An Efficient and Lightweight Deep Learning Model for Human Activity Recognition Using Smartphones

Ankita, Shalli Rani, Himanshi Babbar, Sonya Coleman, Aman Singh, Hani Moaiteq Aljahdali

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)
72 Downloads (Pure)


Traditional pattern recognition approaches have gained a lot of popularity. However, these are largely dependent upon manual feature extraction, which makes the generalized model obscure. The sequences of accelerometer data recorded can be classified by specialized smartphones into well known movements that can be done with human activity recognition. With the high success and wide adaptation of deep learning approaches for the recognition of human activities, these techniques are widely used in wearable devices and smartphones to recognize the human activities. In this paper, convolutional layers are combined with long short-term memory (LSTM), along with the deep learning neural network for human activities recognition (HAR). The proposed model extracts the features in an automated way and categorizes them with some model attributes. In general, LSTM is alternative form of recurrent neural network (RNN) which is famous for temporal sequences’ processing. In the proposed architecture, a dataset of UCI-HAR for Samsung Galaxy S2 is used for various human activities. The CNN classifier, which should be taken single, and LSTM models should be taken in series and take the feed data. For each input, the CNN model is applied, and each input image’s output is transferred to the LSTM classifier as a time step. The number of filter maps for mapping of the various portions of image is the most important hyperparameter used. Transformation on the basis of observations takes place by using Gaussian standardization. CNN-LSTM, a proposed model, is an efficient and lightweight model that has shown high robustness and better activity detection capability than traditional algorithms by providing the accuracy of 97.89%.
Original languageEnglish
Article number3845
Number of pages17
Issue number11
Early online date2 Jun 2021
Publication statusPublished (in print/issue) - 2 Jun 2021

Bibliographical note

Funding Information:
This project was supported by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant No. (KEP-11-611-42).

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • Convolutional neural network
  • Deep learning
  • Human activity recognition
  • Long-short term memory


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