving,” Signal Process.: Image Commun., vol. 70, pp. 79–88, 2019.
[183] P. F. Jaeger et al., “Retina u-net: Embarrassingly simple exploitation of segmentation supervision for medical object detection,” Mach. Learn. Health Workshop NeurIPS, 2019.
[184] S.-G. Lee, J. S. Bae, H. Kim, J. H. Kim, and S. Yoon, “Liver lesion detection from weakly-labeled multi-phase ct volumes with a grouped single shot multibox detector,” in Proc. Int. Conf. Med. Image Comput. Comput. Assisted Interv., 2018, pp. 693–701
[185] A. Shrivastava, A. Gupta, and R. Girshick, “Training regionbased object detectors with online hard example mining,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2016, pp. 761–769.
[186] W. Ouyang, X. Wang, C. Zhang, and X. Yang, “Factors in finetuning deep model for object detection with long-tail distribution,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2016, pp. 864–873.
[187] T. Lin, P. Dollar, R. B. Girshick, K. He, B. Hariharan, and S. J. Belongie, “Feature pyramid networks for object detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2017, pp. 936–944.
[188] B. Singh and L. S. Davis, “An analysis of scale invariance in object detection - snip,” in Proc. Conf. Comput. Vis. Pattern Recognit., 2018. pp. 3578–3587.
[189] B. Singh, M. Najibi, and L. S. Davis, “Sniper: Efficient multi-scale training,” in Proc. Int. Conf. Neural Inf. Process. Syst., 2018, pp. 9333–9343.
[190] J. Pang, K. Chen, J. Shi, H. Feng, W. Ouyang, and D. Lin, “Libra R-CNN: Towards balanced learning for object detection,” in Prco. IEEE Conf. Comput. Vis. Pattern Recognit., 2019, pp. 821–830.
[191] Y. Cao, K. Chen, C. C. Loy, and D. Lin, “Prime Sample Attention in Object Detection,” 2019, arXiv:1904.04821.
[192] Doyle, T. L., Newton, R. U., & Burnett, A. F. (2005). Reliability of traditional and fractal dimension measures of quiet stance center of pressure in young, healthy people. Archives of physical medicine and rehabilitation, 86(10), 2034-2040.
[193] Fuss, F. K. (2013). A robust algorithm for optimisation and customisation of fractal dimensions of time series modified by nonlinearly scaling their time derivatives: Mathematical theory and practical applications. Computational and Mathematical Methods in Medicine, 2013.
[194] Hu, X., Zhao, J., Peng, D., Sun, Z., & Qu, X. (2018). Estimation of foot plantar center of pressure trajectories with low-cost instrumented insoles using an individual-specific nonlinear model. Sensors, 18(2), 421.
[195] Ko, M., Hughes, L., & Lewis, H. (2012). Walking speed and peak plantar pressure distribution during barefoot walking in persons with diabetes. Physiotherapy Research International, 17(1), 29-35.
[196] Wearing, S., Hooper, S., Dubois, P., Smeathers, J., & Dietze, A. (2014). Force-deformation properties of the human heel pad during barefoot walking. Medicine and science in sports and exercise, 46(8), 1588-1594.
[197] Gordon, K. E., & Ferris, D. P. (2007). Learning to walk with a robotic ankle exoskeleton. Journal of biomechanics, 40(12), 2636-2644.
[198] Liu, T., Inoue, Y., & Shibata, K. (2007). Wearable force sensor with parallel structure for measurement of ground-reaction force. Measurement, 40(6), 644-653.