DOI : https://doi.org/10.18517/ijaseit.13.6.19053

Human Age Group Estimation Using Gait Features

Qian Fu Soo (1), Tee Connie (2), Michael Kah Ong Goh (3)
(1) Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450, Melaka, Malaysia
(2) Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450, Melaka, Malaysia
(3) Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450, Melaka, Malaysia
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How to cite (IJASEIT) :
Soo, Qian Fu, et al. “Human Age Group Estimation Using Gait Features”. International Journal on Advanced Science, Engineering and Information Technology, vol. 13, no. 6, Dec. 2023, pp. 2314-27, doi:10.18517/ijaseit.13.6.19053.
Citation Format :
In many practical applications, identifying the target age group is essential for marketing products and services. For instance, gaming and entertainment companies need to understand which age groups are most likely to purchase their services. This knowledge allows them to optimize their products and services to better cater to their target audience. This study proposes an age group prediction system using gait features. Gait, in this context, pertains to an individual's unique walking style. A diverse dataset containing subjects from 3 to 70 years old is collected. The age group is classified into three categories: child, adult, and senior. The critical aspect of this research lies in the preprocessing techniques applied to the gait patterns. The gait patterns are extracted from landmark human joint positions' key point values and preprocessed using smoothening techniques. Additionally, dimension reduction techniques enhance computational efficiency and accuracy before feeding the features into a deep learning-based classifier. These preprocessing steps play a pivotal role in the success of the deep learning-based classifier. A promising accuracy of up to 95% is reported for correctly recognizing the human age groups. The outcomes of this investigation underscore the tremendous potential of leveraging machine learning techniques to refine marketing strategies and boost customer satisfaction. The proposed approach can aid companies in aligning their products and services with the preferences and needs of distinct age groups, thereby enhancing their market presence and resonance with their target audience.

Y. F. Ti, T. Connie, and M. K. O. Goh, “GenReGait: Gender Recognition using Gait Features,” Journal of Informatics and Web Engineering, vol. 2, no. 2, pp. 129-140, Sep. 2023, doi: 10.33093/jiwe.2023.2.2.10.

T. B. Aderinola, T. Connie, T. S. Ong, W.-C. Yau, and A. B. J. Teoh, “Learning Age From Gait: A Survey,” IEEE Access, vol. 9, pp. 100352-100368, 2021, doi: 10.1109/ACCESS.2021.3095477.

N. Li and X. Zhao, “A Strong and Robust Skeleton-Based Gait Recognition Method with Gait Periodicity Priors,” IEEE Trans Multimedia, vol. 25, pp. 3046-3058, 2023, doi: 10.1109/TMM.2022.3154609.

H. Chao, K. Wang, Y. He, J. Zhang, and J. Feng, “GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set,” IEEE Trans Pattern Anal Mach Intell, pp. 1-1, 2021, doi: 10.1109/TPAMI.2021.3057879.

C. Song, Y. Huang, W. Wang, and L. Wang, “CASIA-E: A Large Comprehensive Dataset for Gait Recognition,” IEEE Trans Pattern Anal Mach Intell, pp. 1-16, 2022, doi: 10.1109/TPAMI.2022.3183288.

Z. Zhang, L. Tran, F. Liu, and X. Liu, “On Learning Disentangled Representations for Gait Recognition,” IEEE Trans Pattern Anal Mach Intell, vol. 44, no. 1, pp. 345-360, Jan. 2022, doi: 10.1109/TPAMI.2020.2998790.

I. Hagoort, N. Vuillerme, T. Hortobí¡gyi, and C. J. C. Lamoth, “Age and walking conditions differently affect domains of gait,” Hum Mov Sci, vol. 89, p. 103075, Jun. 2023, doi: 10.1016/j.humov.2023.103075.

A. Macie, T. Matson, and A. Schinkel-Ivy, “Age affects the relationships between kinematics and postural stability during gait,” Gait Posture, vol. 102, pp. 86-92, May 2023, doi: 10.1016/j.gaitpost.2023.03.004.

K. A. Boyer et al., “Age-related changes in gait biomechanics and their impact on the metabolic cost of walking: Report from a National Institute on Aging workshop,” Exp Gerontol, vol. 173, p. 112102, Mar. 2023, doi: 10.1016/j.exger.2023.112102.

O. Jayakody et al., “Age-related changes in gait domains: Results from the LonGenity study,” Gait Posture, vol. 100, pp. 8-13, Feb. 2023, doi: 10.1016/j.gaitpost.2022.11.009.

O. Jayakody et al., “Age-related changes in gait domains: Results from the LonGenity study,” Gait Posture, vol. 100, pp. 8-13, Feb. 2023, doi: 10.1016/j.gaitpost.2022.11.009.

D. da S. F. de Campos, S. Shokur, A. C. de Lima-Pardini, M. Runfeng, M. Bouri, and D. B. Coelho, “Kinematics predictors of spatiotemporal parameters during gait differ by age in healthy individuals,” Gait Posture, vol. 96, pp. 216-220, Jul. 2022, doi: 10.1016/j.gaitpost.2022.05.034.

