From Inertial to Ambient: A Systematic Review of Sensor Modalities and Data Acquisition Strategies in Human Activity Recognition

References

[1] Aggarwal, J.K., & Ryoo, M.S. (2011). Human activity analysis. ACM Computing Surveys (CSUR), 43, 1 - 43.

[2] Aggarwal, J. K., & Xia, L. (2014). Human activity recognition from 3D data: A review. Pattern Recognition Letters, 48, 70–80. https://

[3] Aguileta, A. A., Brena, R. F., Mayora, O., Molino-Minero-Re, E., & Trejo, L. A. (2019). Multi-sensor fusion for activity recognition— A survey. Sensors, 19(17), 3808. https://

[4] Anguita, D., Ghio, A., Oneto, L., Parra, X., & Reyes-Ortiz, J. L. (2013). A public domain dataset for human activity recognition using smartphones. In Proceedings of the 21st International European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2013), 437–442.

[5] Aouedi, O., Sacco, A., Khan, L. U., Nguyen, D. C., & Guizani, M. (2024). Federated learning for human activity recognition: Overview, advances, and challenges. IEEE Open Journal of the Communications Society, 5, 7341 –7367. https:// 28

[6] Arshad, M. H., Bilal, M., & Gani, A. (2022). Human Activity Recognition: Review, Taxonomy and Open Challenges. Sensors (Basel, Switzerland), 22(17), 6463. https://

[7] Atallah, L., Lo, B., King, R., & Yang, G.-Z. (2011). Sensor positioning for activity recognition using wearable accelerometers. IEEE Transactions on Biomedical Circuits and Systems, 5(4), 320 –329. https://

[8] Atrey, P. K., Hossain, M. A., El Saddik, A., & Kankanhalli, M. S. (2010). Multimodal fusion for multimedia analysis: A survey. Multimedia Systems, 16(6), 345–379.

[9] Banos, O., Galvez, J.-M., Damas, M., Pomares, H., & Rojas, I. (2014). Window Size Impact in Human Activity Recognition. Sensors, 14(4), 6474 -6499. https://

[10] Bao, L., & Intille, S. S. (2004). Activity recognition from user-annotated acceleration data. In International Conference on Pervasive Computing (pp. 1–17). Springer.

[11] Barua, A., Jiang, X., & Fuller, D. (2024). The effectiveness of simple heuristic features in sensor orientation and placement problems in human activity recognition using a single smartphone accelerometer. BioMedical Engineering OnLine, 23, 21. https://

[12] Beddiar, D. R., Nini, B., Sabokrou, M., & Hadid, A. (2020). Vision-based human activity recognition: A survey. Multimedia Tools and Applications, 79, 30509–30555. https://

[13] Bian, S., Liu, M., Zhou, B., & Lukowicz, P. (2022). The State-of-the-Art Sensing Techniques in Human Activity Recognition: A Survey. Sensors (Basel, Switzerland), 22(12), 4596. https://

[14] Bibbò, L., & Vellasco, M. M. B. R. (2023). Human Activity Recognition (HAR) in Healthcare. Applied Sciences, 13(24), 13009. https://

[15] Bibbò, L., & Serrano, A. (2025). Human Activity Recognition (HAR) in Healthcare, 2nd Edition. Applied Sciences, 15(10), 5762. https://

[16] Bouchabou, D., Nguyen, S. M., Lohr, C., Leduc, B., & Kanellos, I. (2021). A survey of human activity recognition in smart homes based on IoT sensors algorithms: Taxonomies, challenges, and opportunities with deep learning. Sensors, 21(18), 6037. https://

[17] Bulling, A., Blanke, U., & Schiele, B. (2014). A tutorial on human activity recognition using body-worn inertial sensors. ACM Computing Surveys, 46(3), Article 33. https://

[18] Charlton, P. H., Kotzen, K., Mejía-Mejía, E., Aston, P. J., Budidha, K., Mant, J., Pettit, C., Behar, J. A., & Kyriacou, P. A. (2022). Detecting beats in the photoplethysmogram: benchmarking open-source algorithms. Physiological measurement, 43(8), 085007. https://

