Daily Activity Recognition using Wearable Sensors via Machine Learning and Feature Selection

Badawi, Abeer; Al-Kabbany, Ahmad; Shaban, Heba

doi:10.1109/icces.2018.8639309

Cited by 5 publications

(11 citation statements)

References 12 publications

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“…As mentioned earlier [6] and boldfaced in Table 5, the best accuracy is achieved using the RF classifier. e best single axis achieved 94.7% with 28 features, and the combination of all parameters achieved 96.8% with 112 features.…”

Section: Comparison Between Single Axis Vs Triple Axes and Allmentioning

confidence: 59%

“…is dataset contains twelve activities, with the following ID numbers: walking with ID number [0], running with ID number [1], going down with ID number [2], going up with ID number [3], sitting with ID number [4], sitting down with ID number [5], standing up with ID number [6], standing with ID number [7], bicycling with ID number [8], down by elevator with ID number [9], up by elevator with ID number [10], and sitting in the car with ID number [11]. e data was collected from 18 adults (14 males and 4 females).…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…Our own previous research [5] investigates the relation between the recognition accuracy and the IMU's placementsand-orientations [5]. We also highlight the impact (in terms of accuracy) of applying feature selection techniques on the IMU's data [6]. is research, however, studies the fusion of EMG sensor data with the IMU's data.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we have considered widely used classifiers, namely, support vector machine (SVM), naive Bayes (NB), neural networks as multilayer perceptron (MLP), decision tree (DT), k-nearest neighbor (k-NN), and random forest (RF) [5]. We have also followed the study in [6] while applying feature selection. Hence, we used the different approaches for sequential feature selection including sequential forward feature selection (SFS), sequential backward feature selection (SBS), and sequential forward floating feature selection (SFFS) [6].…”

Section: Introductionmentioning

confidence: 99%

“…We have also followed the study in [6] while applying feature selection. Hence, we used the different approaches for sequential feature selection including sequential forward feature selection (SFS), sequential backward feature selection (SBS), and sequential forward floating feature selection (SFFS) [6].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Sensor Type, Axis, and Position-Based Fusion and Feature Selection for Multimodal Human Daily Activity Recognition in Wearable Body Sensor Networks

Badawi

Al-Kabbany

Shaban

2020

Journal of Healthcare Engineering

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This research addresses the challenge of recognizing human daily activities using surface electromyography (sEMG) and wearable inertial sensors. Effective and efficient recognition in this context has emerged as a cornerstone in robust remote health monitoring systems, among other applications. We propose a novel pipeline that can attain state-of-the-art recognition accuracies on a recent-and-standard dataset—the Human Gait Database (HuGaDB). Using wearable gyroscopes, accelerometers, and electromyography sensors placed on the thigh, shin, and foot, we developed an approach that jointly performs sensor fusion and feature selection. Being done jointly, the proposed pipeline empowers the learned model to benefit from the interaction of features that might have been dropped otherwise. Using statistical and time-based features from heterogeneous signals of the aforementioned sensor types, our approach attains a mean accuracy of 99.8%, which is the highest accuracy on HuGaDB in the literature. This research underlines the potential of incorporating EMG signals especially when fusion and selection are done simultaneously. Meanwhile, it is valid even with simple off-the-shelf feature selection methods such the Sequential Feature Selection family of algorithms. Moreover, through extensive simulations, we show that the left thigh is a key placement for attaining high accuracies. With one inertial sensor on that single placement alone, we were able to achieve a mean accuracy of 98.4%. The presented in-depth comparative analysis shows the influence that every sensor type, position, and placement can have on the attained recognition accuracies—a tool that can facilitate the development of robust systems, customized to specific scenarios and real-life applications.

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Section: Comparison Between Single Axis Vs Triple Axes and Allmentioning

confidence: 59%