Next Article in Journal
Bimodal Biometric Verification Using the Fusion of Palmprint and Infrared Palm-Dorsum Vein Images
Previous Article in Journal
Design and Calibration of a New 6 DOF Haptic Device
Open AccessReview

Physical Human Activity Recognition Using Wearable Sensors

Laboratory of Images, Signals and Intelligent Systems (LISSI), University of Paris-Est Créteil (UPEC), 122 rue Paul Armangot, Vitry-Sur-Seine 94400, France
Laboratory of Information Science and Systems (LSIS, CNRS-UMR7296), University of Toulon, Bâtiment R, BP 20132, La Garde Cedex 83957, France
French Institute of Science and Technology for Transport, development and Networks (IFSTTAR), University of Paris-Est, COSYS, GRETTIA, Marne la Vallée F-77447, France
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Sensors 2015, 15(12), 31314-31338;
Received: 11 September 2015 / Revised: 2 December 2015 / Accepted: 8 December 2015 / Published: 11 December 2015
(This article belongs to the Section Physical Sensors)
This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors’ placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject. View Full-Text
Keywords: activity recognition; wearable sensors; smart spaces; data classifiers; accelerometers; physical activities activity recognition; wearable sensors; smart spaces; data classifiers; accelerometers; physical activities
Show Figures

Figure 1

MDPI and ACS Style

Attal, F.; Mohammed, S.; Dedabrishvili, M.; Chamroukhi, F.; Oukhellou, L.; Amirat, Y. Physical Human Activity Recognition Using Wearable Sensors. Sensors 2015, 15, 31314-31338.

AMA Style

Attal F, Mohammed S, Dedabrishvili M, Chamroukhi F, Oukhellou L, Amirat Y. Physical Human Activity Recognition Using Wearable Sensors. Sensors. 2015; 15(12):31314-31338.

Chicago/Turabian Style

Attal, Ferhat; Mohammed, Samer; Dedabrishvili, Mariam; Chamroukhi, Faicel; Oukhellou, Latifa; Amirat, Yacine. 2015. "Physical Human Activity Recognition Using Wearable Sensors" Sensors 15, no. 12: 31314-31338.

Find Other Styles

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop