Physical Human Activity Recognition Using Wearable Sensors

By Ferhat Attal, Samer Mohammed, Mariam Dedabrishvili, Faicel Chamroukhi, Latifa Oukhellou, Yacine Amirat. Italian National Conference on Sensors, 2015

760 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/bbac65dc51363ce4d4ee95a48fef98e49da407cf

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Citations in the corpus

Human Activity Recognition With Smartphone and Wearable Sensors Using Deep Learning Techniques: A Review (2021, background): “independent quality of life who lives alone [2].”
Scaling Human Activity Recognition via Deep Learning-based Domain Adaptation (2018, background): “Researchers have explored multiple novel sensing systems such as wearable [10], ambient, PIR sensor, sensor tag, RFID etc.”
Tree Alignment Based on Needleman-Wunsch Algorithm for Sensor Selection in Smart Homes (2017, methodology): “[9] applied the random forest as a wrapper method to identify the set of informative sensors for recognising human physical activities, such as sitting, lying, standing, etc.”
Secure and Robust Machine Learning for Healthcare: A Survey (2020, background): “different ML techniques for human activity recognition with application to remote monitoring of patients using wearable devices is presented in [67].”
Human Activity Recognition Using Smartphones (2018, methodology): “tried to classify activity depending on wearable multiple gyroscope and accelerometers[2].”
Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey (2020, methodology): “The second most used model is the classical k-Nearest Neighbor (kNN) model [4], [6], [42], [60], [61], [69], [78], [79], [92], [95], [96], [113], [118], [122], [127], [136], [142], [145], [162], [164], [169], [173], [186], which was used in 23 papers, achieving an average accuracy of 93.”