Deep Human Motion Detection and Multi-Features Analysis for Smart Healthcare Learning Tools
Open accessAbstract
Unhealthy lifestyle causes several chronic diseases in humans. Many products are introduced to avoid such illnesses and provide e-learning-based healthcare services. However, the main focus is still on providing comfortable and reliable solutions. Inertial measurement units (IMU) are considered as the most independent and non-intrusive way to monitor and analyze human health via motion patterns detection. Deep learning is also taken as an excellent tool to detect motion patterns from IMU data. In this paper, a deep-learning-based human motion detection approach for smart healthcare learning tool has been proposed. A novel hybrid descriptors-based pre-classification and multi-features analysis algorithm is proposed to classify the human motion for healthcare e-learning. For pre-processing, a quaternion-based filter is utilized to filter the IMU signals. An experiment is performed over the acceleration signals by using minimum and average gravity removal techniques. Next, signal segmentation of multiple time intervals has been applied to segment data and ultimately compare the results to decide which type provides better performance. Then, pre-classification is done using motion pattern identification in the form of active and passive patterns. During the features analysis phase, features have been extracted based on both active and passive motion patterns. Further, an orthogonal fuzzy neighborhood discriminant analysis technique has been used to reduce the dimensionality of the extracted feature vector. Finally, a deep learner known as long-short term memory has been applied to classify the actions of both active and passive motion features for healthcare e-learning systems. For this purpose, we utilized two datasets: REALDISP and wearable computing. The experimental results show that our proposed system for smart healthcare learning outperformed other state-of-the-art systems. The proposed implemented system provided 87.35% accuracy for REALDISP and 85.18% accuracy for wearable computing datasets. Furthermore, the classified motion patterns are provided to a smart healthcare advisor in order to provide live feedback about human health for immediate action.
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The proposed system's mean accuracy rate of 87.0% is higher than traditional methods proposed in the literature.
“The mean accuracy rate of 87.0% is higher than the traditional methods proposed in the literature.”
From A Multimodal IoT-Based Locomotion Classification System Using Features Engineering and Recursive Neural Network (2023) · cites this paper as [38]
Cites
Estimation of IMU and MARG orientation using a gradient descent algorithm
Madgwick, S., Harrison, A. J., & Vaidyanathan, R. (2011) · International Conference on Rehabilitation Robotics · doi:10.1109/icorr.2011.5975346 · Cited by 2,108 · see citing works
Ultrahigh sensitivity magnetic field and magnetization measurements with an atomic magnetometer
Dang, H. B., Maloof, A. C., & Romalis, M. V. (2010) · Applied Physics Letters · doi:10.1063/1.3491215 · Cited by 891 · see citing works
Qiu, S., Zhao, H., Jiang, N., Wang, Z., Liu, L., An, Y., Zhao, H., Miao, X., Liu, R., & Fortino, G. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.11.006 · Cited by 671 · see citing works
Robust human activity recognition from depth video using spatiotemporal multi-fused features
Jalal, A., Kim, Y.-H., Kim, Y.-J., Kamal, S., & Kim, D. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.08.003 · Cited by 392 · see citing works
Self-citationAn intelligent healthcare monitoring framework using wearable sensors and social networking data
Ali, F., El-Sappagh, S., Islam, S. M. R., Ali, A., Attique, M., Imran, M., & Kwak, K.-S. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.07.047 · Cited by 355 · see citing works
Spatio-Temporal Attention-Based LSTM Networks for 3D Action Recognition and Detection
Song, S., Lan, C., Xing, J., Zeng, W., & Liu, J. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2818328 · Cited by 261 · see citing works
Mekruksavanich, S., & Jitpattanakul, A. (2021) · Electronics · doi:10.3390/electronics10030308 · Cited by 229 · see citing works
