Wearable IoT and Artificial Intelligence Techniques for Leveraging the Human Activity Analysis

[1] A. Khanna and S. Kaur, “Internet of Things (IoT) Applications and Challenges: A Comprehensive Review”, Wirel. Pers. Commun., vol. 114, pp. 1–76, Sep. 2020. KhannaA. KaurS. Internet of Things (IoT) Applications and Challenges: A Comprehensive Review Wirel. Pers. Commun. 114 1 76 Sep. 2020 Search in Google Scholar

[2] Vikram Rajpoot; Sudeep Ray Gaur; Aditya Patel; Akash Saxena, “Internet of Things for Human-Activity Recognition Based on Wearable Sensor Data,” in Sensor Data Analysis and Management: The Role of Deep Learning, IEEE, 2021, pp.19–31, doi: 10.1002/9781119682806.ch2 . RajpootVikram Ray GaurSudeep PatelAditya SaxenaAkash “Internet of Things for Human-Activity Recognition Based on Wearable Sensor Data,” in Sensor Data Analysis and Management: The Role of Deep Learning IEEE 2021 19 31 10.1002/9781119682806.ch2 Open DOI

[3] Paraschiakos S., Cachucho R., Moed M., van Heemst D., Mooijaart S., Slagboom E. P., Knobbe A., and Beekman M., Activity recognition using wearable sensors for tracking the elderly, User Modeling and User-Adapted Interaction. (2020) 30, no. 3, 567–605, https://doi.org/10.1007/s11257-020-09268-2. ParaschiakosS. CachuchoR. MoedM. van HeemstD. MooijaartS. SlagboomE. P. KnobbeA. BeekmanM. Activity recognition using wearable sensors for tracking the elderly User Modeling and User-Adapted Interaction 2020 30 3 567 605 https://doi.org/10.1007/s11257-020-09268-2 Search in Google Scholar

[4] Dian, F. & Vahidnia, Reza & Rahmati, Alireza. (2020). Wearables and the Internet of Things (IoT), Applications, Opportunities, and Challenges: A Survey. IEEE Access. PP. 1–1. 10.1109/ACCESS.2020.2986329. DianF. VahidniaReza RahmatiAlireza 2020 Wearables and the Internet of Things (IoT), Applications, Opportunities, and Challenges: A Survey IEEE Access 1 1 10.1109/ACCESS.2020.2986329 Open DOISearch in Google Scholar

[5] Mukhopadhyay SC, Suryadevara NK, Nag A. Wearable Sensors and Systems in the IoT. Sensors. 2021; 21(23):7880. https://doi.org/10.3390/s21237880 MukhopadhyaySC SuryadevaraNK NagA. Wearable Sensors and Systems in the IoT Sensors 2021 21 23 7880 https://doi.org/10.3390/s21237880 Search in Google Scholar

[6] Rahmani, Amir & Szu-Han, Wang & Yu-Hsuan, Kang & Haghparast, Majid. (2022). The Internet of Things for Applications in Wearable Technology. IEEE Access. PP. 1–1. 10.1109/ACCESS.2022.3224487. RahmaniAmir Szu-HanWang Yu-HsuanKang HaghparastMajid 2022 The Internet of Things for Applications in Wearable Technology IEEE Access 1 1 10.1109/ACCESS.2022.3224487 Open DOISearch in Google Scholar

[7] G. S. Mubibya and J. Almhana, “Improving Human Activity Recognition using ML and Wearable Sensors,” ICC 2022 - IEEE International Conference on Communications, Seoul, Korea, Republic of, 2022, pp. 165–170, doi: 10.1109/ICC45855.2022.9839267. MubibyaG. S. AlmhanaJ. Improving Human Activity Recognition using ML and Wearable Sensors ICC 2022 - IEEE International Conference on Communications Seoul, Korea, Republic of 2022 165 170 10.1109/ICC45855.2022.9839267 Open DOISearch in Google Scholar