I. Hagoort, N. Vuillerme, T. Hortobí¡gyi, and C. J. Lamoth, “Outcome-dependent effects of walking speed and age on quantitative and qualitative gait measures,” Gait Posture, vol. 93, pp. 39-46, Mar. 2022, doi: 10.1016/j.gaitpost.2022.01.001.

D. Zhang, Y. Wang, and B. Bhanu, “Age Classification Base on Gait Using HMM,” in 2010 20th International Conference on Pattern Recognition, IEEE, Aug. 2010, pp. 3834-3837. doi: 10.1109/ICPR.2010.934.

Chang Yang and Wenyong Wang, “A novel age interval identification method based on Gait monitoring,” in 2015 4th International Conference on Computer Science and Network Technology (ICCSNT), IEEE, Dec. 2015, pp. 266-269. doi: 10.1109/ICCSNT.2015.7490749.

M. Nabila, A. I. Mohammed, and B. J. Yousra, “Gait”based human age classification using a silhouette model,” IET Biom, vol. 7, no. 2, pp. 116-124, Mar. 2018, doi: 10.1049/iet-bmt.2016.0176.

M. Hema*, K. Babulu, and N. Balaji, “Recognition of Gender using Gait Energy Image Projections Based on Random Forest Classifier,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 12, pp. 1518-1523, Oct. 2019, doi: 10.35940/ijitee.L3104.1081219.

Q. Riaz, M. Z. U. H. Hashmi, M. A. Hashmi, M. Shahzad, H. Errami, and A. Weber, “Move Your Body: Age Estimation Based on Chest Movement During Normal Walk,” IEEE Access, vol. 7, pp. 28510-28524, 2019, doi: 10.1109/ACCESS.2019.2901959.

C. Xu et al., “Real-Time Gait-Based Age Estimation and Gender Classification from a Single Image,” in 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE, Jan. 2021, pp. 3459-3469. doi: 10.1109/WACV48630.2021.00350.

K. Saho, K. Shioiri, M. Fujimoto, and Y. Kobayashi, “Micro-Doppler Radar Gait Measurement to Detect Age- and Fall Risk-Related Differences in Gait: A Simulation Study on Comparison of Deep Learning and Gait Parameter-Based Approaches,” IEEE Access, vol. 9, pp. 18518-18526, 2021, doi: 10.1109/ACCESS.2021.3053298.

O. Costilla-Reyes, P. Scully, I. Leroi, and K. B. Ozanyan, “Age-Related Differences in Healthy Adults Walking Patterns Under a Cognitive Task With Deep Neural Networks,” IEEE Sens J, vol. 21, no. 2, pp. 2353-2363, Jan. 2021, doi: 10.1109/JSEN.2020.3021349.

Y. Chen, R. Ou, Y. Deng, and X. Yin, “WIAGE: A Gait-based Age Estimation System Using Wireless Signals,” in 2021 IEEE Global Communications Conference (GLOBECOM), IEEE, Dec. 2021, pp. 01-06. doi: 10.1109/GLOBECOM46510.2021.9685336.

Y. Liu et al., “Application of Machine Vision in Classifying Gait Frailty Among Older Adults,” Front Aging Neurosci, vol. 13, Nov. 2021, doi: 10.3389/fnagi.2021.757823.

X. Lv, N. Ta, T. Chen, J. Zhao, and H. Wei, “Analysis of Gait Characteristics of Patients with Knee Arthritis Based on Human Posture Estimation,” Biomed Res Int, vol. 2022, pp. 1-8, Apr. 2022, doi: 10.1155/2022/7020804.

H.-S. Fang et al., “AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time,” Nov. 2022.

H. Ahmed and A. Ullah, “Exponential Moving Average Extended Kalman Filter for Robust Battery State-of-Charge Estimation,” in 2022 International Conference on Innovations in Science, Engineering and Technology (ICISET), IEEE, Feb. 2022, pp. 555-560. doi: 10.1109/ICISET54810.2022.9775853.

N. D. Nagahawatte, L. K. Cheng, R. Avci, L. R. Bear, and N. Paskaranandavadivel, “A generalized framework for pacing artifact removal using a Hampel filter,” in 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE, Jul. 2022, pp. 2009-2012. doi: 10.1109/EMBC48229.2022.9871096.

K. Hasan, M. M. Othman, S. T. Meraj, S. Mekhilef, and A. F. Bin Abidin, “Shunt Active Power Filter Based on Savitzky-Golay Filter: Pragmatic Modelling and Performance Validation,” IEEE Trans Power Electron, vol. 38, no. 7, pp. 8838-8850, Jul. 2023, doi: 10.1109/TPEL.2023.3258457.

S. Prakash, A. S. Jalal, and P. Pathak, “Forecasting COVID-19 Pandemic using Prophet, LSTM, hybrid GRU-LSTM, CNN-LSTM, Bi-LSTM and Stacked-LSTM for India,” in 2023 6th International Conference on Information Systems and Computer Networks (ISCON), IEEE, Mar. 2023, pp. 1-6. doi: 10.1109/ISCON57294.2023.10112065.

X. Wen and W. Li, “Time Series Prediction Based on LSTM-Attention-LSTM Model,” IEEE Access, vol. 11, pp. 48322-48331, 2023, doi: 10.1109/ACCESS.2023.3276628.

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