Human Activity Recognition Using Smartphone Sensor Data

  • Sweta JainEmail author
  • Sadare Alam
  • K. Shreesha Prabhu
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 100)


The hot topic in recent times is recognition of human activities through a smartphone, smart home, remote monitoring and assisted healthcare. These fall under ambient intelligent services. This also includes recognition of simple activities like sitting, running and walking, and more research is being held for semi-complex activities such as moving upstairs and downstairs, running and jogging. Activity recognition is the problem of predicting the current action of a person by using the motion sensors worn on the body. This problem is approached by using supervised classification model where a model is trained from a known set of data, and a query is then resolved to a known activity label by using the learned model. The exigent issue here is whether how to feed this classification model with a set of features, where the input provided is a raw sensor data. In this study, three classification techniques are considered and their accuracy in predicting the correct activity. In addition to the systematic comparison of the results, a comprehensive evaluation of data collection and some preprocessing steps are provided such as filtering and feature generation. The results determine that feeding a support vector machine with an ensemble selection of most relevant features by using principal component analysis yields best results.


Human activity Smartphone sensors Walking Running 


  1. 1.
    Wu Z, Zhang S, Zhang C Human activity recognition using wearable devices sensor data Google Scholar
  2. 2.
    Anguita D, Ghio A, Oneto L, Parra X, Reyes-Ortiz JL Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In: Ambient assisted living and home careGoogle Scholar
  3. 3.
    DeVaul RW, Dunn S (2001) Real-time motion classification for wearable computing applications. Project PaperGoogle Scholar
  4. 4.
    Zhang M, Sawchuk AA (2011) A feature selection-based framework for human activity recognition using wearable multimodal sensors. In: Proceedings of the 6th international conference on body area networks. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)Google Scholar
  5. 5.
    Maurer U, Smailagic A, Siewiorek D, Deisher M (2006) Activity recognition and monitoring using multiple sensors on different body positions. In: International workshop on wearable and implantable body sensor networks (BSN’06) Google Scholar
  6. 6.
    Anguita D, Ghio A, Oneto L, Parra X, Reyes-Ortiz J. L (2013) A public domain dataset for human activity recognition using smartphones. In: 21th European symposium on artificial neural networks, computational intelligence and machine learning, ESANN 2013, Bruges, Belgium, 24–26 April 2013Google Scholar
  7. 7.
    Shlens J (2014) A tutorial on principal component analysis. arXivn preprint arXiv:1404.1100
  8. 8.
    Brown M, Deitch T, O’Conor L Activity classification with smartphone dataGoogle Scholar
  9. 9.
    Pai A, Nachum O, Kanter M (2013) Activity classification using smartphone accelerometer dataGoogle Scholar
  10. 10.
    Sunny JT et al Applications and challenges of human activity recognition using sensors in a smart environment. Int J 2: 50–57Google Scholar
  11. 11.
    Lara OD, Labrador MA (2013) A survey on human activity recognition using wearable sensors. IEEE Commun Surv Tutor 15(3):1192–1209CrossRefGoogle Scholar
  12. 12.
    Olguın DO, Pentland AS (2006) Human activity recognition: accuracy across common locations for wearable sensors. In: Proceedings of 2006 10th IEEE international symposium on wearable computers, Citeseer, Montreux, SwitzerlandGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Maulana Azad National Institute of TechnologyBhopalIndia

Personalised recommendations