Gholamiangonabadi, D., Kiselov, N., & Grolinger, K. (2020) · IEEE Access · doi:10.1109/access.2020.3010715 · Cited by 202 · see citing works
Wavelet speech enhancement based on the Teager energy operator
Bahoura, M., & Rouat, J. (2001) · IEEE Signal Processing Letters · doi:10.1109/97.889636 · Cited by 195 · see citing works
Quaid, M. A. K., & Jalal, A. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08463-7 · Cited by 185 · see citing works
Self-citationZdravevski, E., Lameski, P., Trajkovik, V., Kulakov, A., Chorbev, I., Goleva, R., Pombo, N. G. C. C., & García, N. M. (2017) · IEEE Access · doi:10.1109/access.2017.2684913 · Cited by 172 · see citing works
Awais, M., Raza, M., Singh, N. K., Bashir, K., Manzoor, U., Islam, S. U., & Rodrigues, J. J. P. C. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3044031 · Cited by 166 · see citing works
A benchmark dataset to evaluate sensor displacement in activity recognition
Baños, O., Damas, M., Pomares, H., Rojas, I., Tóth, M. A., & Amft, O. (2012) · Proceedings of the 2012 ACM Conference on Ubiquitous Computing · doi:10.1145/2370216.2370437 · Cited by 163 · see citing works
Renaudin, V., & Combettes, C. (2014) · Sensors · doi:10.3390/s141222864 · Cited by 158 · see citing works
Wearable Computing: Accelerometers’ Data Classification of Body Postures and Movements
Ugulino, W. C., Cardador, D., Vega, K. C., Velloso, E., Milidiú, R. L., & Fukś, H. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34459-6_6 · Cited by 150 · see citing works
Tang, Y., Teng, Q., Zhang, L., Min, F., & He, J. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3015521 · Cited by 143 · see citing works
Mahmood, M., Jalal, A., & Kim, K. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08527-8 · Cited by 131 · see citing works
Self-citationShallow Convolutional Neural Networks for Human Activity Recognition Using Wearable Sensors
Huang, W., Zhang, L., Gao, W., Min, F., & He, J. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3091990 · Cited by 129 · see citing works
Jalal, A., Quaid, M. A. K., Tahir, S. B. U. D., et al. (2020) · Sensors · doi:10.3390/s20226670 · Cited by 120 · see citing works
Self-citationAccurate and Reliable Gait Cycle Detection in Parkinson's Disease
Hundza, S. R., Hook, W. R., Harris, C. R., Mahajan, S. V., Leslie, P., Spani, C., Spalteholz, L. G., Birch, B., Commandeur, D. T., & Livingston, N. J. (2013) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2013.2282080 · Cited by 119 · see citing works
Tahir, S. B. U. D., Jalal, A., & Kim, K. (2020) · Entropy · doi:10.3390/e22050579 · Cited by 118 · see citing works
Self-citationOrthogonal Fuzzy Neighborhood Discriminant Analysis for Multifunction Myoelectric Hand Control
Khushaba, R. N., Al-Ani, A., & Al-Jumaily, A. A. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2009.2039480 · Cited by 110 · see citing works
Telemonitoring of Daily Activity Using Accelerometer and Gyroscope in Smart Home Environments
Batool, M., Jalal, A., & Kim, K. (2020) · Journal of Electrical Engineering and Technology · doi:10.1007/s42835-020-00554-y · Cited by 110 · see citing works
Self-citationImage Denoising Using a Compressive Sensing Approach Based on Regularization Constraints
Mahdaoui, A. E., Ouahabi, A., & Moulay, M. S. (2022) · Sensors · doi:10.3390/s22062199 · Cited by 108 · see citing works
Characterization of Various IMU Error Sources and the Effect on Navigation Performance
Flenniken, W. S., Wall, J. H., & Bevly, D. M. (2005) · Cited by 107 · see citing works
Recognition of Human Home Activities via Depth Silhouettes and ℜ Transformation for Smart Homes
Jalal, A., Uddin, M. Z., Kim, J. T., et al. (2011) · Indoor and Built Environment · doi:10.1177/1420326x11423163 · Cited by 103 · see citing works
Self-citationAdaptive Body Posture Analysis for Elderly-Falling Detection with Multisensors
Lai, C., Huang, Y., Park, J. H., et al. (2010) · IEEE Intelligent Systems · doi:10.1109/mis.2010.39 · Cited by 89 · see citing works
Self-citationJalal, A., Batool, M., & Kim, K. (2020) · Sustainability · doi:10.3390/su122410324 · Cited by 86 · see citing works
Self-citationTahir, S. B. U. D., Jalal, A., & Batool, M. (2020) · International Conference on Advancements in Computational Sciences (ICACS) · doi:10.1109/icacs47775.2020.9055944 · Cited by 84 · see citing works