[19] Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21, 6. https://

[20] Chung, S., Lim, J., Noh, K. J., Kim, G., & Jeong, H. (2019). Sensor data acquisition and multimodal sensor fusion for human activity recognition using deep learning. Sensors, 19(7), 1716. https://

[21] Contoli, C., Freschi, V., & Lattanzi, E. (2024). Energy-aware human activity recognition for wearable devices: A comprehensive review. Pervasive and Mobile Computing, 104, 101976. https://

[22] Dang, L. M., Min, K., Wang, H., Piran, M. J., Lee, C. H., & Moon, H. (2020). Sensor-based and vision-based human activity recognition: A comprehensive survey. Pattern Recognition, 108, Article 107561. https://

[23] Demrozi, F., Pravadelli, G., Bihorac, A., & Rashidi, P. (2020). Human activity recognition using inertial, physiological and environmental sensors: A comprehensive survey. IEEE Access, 8, 210816–210836. https:// 5

[24] Du, J., Gerdtman, C., & Lindén, M. (2018). Signal quality improvement algorithms for MEMS gyroscope-based human motion analysis systems: A systematic review. Sensors, 18 (4), 1123. https://

[25] Fernández-Cuevas, I., Bouzas Marins, J. C., Arnáiz Lastras, J., Gómez Carmona, P. M., Piñonosa Cano, S., García-Concepción, M. Á., & Sillero-Quintana, M. (2015). Classification of factors influencing the use of infrared thermography in humans: A review. Infrared Physics & Technology, 71, 28 –55. https://

[26] Ghapanchizadeh, H., Ahmad, S. A., Ishak, A. J., & Al-Quraishi, M. S. (2017). Review of surface electrode placement for recording electromyography signals. Biomedical Research, 2017(Special Issue: ComplexWorldofNeuroscience), S1–S7.

[27] Gomaa, W., & Khamis, M. A. (2023). A perspective on human activity recognition from inertial motion data. Neural Computing and Applications, 35, 20463 –20568. https://

[28] Gorce, P., & Jacquier-Bret, J. (2025). Fall Detection in Elderly People: A Systematic Review of Ambient Assisted Living and Smart Home -Related Technology Performance. Sensors (Basel, Switzerland), 25(21), 6540. https://

[29] Gosavi, D., Chavan, D., Kaware, A., Bhaemal, S., & [Remaining Author Name(s)]. (2026). Smart elderly activity recognition and fall detection using machine learning. International Journal for Research in Applied Science and Engineering Technology (IJRASET), 14(5), 1 –5.// 10.22214/ijraset.2026.82943

[30] Grataloup A and Kurpicz-Briki M (2024) A systematic survey on the application of federated learning in mental state detection and human activity recognition. Front. Digit. Health 6:1495999. 10.3389/fdgth.2024.1495999

[31] Guo, L., Sandsjö, L., Ortiz-Catalan, M., & Skrifvars, M. (2020). Systematic review of textile-based electrodes for long-term and continuous surface electromyography recording. Textile Research Journal, 90(2), 227–244. https://

[32] Han, J., Shao, L., Xu, D., & Shotton, J. (2013). Enhanced computer vision with Microsoft Kinect sensor: A review. IEEE Transactions on Cybernetics, 43(5), 1314– 1334. https://

[33] Hiremath, S. K., & Plötz, T. (2023). The lifespan of human activity recognition systems for smart homes. Sensors, 23(18), 7729. https://

[34] Janidarmian, M., Fekr, A. R., Radecka, K., & Zilic, Z. (2017). A comprehensive analysis on wearable acceleration sensors in human activity recognition. Sensors, 17(3), 529. https://

[35] Karayaneva, Y., Sharifzadeh, S., Jing, Y., & Tan, B. (2023). Human activity recognition for AI-enabled healthcare using low- resolution infrared sensor data. Sensors, 23(1), 478. https://

[36] Lara, Ó. D., & Labrador, M. A. (2013). A survey on human activity recognition using wearable sensors. IEEE Communications Surveys and Tutorials, 15(3), 1192–1209. https:// 0192

