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

Joint Authors

Badawi, Abeer A.
Al-Kabbany, Ahmad
Shaban, Heba A.

Source

Journal of Healthcare Engineering

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-06-08

Country of Publication

Egypt

No. of Pages

14

Main Subjects

Public Health
Medicine

Abstract EN

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.

American Psychological Association (APA)

Badawi, Abeer A.& Al-Kabbany, Ahmad& Shaban, Heba A.. 2020. Sensor Type, Axis, and Position-Based Fusion and Feature Selection for Multimodal Human Daily Activity Recognition in Wearable Body Sensor Networks. Journal of Healthcare Engineering،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1186385

Modern Language Association (MLA)

Badawi, Abeer A.…[et al.]. Sensor Type, Axis, and Position-Based Fusion and Feature Selection for Multimodal Human Daily Activity Recognition in Wearable Body Sensor Networks. Journal of Healthcare Engineering No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1186385

American Medical Association (AMA)

Badawi, Abeer A.& Al-Kabbany, Ahmad& Shaban, Heba A.. Sensor Type, Axis, and Position-Based Fusion and Feature Selection for Multimodal Human Daily Activity Recognition in Wearable Body Sensor Networks. Journal of Healthcare Engineering. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1186385

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1186385