Self-citationCheng, X., Zhang, L., Tang, Y., Liu, Y., Wu, H., & He, J. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3149337 · Cited by 84 · see citing works
Javeed, M., Jalal, A., & Kim, K. (2021) · International Bhurban Conference on Applied Sciences and Technology (IBCAST) · doi:10.1109/ibcast51254.2021.9393014 · Cited by 83 · see citing works
Self-citationCelik, Y., Stuart, S., Woo, W., Sejdic, E., & Godfrey, A. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.09.016 · Cited by 83 · see citing works
Wang, G., Li, Q., Wang, L., Wang, W., Wu, M., & Liu, T. (2018) · Sensors · doi:10.3390/s18061965 · Cited by 82 · see citing works
Multimodal Human Hand Motion Sensing and Analysis—A Review
Xue, Y., Ju, Z., Xiang, K., Chen, J., & Liu, H. (2018) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2018.2800167 · Cited by 76 · see citing works
Ansar, H., Jalal, A., Gochoo, M., & Kim, K. (2021) · Sustainability · doi:10.3390/su13052961 · Cited by 75 · see citing works
Self-citationDeep Human Activity Recognition With Localisation of Wearable Sensors
Lawal, I. A., & Bano, S. (2020) · IEEE Access · doi:10.1109/access.2020.3017681 · Cited by 70 · see citing works
Gochoo, M., Tahir, S. B. U. D., Jalal, A., & Kim, K. (2021) · IEEE Access · doi:10.1109/access.2021.3078513 · Cited by 64 · see citing works
Self-citationJaveed, M., Gochoo, M., Jalal, A., & Kim, K. (2021) · Sustainability · doi:10.3390/su13041699 · Cited by 60 · see citing works
Self-citationAkhter, I., Jalal, A., & Kim, K. (2021) · Journal of Electrical Engineering and Technology · doi:10.1007/s42835-021-00756-y · Cited by 60 · see citing works
Self-citationIssa, M., Helmi, A. M., Al‐qaness, M. A. A., Dahou, A., Elaziz, M. A., & Damaševičius, R. (2022) · Healthcare · doi:10.3390/healthcare10061084 · Cited by 55 · see citing works
Comparative Study of Wearable Sensors, Video, and Handwriting to Detect Parkinson’s Disease
Talitckii, A., Kovalenko, E., Shcherbak, A., Anikina, A., Витальевна, Б. Е., Zimniakova, O., Semenov, M., Dylov, D. V., & Somov, A. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3176898 · Cited by 49 · see citing works
A Wearable Activity Recognition Device Using Air-Pressure and IMU Sensors
Yang, D., Huang, J., Tu, X., Ding, G., Shen, T., & Xiao, X. (2018) · IEEE Access · doi:10.1109/access.2018.2890004 · Cited by 46 · see citing works
Patalas‐Maliszewska, J., Halikowski, D., & Damaševičius, R. (2021) · Electronics · doi:10.3390/electronics10232946 · Cited by 43 · see citing works
Xia, S., Chu, L., Pei, L., Zhang, Z., Yu, W., & Qiu, R. C. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3111996 · Cited by 40 · see citing works
Mäkelä, S.-M., Lämsä, A., Keränen, J. S., Liikka, J., Ronkainen, J. K., Peltola, J., Häikiö, J., Järvinen, S., & López, M. B. (2021) · Sustainability · doi:10.3390/su14010220 · Cited by 40 · see citing works
An Efficient Human Activity Recognition Framework Based on Wearable IMU Wrist Sensors
Ayman, A., Attallah, O., & Shaban, H. A. (2019) · IEEE International Conference on Imaging Systems and Techniques (IST) · doi:10.1109/ist48021.2019.9010115 · Cited by 39 · see citing works
Wearable Smart Health Advisors: An IMU-Enabled Posture Monitor
Πετρόπουλος, Α., Sikeridis, D., & Antonakopoulos, T. A. (2020) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2019.2956205 · Cited by 39 · see citing works
Subject- and Environment-Based Sensor Variability for Wearable Lower-Limb Assistive Devices
Krausz, N. E., Hu, B. H., & Hargrove, L. J. (2019) · Sensors · doi:10.3390/s19224887 · Cited by 38 · see citing works
Daily Locomotion Recognition and Prediction: A Kinematic Data-Based Machine Learning Approach
Figueiredo, J., Carvalho, S. P., Goncalve, D., Moreno, J. C., & Santos, C. P. (2020) · IEEE Access · doi:10.1109/access.2020.2971552 · Cited by 37 · see citing works
Friedrich, B., Lau, S., Elgert, L., Bauer, J. M., & Hein, A. (2021) · Healthcare · doi:10.3390/healthcare9020149 · Cited by 37 · see citing works
Ghadi, Y. Y., Javeed, M., Alarfaj, M., Shloul, T. A., Alsuhibany, S. A., Jalal, A., Kamal, S., & Kim, D. (2022) · IEEE Access · doi:10.1109/access.2022.3154775 · Cited by 36 · see citing works
Self-citationUsing Wrist-Worn Activity Recognition for Basketball Game Analysis
Hölzemann, A., & Laerhoven, K. V. (2018) · International Workshop on Sensor-based Activity Recognition and Interaction (iWOAR) · doi:10.1145/3266157.3266217 · Cited by 34 · see citing works