[37] Letts, E., Jakubowski, J. S., King-Dowling, S., Clevenger, K. A., Kobsar, D., & Obeid, J. (2024). Accelerometer techniques for capturing human movement validated against direct observation: A scoping review. Physiological Measurement, 45(7). https://

[38] Li, X., He, Y., & Jing, X. (2019). A survey of deep learning-based human activity recognition in radar. Remote Sensing, 11(9), 1068. https://

[39] Logacjov, A., Bach, K., Kongsvold, A., Bårdstu, H. B., & Mork, P. J. (2021). HARTH: A human activity recognition dataset for machine learning. Sensors, 21(23), 7853. https://

[40] Ma, Y., Zhou, G., & Wang, S. (2019). WiFi sensing with channel state information: A survey. ACM Computing Surveys (CSUR). https://

[41] Manivannan, A., Chin, W. C. B., Barrat, A., & Bouffanais, R. (2020). On the challenges and potential of using barometric sensors to track human activity. Sensors, 20(23), 6786. https://

[42] Mukhopadhyay, S. C. (2015). Wearable sensors for human activity monitoring: A review. IEEE Sensors Journal, 15(3), 1321- 1330. Article 6974987. https://

[43] Ntalampiras, S., Potamitis, I., & Fakotakis, N. (2011). Probabilistic novelty detection for acoustic surveillance under real-world conditions. IEEE Transactions on Multimedia, 13(4), 713 –719. https://

[44] Newaz, N. T., & Hanada, E. (2024). A low- resolution infrared array for unobtrusive human activity recognition that preserves privacy. Sensors, 24(3), 926. https://

[45] Niu, X., Li, Y., Zhang, H., Wang, Q., & Ban, Y. (2013). Fast thermal calibration of low- grade inertial sensors and inertial measurement units. Sensors, 13(9), 12192– 12217. https://

[46] Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012). Feature reduction and selection for EMG signal classification. Expert Systems with Applications, 39(8), 7420–7431. https://

[47] Qureshi, T. S., Shahid, M. H., Farhan, A. A., & Alamri, S. (2025). A systematic literature review on human activity recognition using smart devices: Advances, challenges, and future directions. Artificial Intelligence Review, 58, 276. https://

[48] Ramachandram, D., & Taylor, G. W. (2017). Deep multimodal learning: A survey on recent advances and trends. IEEE Signal Processing Magazine, 34(6), 96 –108. https://

[49] Ramamurthy, S. R., & Roy, N. (2018). Recent trends in machine learning based human activity recognition—A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), Article e1254.

[50] Reiss, A., & Stricker, D. (2012). Introducing a new benchmarked dataset for activity monitoring. Proceedings of the 2012 16th Annual International Symposium on Wearable Computers (ISWC), 108 –109. IEEE. https://

[51] Roggen, D., Calatroni, A., Nguyen-Dinh, L., Chavarriaga, R., & Sagha, H. (2010). OPPORTUNITY Activity Recognition [Dataset]. UCI Machine Learning Repository. https://

[52] Saha, U., Saha, S., Kabir, M. T., Fattah, S. A., & Saquib, M. (2024). Decoding human activities: Analyzing wearable accelerometer and gyroscope data for activity recognition. IEEE Sensors Letters, 8(8), 1 –4. https://

[53] Sanchez-Comas, A., Synnes, K., & Hallberg, J. (2020). Hardware for recognition of human activities: A review of smart home and AAL related technologies. Sensors, 20(14), 4035. https://

[54] Savvidou, F., Tegos, S. A., Diamantoulakis, P. D., & Karagiannidis, G. K. (2024). Passive radar sensing for human activity recognition: A survey. IEEE Open Journal of Engineering in Medicine and Biology, 5, 700–706. https://

[55] Sazonov, E. S., Fulk, G. D., Hill, J., Schutz, Y., & Browning, R. (2011). Monitoring of posture allocations and activities by a shoe- based wearable sensor. IEEE Transactions on Biomedical Engineering, 58(4), 983–990. https://

