An IoT Solution that Classifies Human Physical Activities based on Sensors and Supervised Learning Algorithms

Một giải pháp IoT phân loại các hoạt động thể chất của con người dựa trên các cảm biến và thuật toán học có giám sát

  • Thuong Vu Thi Phuong Dong University, Hà Nội, Vietnam / Graduate University of Science and Technology, VAST, Hanoi City, Vietnam
  • Thu Nguyen Thi Faculty of Electronic Engineering Hanoi University of Industry Hanoi City, Vietnam
  • Tran Duc Nghia Institute of Information Technology Vietnam Academy of Science and Technology Hanoi City, Vietnam
  • Nguyen Nhu Son Institute of Information Technology Vietnam Academy of Science and Technology Hanoi City, Vietnam
  • Tran Duc Tan Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi City, Vietnam
Keywords: IoT, HAR, wearable technology, accelerometer, activity classification

Abstract

At the moment, human physical activities can be detected and classified by activity recognition systems. One
of the key issues in human activity recognition (HAR) systems is cost, speed, and accuracy. With the advancement of mobile devices and increasingly sophisticated microprocessors, along with the integration of various sensors including accelerometers, human activity recognition has become easier and faster. However, the accuracy of activity classification based on accelerometer data depends on many different factors, which can lead to potential deviations in results. Through various experiments, we have found that selecting optimal features is crucial for the performance of classification. In this paper, we propose a simple feature set that yields very promising results with an accuracy of up to 99%.

Author Biographies

Thuong Vu Thi, Phuong Dong University, Hà Nội, Vietnam / Graduate University of Science and Technology, VAST, Hanoi City, Vietnam

Vu Thi Thuong Graduated with a Master’s degree in Computer Science in 2009 at the Military Technical Academy. Currently a lecturer at the Faculty of Information Technology and Communications, Phuong
Dong University. Research field: Machine learning methods to improve signal measurement, using accelerometers and machine learning algorithms to serve the problem of identifying and classifying behavior.

Thu Nguyen Thi, Faculty of Electronic Engineering Hanoi University of Industry Hanoi City, Vietnam

Thu Nguyen Thi received Ph.D. degree in Electronic Engineering from Le Quy Don Technical University, Vietnam in 2018. Dr. Nguyen research interests are in the areas of space-time signal processing for communications
such as MIMO, spatial modulation and cooperative communications, artificial neural networks.

Tran Duc Nghia, Institute of Information Technology Vietnam Academy of Science and Technology Hanoi City, Vietnam

Tran Duc Nghia Received the Ph.D. degree from Sorbonne Paris Cité, France. In his thesis, he focuses on signal processing of EPR spectra for in vivo experiments. He is a Researcher with the Institute of Information
Technology (IOIT), Vietnam Academy of Science and Technology (VAST). His research interests include mathematics and signal processing, electron paramagnetic resonance (EPR), parameter estimation, and data analysis.

Nguyen Nhu Son, Institute of Information Technology Vietnam Academy of Science and Technology Hanoi City, Vietnam

Nguyen Nhu Son Received the Ph.D. degree in Computer Science from The University of Queenslnad, Australia, in 2007. He is currently a Vice Chair of Scientific Council, Head of Department at Institute of Information Technology (IOIT) - Vietnam Academy of Science and Technology. His research interests include artificial intelligence, data mining, soft computing, and fuzzy computing. .

Tran Duc Tan, Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi City, Vietnam

Tran Duc Tan Received BTech degree in electronics engineering from College of Technology, Hanoi, Vietnam in 2002 and MTech degree in electronics engineering from College of Technology, Hanoi, Vietnam in 2005 and Ph.D. degree in electronics engineering from electronics engineering VNU University of Engineering and
Technology, Hanoi, Vietnam. He is currently Vice Dean of Faculty of Electrical and Electronics Engineering, PHENIKAA University. His current research interest is in signal processing for biomedical imaging.

References

M. A. Khan et al., “A Fused Heterogeneous Deep Neural Network and Robust Feature Selection Framework for Human Actions Recognition,” Arab J Sci Eng, Jul. 2021.

S. Chernbumroong, S. Cang, A. Atkins, and H. Yu, “Elderly activities recognition and classification for applications in assisted living,” Expert Systems with Applications, vol. 40, no. 5, pp. 1662–1674, 2013.

K. K. B. Peetoom, M. A. S. Lexis, M. Joore, C. D. Dirksen, and L. P. De Witte, “Literature review on monitoring technologies and their outcomes in independently living elderly people,” Disability and Rehabilitation: Assistive Technology, vol. 10, no. 4, pp. 271–294, 2015.