Khan, M. A., Arshad, H., Damaševičius, R., Alqahtani, A., Alsubai, S., Binbusayyis, A., Nam, Y., & Kang, B.-G. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/8238375 · Cited by 33 · see citing works
Student’s Health Exercise Recognition Tool for E-Learning Education
Shloul, T. A., Javeed, M., Gochoo, M., Alsuhibany, S. A., Ghadi, Y. Y., Jalal, A., & Park, J. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2023.026051 · Cited by 30 · see citing works
Self-citationGait Recognition for Lower Limb Exoskeletons Based on Interactive Information Fusion
Chen, W., Li, J., Zhu, S., Zhang, X., Men, Y., & Wu, H. (2022) · Applied Bionics and Biomechanics · doi:10.1155/2022/9933018 · Cited by 29 · see citing works
Human daily activity recognition by fusing accelerometer and multi-lead ECG data
Jia, R., & Liu, B. (2013) · doi:10.1109/icspcc.2013.6664056 · Cited by 28 · see citing works
A Multi-Sensor Wearable System for the Quantitative Assessment of Parkinson’s Disease
Zhang, H., Li, C., Liu, W., Wang, J., Zhou, J., & Wang, S. (2020) · Sensors · doi:10.3390/s20216146 · Cited by 24 · see citing works
The Synchrosqueezing transform for instantaneous spectral analysis
Thakur, G. (2015) · Applied and numerical harmonic analysis · doi:10.1007/978-3-319-20188-7_15 · Cited by 22 · see citing works
Badawi, A. A., Al-Kabbany, A., & Shaban, H. A. (2020) · Journal of Healthcare Engineering · doi:10.1155/2020/7914649 · Cited by 21 · see citing works
Javeed, M., & Jalal, A. (2021) · International Conference on Innovative Computing (ICIC) · doi:10.1109/icic53490.2021.9692924 · Cited by 21 · see citing works
Self-citationAn Ontology-Based E-Learning Framework for Healthcare Human Resource Management*
Băjenaru, L., Smeureanu, I., & Balog, A. (2016) · Studies in Informatics and Control · doi:10.24846/v25i1y201611 · Cited by 14 · see citing works
Li, C., Su, Z., Li, Q. C., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2891512 · Cited by 9 · see citing works
FootbSense: Soccer Moves Identification Using a Single IMU
Kondo, Y., Ishii, S., Aoyagi, H., et al. (2022) · Smart innovation, systems and technologies · doi:10.1007/978-981-19-0361-8_7 · Cited by 8 · see citing works
Uddin, M. Z., & Kim, T. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2225-6 · Cited by 6 · see citing works
Spectral Roll-off Points Variations: Exploring Useful Information in Feature Maps by Its VariationsPreprint
Yu, Y., You, Y., Yang, Z., Liu, G., Li, P., Yang, Z., & Shan, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.00369 · Cited by 3 · see citing works
Har Enhanced Weakly-Supervised Semantic Segmentation Coupled with Adversarial Learning
Ma, L., Liu, Z., Zheng, N., & Wang, J. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803111 · Cited by 1 · see citing works
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Javeed, M., Mudawi, N. A., Alabduallah, B. I., Jalal, A., & Kim, W. (2023) · Sensors · doi:10.3390/s23104716 · Cited by 18 · see citing works
Self-citation
- Authors
- Hajjej, F., Javeed, M., Ksibi, A., Alarfaj, M., Alnowaiser, K., Jalal, A., Alsufyani, N., Shorfuzzaman, M., & Park, J.
- Affiliations
- Princess Nourah bint Abdulrahman University (SA)
- Air University (PK)
- King Faisal University (SA)
- Prince Sattam Bin Abdulaziz University (SA)
- Taif University (SA)
- Tech University of Korea (KR)
- ORCID
- Venue
- IEEE Access
- Published
- 2022
- Volume
- 10
- Pages
- 116527-116539
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4312947958
- Cited by
- 16
- References
- 66
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 7 · 2023: 4
- FWCI
- 1.15
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Funders
- MSIT (Ministry of Science and ICT), Korea, under the ITRC (IITP-2023-2018-0-01426)
- IITP
- Princess Nourah bint Abdulrahman University Researchers Supporting Project (PNURSP2022R236)
- Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
- Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia (1452)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Keywords
- human motion detection, inertial measurement unit, wearable sensors, smart healthcare, motion pattern classification, signal segmentation, dimensionality reduction, long short-term memory
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