IoT Enabled Smart Activity Recognition using Machine Learning Methods

Main Article Content

P. Anjaiah
Dr. B. V. Ramesh Naresh Yadav


Internet of Things (IoT) enabled architecture-based devices are becoming accessible worldwide irrespective of the area. But functional settings depend on Internet facilities. In this context, the Healthcare industry took a step forward to automate Human Activity Recognition related concepts using IoT and Machine learning methods. This research used a Nodemcu ESP8266 device to track and communicate human activities acquired using ADXL345 accelerometer sensors. Three volunteers participated in this research, and data were acquired using two accelerometer sensors placed on the hand, wrist, and ankle. Data shared to the cloud- Acquired data were analyzed and trained with the Random Forest algorithm and tested with the data, achieving 100% accuracy. This model can be helpful in various applications like elderly patient monitoring, I.C.U., dementia, Alzheimer's, etc.

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How to Cite
P. Anjaiah, and Dr. B. V. Ramesh Naresh Yadav. “IoT Enabled Smart Activity Recognition Using Machine Learning Methods”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 10, Oct. 2022, pp. 09-16, doi:10.17762/ijritcc.v10i10.5729.


Bhogaraju SD, Korupalli VRK. Design of Smart Roads-A Vision on Indian Smart Infrastructure Development. In: 2020 International Conference on COMmunication Systems & Networks (COMSNETS). 2020, pp. 773–778.

Callahan A, Shah NH. Machine learning in healthcare. In: Key Advances in Clinical Informatics. Elsevier, 2017, pp. 279–291.

Elayan H, Shubair RM, Kiourti A. Wireless sensors for medical applications: Current status and future challenges. In: 2017 11th European Conference on Antennas and Propagation (EUCAP). 2017, pp. 2478–2482.

Taylor W, Shah SA, Dashtipour K, et al. An intelligent non-invasive real-time human activity recognition system for next-generation healthcare. Sensors 2020; 20: 2653.

Tcarenko I, Gia TN, Rahmani AM, et al. Energy-efficient iot-enabled fall detection system with messenger-based notification. In: International Conference on Wireless Mobile Communication and Healthcare. 2016, pp. 19–26.

Miotto R, Wang F, Wang S, et al. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform 2018; 19: 1236–1246.

Kajol Khatri, & Dr. Anand Sharma. (2022). A Study on Lightening Asynchronous Pipeline Controller for Reusable Delay Path Synthesis. Acta Energetica, (03), 29–34. Retrieved from

Strielkina A, Uzun D, Kharchenko V. Modelling of healthcare IoT using the queueing theory. In: 2017 9th IEEE international conference on intelligent data acquisition and advanced computing systems: technology and applications (IDAACS). 2017, pp. 849–852.

Suto J, Oniga S, Sitar PP. Feature analysis to human activity recognition. Int J Comput Commun Control 2016; 12: 116–130.

Chen Y, Xue Y. A deep learning approach to human activity recognition based on single accelerometer. In: 2015 IEEE international conference on systems, man, and cybernetics. 2015, pp. 1488–1492.

Büsching F, Kulau U, Gietzelt M, et al. Comparison and validation of capacitive accelerometers for health care applications. Comput Methods Programs Biomed 2012; 106: 79–88.

Mariusz Filipowicz, & Waleed F. Faris. (2022). Recent Advancement in the Field of Analogue Layout Synthesis. Acta Energetica, (02), 01–07. Retrieved from

Bhat G, Deb R, Chaurasia V.V., et al. Online human activity recognition using low-power wearable devices. In: 2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). 2018, pp. 1–8.

Kumar K.V.R., Elias S. Predictive Analysis for Detection of Human Neck Postures using a robust integration of kinetics and kinematics. arxiv, 2020.

Kumar K.V.R., Elias S. Smart Neck-Band for Rehabilitation of Musculoskeletal Disorders. In: The proceedings of IEEE International Conference on COMmunication Systems & Networks (COMSNETS), Bengaluru, India, 2020. 2020.

Neves P, Stachyra M, Rodrigues J. Application of wireless sensor networks to healthcare promotion. J Commun Softw Syst 2008; 4: 181–190.

Hammerla NY, Halloran S, Plötz T. Deep, convolutional, and recurrent models for human activity recognition using wearables. arXiv Prepr arXiv160408880.

Ever Y.K. Secure-anonymous user authentication scheme for e-healthcare application using wireless medical sensor networks. IEEE Syst J 2018; 13: 456–467.

Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol; 2.

Jayaysingh R, David J, Raaj M.J.M., et al. IoT Based Patient Monitoring System Using NodeMCU. In: 2020 5th International Conference on Devices, Circuits and Systems (ICDCS). 2020, pp. 240–243.

Kim Y, Imani M, Rosing TS. Efficient human activity recognition using hyperdimensional computing. In: Proceedings of the 8th International Conference on the Internet of Things. 2018, pp. 1–6.

Kumar K.V.R., Kumar KD, Poluru RK, et al. internet of things and fog computing applications in intelligent transportation systems. In: Architecture and Security Issues in Fog Computing Applications. I.G.I. Global, 2020, pp. 131–150.

Lenz R, Reichert M. I.T. support for healthcare processes--premises, challenges, perspectives. Data Knowl Eng 2007; 61: 39–58.

Mutlag AA, Abd Ghani MK, Arunkumar N al, et al. Enabling technologies for fog computing in healthcare IoT systems. Futur Gener Comput Syst 2019; 90: 62–78.

Ronao CA, Cho S-B. Human activity recognition with smartphone sensors using deep learning neural networks. Expert Syst Appl 2016; 59: 235–244.

Rault T, Bouabdallah A, Challal Y, et al. A survey of energy-efficient context recognition systems using wearable sensors for healthcare applications. Pervasive Mob Comput 2017; 37: 23–44.

Ng HS, Sim ML, Tan CM. Security issues of wireless sensor networks in healthcare applications. B.T. Technol J 2006; 24: 138–144.

Seeja G, Reddy O, Kumar K.V.R., et al. Internet of Things and Robotic Applications in the Industrial Automation Process. In: Innovations in the Industrial Internet of Things (IIoT) and Smart Factory. I.G.I. Global, 2021, pp. 50–64.

Yu K-H, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng 2018; 2: 719–731.