[8] A. Subasi, K. Khateeb, T. Brahimi and A. Sarirete, “Human activity recognition using machine learning methods in a smart healthcare environment” in Innovation in Health Informatics, Elsevier, pp. 123–144, 2020. SubasiA. KhateebK. BrahimiT. SarireteA. Human activity recognition using machine learning methods in a smart healthcare environment Innovation in Health Informatics Elsevier 123 144 2020 Search in Google Scholar

[9] D. Radivojević, N. Mirkov and S. Maletić, “Human Activity Recognition based on Machine Learning Classification of Smartwatch Accelerometer Dataset”, FME Trans., vol. 49, pp. 225–232, Jan. 2021. RadivojevićD. MirkovN. MaletićS. Human Activity Recognition based on Machine Learning Classification of Smartwatch Accelerometer Dataset FME Trans. 49 225 232 Jan. 2021 Search in Google Scholar

[10] Sabry, F., Eltaras, T., Labda, W., Alzoubi, K., & Malluhi, Q. (2022). Machine Learning for Healthcare Wearable Devices: The Big Picture. Journal of healthcare engineering, 2022, 4653923. https://doi.org/10.1155/2022/4653923 SabryF. EltarasT. LabdaW. AlzoubiK. MalluhiQ. 2022 Machine Learning for Healthcare Wearable Devices: The Big Picture Journal of healthcare engineering 2022 4653923 https://doi.org/10.1155/2022/4653923 Search in Google Scholar

[11] Mekruksavanich S. and Jitpattanakul A., LSTM networks using smartphone data for sensor-based human activity recognition in smart homes, Sensors. (2021) 21, no. 5. MekruksavanichS. JitpattanakulA. LSTM networks using smartphone data for sensor-based human activity recognition in smart homes Sensors 2021 21 5 Search in Google Scholar

[12] Xinyu Song, Hongyu Sun, Yanhua Dong, Ying Pei, and Shuai Wang. 2024. Deep Learning-Based Wearable Human Activity Recognition: Model and Performance Analysis. In Proceedings of the 2024 8th International Conference on Control Engineering and Artificial Intelligence (CCEAI ‘24). Association for Computing Machinery, New York, NY, USA, 30–36. https://doi.org/10.1145/3640824.3640830 SongXinyu SunHongyu DongYanhua PeiYing WangShuai 2024 Deep Learning-Based Wearable Human Activity Recognition: Model and Performance Analysis In Proceedings of the 2024 8th International Conference on Control Engineering and Artificial Intelligence (CCEAI ‘24). Association for Computing Machinery New York, NY, USA 30 36 https://doi.org/10.1145/3640824.3640830 Search in Google Scholar

[13] Alarfaj, M., Al Madini, A., Alsafran, A., Farag, M., Chtourou, S., Afifi, A., Ahmad, A., Al Rubayyi, O., Al Harbi, A., & Al Thunaian, M. (2024). Wearable sensors based on artificial intelligence models for human activity recognition. Frontiers in Artificial Intelligence, 7. https://doi.org/10.3389/frai.2024.1424190. AlarfajM. Al MadiniA. AlsafranA. FaragM. ChtourouS. AfifiA. AhmadA. Al RubayyiO. Al HarbiA. Al ThunaianM. 2024 Wearable sensors based on artificial intelligence models for human activity recognition Frontiers in Artificial Intelligence 7 https://doi.org/10.3389/frai.2024.1424190 Search in Google Scholar

[14] Alanazi, M., Aldahr, R.S. and Ilyas, M. 2024. Human Activity Recognition through Smartphone Inertial Sensors with ML Approach. Engineering, Technology & Applied Science Research. 14, 1 (Feb. 2024), 12780–12787. DOI: https://doi.org/10.48084/etasr.6586. AlanaziM. AldahrR.S. IlyasM. 2024 Human Activity Recognition through Smartphone Inertial Sensors with ML Approach Engineering, Technology & Applied Science Research 14 1 (Feb. 2024) 12780 12787 https://doi.org/10.48084/etasr.6586 Search in Google Scholar