[56] Shin, J., Hassan, N., Miah, A. S. M., & Nishimura, S. (2025). A Comprehensive Methodological Survey of Human Activity Recognition Across Diverse Data Modalities. Sensors, 25(13), 4028. https://

[57] Soken, H. E. (2017). A survey of calibration algorithms for small satellite magnetometers. In 2017 IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace) (pp. 62 –67). IEEE. https:// .7999

[58] Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427–437. https://

[59] Straczkiewicz, M., James, P., & Onnela, J.-P. (2021). A systematic review of smartphone- based human activity recognition methods for health research. npj Digital Medicine, 4, 148. https://

[60] Sun, Z., Ke, Q., Rahmani, H., Bennamoun, M., Wang, G., & Liu, J. (2023). Human action recognition from various data modalities: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3), 3200–3225. https://

[61] Tamura, T., Maeda, Y., Sekine, M., & Yoshida, M. (2014). Wearable Photoplethysmographic Sensors—Past and Present. Electronics, 3(2), 282-302. https://

[62] Vaizman, Y., Ellis, K., & Lanckriet, G. R. (2017). Recognizing detailed human context in-the-wild from smartphones and smartwatches. IEEE Pervasive Computing, 16(4), 62 –74. https://

[63] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (pp. 5998–6008).

[64] Wang, Y., Cang, S., & Yu, H. (2019). A survey on wearable sensor modality centred human activity recognition in health care. Expert Systems with Applications, 137, 167– 190. https://

[65] Wang, J., Chen, Y., Hao, S., Peng, X., & Hu, L. (2019). Deep learning for sensor-based activity recognition: A survey. Pattern Recognition Letters, 119, 3 –11. https://

[66] Webber, M., Rojas, R. F.(2024). Human Activity Recognition With Accelerometer and Gyroscope: A Data Fusion Approach. IEEE Sensors Journal. https://

[67] Yadav, S. K., Tiwari, K., Pandey, H. M., & Akbar, S. A. (2021). A review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directions. Knowledge- Based Systems, 223, Article 106970. https://

[68] Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022). Deep learning in human activity recognition with wearable sensors: A review on advances. Sensors, 22(4), 1476. https://

@article{1722556,
      author = {Oluwole A. Ayegbusi, Moses O. Onyesolu},
      title = {From Inertial to Ambient: A Systematic Review of Sensor Modalities and Data Acquisition Strategies in Human Activity Recognition},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {2},
      pages = {3218-3240},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722556.pdf},
      abstract = {Human Activity Recognition (HAR) is a rapidly advancing research domain with transformative applications across healthcare, smart homes, rehabilitation, elderly monitoring, sports analytics, and context-aware computing. The performance and deployability of HAR systems are fundamentally determined by the sensor modalities through which human motion and behavioral data are captured. This systematic review provides a comprehensive examination of the full spectrum of sensor modalities employed in HAR research spanning wearable inertial sensors, physiological sensors, vision-based sensors, depth cameras, ambient sensing systems, and emerging multimodal fusion frameworks tracing, technical characteristics, application domains, and comparative strengths and limitations. The review further examines data acquisition protocols, benchmark datasets, and the persistent challenges of inter-subject variability, sensor placement sensitivity, privacy constraints, and computational efficiency that continue to shape the field. Emerging directions including federated sensing, edge-optimized data acquisition, self-supervised learning from unlabeled sensor streams, and privacy-preserving modalities are critically evaluated as promising pathways toward robust, scalable, and ethically responsible HAR deployment. By synthesizing evidence across more than a decade of HAR sensor research, this review provides a structured reference for researchers and practitioners designing next-generation activity recognition systems, and identifies the most consequential open challenges and future directions for the field.},
      keywords = {human activity recognition; sensor modalities; wearable sensors; inertial measurement unit; ambient sensing; multimodal fusion; data acquisition; deep learning; privacy-preserving har},
      month = {August},
      doi = {https://doi.org/10.64388/IREV10I2-1722556}
  }