Published on in Vol 7, No 2 (2019): February

Preprints (earlier versions) of this paper are available at, first published .
Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors’ Data

Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors’ Data

Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors’ Data


  1. Chen R, Stewart W, Sun J, Ng K, Yan X. Recurrent Neural Networks for Early Detection of Heart Failure From Longitudinal Electronic Health Record Data. Circulation: Cardiovascular Quality and Outcomes 2019;12(10) View
  2. Sczuka K, Schwickert L, Becker C, Klenk J. Re-Enactment as a Method to Reproduce Real-World Fall Events Using Inertial Sensor Data: Development and Usability Study. Journal of Medical Internet Research 2020;22(4):e13961 View
  3. Culman C, Aminikhanghahi S, J. Cook D. Easing Power Consumption of Wearable Activity Monitoring with Change Point Detection. Sensors 2020;20(1):310 View
  4. Wu G, Liu Z, Chen X, Uddin M. Weighted Classification of Machine Learning to Recognize Human Activities. Complexity 2021;2021:1 View
  5. Qian H, Pan S, Miao C. Weakly-supervised sensor-based activity segmentation and recognition via learning from distributions. Artificial Intelligence 2021;292:103429 View
  6. Bahador N, Ferreira D, Tamminen S, Kortelainen J. Deep Learning–Based Multimodal Data Fusion: Case Study in Food Intake Episodes Detection Using Wearable Sensors. JMIR mHealth and uHealth 2021;9(1):e21926 View
  7. Zhao Q, Li Z, Shah D, Fischer H, Solís P, Wentz E. Understanding the interaction between human activities and physical health under extreme heat environment in Phoenix, Arizona. Health & Place 2023;79:102691 View
  8. Pavliuk O, Mishchuk M, Strauss C. Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform. Algorithms 2023;16(2):77 View
  9. Gurewitz O, Shifrin M, Dvir E. Data Gathering Techniques in WSN: A Cross-Layer View. Sensors 2022;22(7):2650 View
  10. Li Q, Liu Y, Zhu J, Chen Z, Liu L, Yang S, Zhu G, Zhu B, Li J, Jin R, Tao J, Chen L. Upper-Limb Motion Recognition Based on Hybrid Feature Selection: Algorithm Development and Validation. JMIR mHealth and uHealth 2021;9(9):e24402 View
  11. Huang J, Kaewunruen S, Ning J. AI-Based Quantification of Fitness Activities Using Smartphones. Sustainability 2022;14(2):690 View
  12. Geng H, Huan Z, Liang J, Hou Z, Lv S, Wang Y. Segmentation and Recognition Model for Complex Action Sequences. IEEE Sensors Journal 2022;22(5):4347 View
  13. Ragani Lamooki S, Hajifar S, Hannan J, Sun H, Megahed F, Cavuoto L, Nisar H. Classifying tasks performed by electrical line workers using a wrist-worn sensor: A data analytic approach. PLOS ONE 2022;17(12):e0261765 View
  14. Uslu G, Baydere S. A Segmentation Scheme for Knowledge Discovery in Human Activity Spotting. IEEE Transactions on Cybernetics 2022;52(7):5668 View
  15. Tsang K, Pinnock H, Wilson A, Shah S. Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review. Journal of Asthma and Allergy 2022;Volume 15:855 View
  16. Hua R, Wang Y. Robust Foot Motion Recognition Using Stride Detection and Weak Supervision-Based Fast Labeling. IEEE Sensors Journal 2021;21(14):16245 View
  17. Qi W, Wang N, Su H, Aliverti A. DCNN based human activity recognition framework with depth vision guiding. Neurocomputing 2022;486:261 View
  18. Lamooki S, Hajifar S, Kang J, Sun H, Megahed F, Cavuoto L. A data analytic end-to-end framework for the automated quantification of ergonomic risk factors across multiple tasks using a single wearable sensor. Applied Ergonomics 2022;102:103732 View
  19. Hendry D, Rohl A, Rasmussen C, Zabatiero J, Cliff D, Smith S, Mackenzie J, Pattinson C, Straker L, Campbell A. Objective Measurement of Posture and Movement in Young Children Using Wearable Sensors and Customised Mathematical Approaches: A Systematic Review. Sensors 2023;23(24):9661 View
  20. Jiang Z, Van Zoest V, Deng W, Ngai E, Liu J. Leveraging Machine Learning for Disease Diagnoses Based on Wearable Devices: A Survey. IEEE Internet of Things Journal 2023;10(24):21959 View
  21. Xie P, Wang Y, Yu J, Lv Y, Cheng S, Hao Y, Chen X, Zhang T, Chen J. Abnormal Low-Frequency Corticokinematic Coherence in Stroke: An Electroencephalography and Acceleration Study. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2024;32:233 View
  22. Demrozi F, Pravadelli G, Bihorac A, Rashidi P. Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey. IEEE Access 2020;8:210816 View

Books/Policy Documents

  1. Latif T, Dieffenderfer J, da Silva R, Lobaton E, Bozkurt A. Encyclopedia of Sensors and Biosensors. View