[15] Raj, R., Kos, A. An improved human activity recognition technique based on convolutional neural network. Sci Rep 13, 22581 (2023). https://doi.org/10.1038/s41598-023-49739-1. RajR. KosA. An improved human activity recognition technique based on convolutional neural network Sci Rep 13 22581 2023 https://doi.org/10.1038/s41598-023-49739-1 Search in Google Scholar

[16] Nia, Nafiseh & Amiri, Amin & Nasab, Ahad & Kaplanoglu, Erkan & Liang, Yu. (2023). The Power of ANN-Random Forest Algorithm in Human Activities Recognition Using IMU Data. 1–7. 10.1109/BHI58575.2023.10313507. NiaNafiseh AmiriAmin NasabAhad KaplanogluErkan LiangYu 2023 The Power of ANN-Random Forest Algorithm in Human Activities Recognition Using IMU Data 1 7 10.1109/BHI58575.2023.10313507 Open DOISearch in Google Scholar

[17] Li, Y., & Wang, L. (2022). Human activity recognition based on residual network and BiLSTM. Sensors, 22(2), 635. LiY. WangL. 2022 Human activity recognition based on residual network and BiLSTM Sensors 22 2 635 Search in Google Scholar

[18] F. Nazari, D. Nahavandi, N. Mohajer and A. Khosravi, “Comparison of Deep Learning Techniques on Human Activity Recognition using Ankle Inertial Signals,” 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Prague, Czech Republic, 2022, pp. 2251–2256, doi: 10.1109/SMC53654.2022.9945472. NazariF. NahavandiD. MohajerN. KhosraviA. Comparison of Deep Learning Techniques on Human Activity Recognition using Ankle Inertial Signals 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Prague, Czech Republic 2022 2251 2256 10.1109/SMC53654.2022.9945472 Open DOISearch in Google Scholar

[19] Uddin, M. Z., & Soylu, A. (2021). Human activity recognition using wearable sensors, discriminant analysis, and long short-term memory-based neural structured learning. Scientific reports, 11(1), 16455. https://doi.org/10.1038/s41598-021-95947-y. UddinM. Z. SoyluA. 2021 Human activity recognition using wearable sensors, discriminant analysis, and long short-term memory-based neural structured learning Scientific reports 11 1 16455 https://doi.org/10.1038/s41598-021-95947-y Search in Google Scholar

[20] Wang, H., Kadry, S. N., & Raj, E. D. (2020). Continuous health monitoring of sportsperson using IoT devices based wearable technology. Computer Communications, 160, 588–595. https://doi.org/10.1016/j.comcom.2020.04.025. WangH. KadryS. N. RajE. D. 2020 Continuous health monitoring of sportsperson using IoT devices based wearable technology Computer Communications 160 588 595 https://doi.org/10.1016/j.comcom.2020.04.025 Search in Google Scholar

[21] Mahmud, T., Sayyed, A. S., Fattah, S. A., & Kung, S. Y. (2020). A novel multi-stage training approach for human activity recognition from multimodal wearable sensor data using deep neural network. IEEE Sensors Journal, 21(2), 1715–1726. MahmudT. SayyedA. S. FattahS. A. KungS. Y. 2020 A novel multi-stage training approach for human activity recognition from multimodal wearable sensor data using deep neural network IEEE Sensors Journal 21 2 1715 1726 Search in Google Scholar

[22] Hou, C. (2020). A study on IMU-Based Human Activity Recognition Using Deep Learning and Traditional Machine Learning. 2020 5th International Conference on Computer and Communication Systems (ICCCS), 225–234. HouC. 2020 A study on IMU-Based Human Activity Recognition Using Deep Learning and Traditional Machine Learning 2020 5th International Conference on Computer and Communication Systems (ICCCS) 225 234 Search in Google Scholar

[23] Seelwal, P., Prasad, A. N., Srinivas, C., & Rekha, V. S. (2023). Human activity recognition using WISDM datasets. Journal of Online Engineering Education, 14(1s), 88. SeelwalP. PrasadA. N. SrinivasC. RekhaV. S. 2023 Human activity recognition using WISDM datasets Journal of Online Engineering Education 14 1s 88 Search in Google Scholar