P. Pierleoni, A. Belli, L. Palma, M. Pellegrini, L. Pernini, and S. Valenti, “A High Reliability Wearable Device for Elderly Fall Detection,” IEEE Sensors Journal, vol. 15, no. 8, pp. 4544–4553, 2015.

S. E.-R. Pham Van Thanh, Duc-Tan Tran, Dinh-Chinh Nguyen, Nguyen Duc Anh, Dang Nhu Dinh and K. Sandrasegaran, “Development of a Real-Time, Simple and High- Accuracy Fall Detection System for Elderly Using 3-DOF Accelerometers,” Arabian Journal for Science and Engineering, pp. 3–4, 2018.

N. T. T. Hong, et al., “A Low-cost Real-time IoT Human Activity Recognition System Based on Wearable Sensor and the Supervised Learning Algorithms,” Measurement, Vol. 218, 113231, 2023.

A. Jain and V. Kanhangad, “Exploring orientation and accelerometer sensor data for personal authentication in smartphones using touchscreen gestures,” Pattern Recognition Letters, vol. 68, pp. 351–360, 2015.

N. N. Diep, C. Pham, and T. M. Phuong, “A classifier based approach to real-time fall detection using low-cost

wearable sensors,” in Proceedings of the Fifth Symposium on Information and Communication Technology - SoICT ’14, Hanoi, Viet Nam, 2014.

A. Wang, G. Chen, J. Yang, S. Zhao, and C. Y. Chang, “A Comparative Study on Human Activity Recognition Using Inertial Sensors in a Smartphone,” IEEE Sensors Journal, vol. 16, no. 11, pp. 4566–4578, 2016.

S. Khan et al., “Human Action Recognition: A Paradigm of Best Deep Learning Features Selection and Serial Based Extended Fusion,” Sensors, vol. 21, no. 23, 2021.

F. Marquis-Faulkes, S. J. McKenna, A. F. Newell, and P. Gregor, “Gathering the requirements for a fall monitor

using drama and video with older people,” Technology and Disability, vol. 17, no. 4, pp. 227–236, 2005.

M. A. Khan, Y.-D. Zhang, S. A. Khan, M. Attique, A. Rehman, and S. Seo, “A resource conscious human action

recognition framework using 26-layered deep convolutional neural network,” Multimed Tools Appl, vol. 80, no. 28–29, pp. 35827–35849, Nov. 2021.

M. Sundholm, J. Cheng, B. Zhou, A. Sethi, and P. Lukowicz, “Smart-mat: Recognizing and counting gym exercises with low-cost resistive pressure sensing matrix,” UbiComp 2014 - Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 373–382, 2014.

C. Zhu and W. Sheng, “Wearable sensor-based hand gesture and daily activity recognition for robot-assisted living,” IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, vol. 41, no. 3, pp. 569–573, 2011.

A. Mannini, S. S. Intille, M. Rosenberger, A. M. Sabatini, and W. Haskell, “Activity Recognition Using a Single

Accelerometer Placed at the Wrist or Ankle,” Medicine & Science in Sports & Exercise, vol. 45, no. 11, pp. 2193–2203, 2013.

J. P¨arkk¨a, M. Ermes, P. Korpip¨a¨a, J. M¨antyj¨arvi, J. Peltola, and I. Korhonen, “Activity classification using realistic data from wearable sensors,” IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 1, pp. 119–128, 2006.

M. Ahmed et al., “Real-Time Violent Action Recognition Using Key Frames Extraction and Deep Learning,” Computers, Materials & Continua, vol. 69, no. 2, pp. 2217–2230, 2021.

N. Ravi, N. Dandekar, P. Mysore and M. L. Littman, “Activity recognition from accelerometer data,” Lecture Notes in Networks and Systems, vol. 43, pp. 317–329, 2019.

L. Bao and S. S. Intille, “Activity Recognition from User- Annotated Acceleration Data BT-UbiComp 2002: Ubiquitous Computing,” Ubiquitous Computing, vol. 3001, no. Chapter 1, pp. 1–17, 2004.

J. R. Kwapisz, G. M. Weiss, and S. A. Moore, “Activity recognition using cell phone accelerometers,” ACM SIGKDD Explorations Newsletter, vol. 12, no. 2, pp. 74–82, 2011.

P. Nguyen Huu, N. Nguyen Thi and T. P. Ngoc, “Proposing Posture Recognition System Combining MobilenetV2 and LSTM for Medical Surveillance”, IEEE Access, vol. 10, pp. 1839-1849, 2022.

Published
2024-11-25