research-article

Subsampled Randomized Hadamard Transformation-based Ensemble Extreme Learning Machine for Human Activity Recognition

Authors:

Dipanwita Thakur,

Arindam PalAuthors Info & Claims

ACM Transactions on Computing for Healthcare, Volume 5, Issue 1

Article No.: 5, Pages 1 - 23

https://doi.org/10.1145/3634813

Published: 13 January 2024 Publication History

Abstract

Extreme Learning Machine (ELM) is becoming a popular learning algorithm due to its diverse applications, including Human Activity Recognition (HAR). In ELM, the hidden node parameters are generated at random, and the output weights are computed analytically. However, even with a large number of hidden nodes, feature learning using ELM may not be efficient for natural signals due to its shallow architecture. Due to noisy signals of the smartphone sensors and high dimensional data, substantial feature engineering is required to obtain discriminant features and address the “curse-of-dimensionality”. In traditional ML approaches, dimensionality reduction and classification are two separate and independent tasks, increasing the system’s computational complexity. This research proposes a new ELM-based ensemble learning framework for human activity recognition to overcome this problem. The proposed architecture consists of two key parts: (1) Self-taught dimensionality reduction followed by classification. (2) they are bridged by “Subsampled Randomized Hadamard Transformation” (SRHT). Two different HAR datasets are used to establish the feasibility of the proposed framework. The experimental results clearly demonstrate the superiority of our method over the current state-of-the-art methods.

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Cited By

Yang JLiao TZhao JYan YHuang YZhao ZXiong JLiu C(2024)Domain Adaptation for Sensor-Based Human Activity Recognition with a Graph Convolutional NetworkMathematics10.3390/math1204055612:4(556)Online publication date: 12-Feb-2024
https://doi.org/10.3390/math12040556
Chadha JJain AKumar YModi N(2024)Hybrid Deep Learning Approaches for Human Activity Recognition and Postural Transitions Using Mobile Device SensorsSN Computer Science10.1007/s42979-024-03300-75:7Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s42979-024-03300-7

Index Terms

Subsampled Randomized Hadamard Transformation-based Ensemble Extreme Learning Machine for Human Activity Recognition
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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cover image ACM Transactions on Computing for Healthcare

ACM Transactions on Computing for Healthcare Volume 5, Issue 1

January 2024

130 pages

EISSN:2637-8051

DOI:10.1145/3613527

Editors:
Insup Lee
University of Pennsylvania, USA
,
Gang Zhou
William and Mary, USA

Issue’s Table of Contents

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Publication History

Published: 13 January 2024

Online AM: 27 November 2023

Accepted: 23 November 2023

Revised: 22 September 2023

Received: 12 March 2023

Published in HEALTH Volume 5, Issue 1

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Cited By

Yang JLiao TZhao JYan YHuang YZhao ZXiong JLiu C(2024)Domain Adaptation for Sensor-Based Human Activity Recognition with a Graph Convolutional NetworkMathematics10.3390/math1204055612:4(556)Online publication date: 12-Feb-2024
https://doi.org/10.3390/math12040556
Chadha JJain AKumar YModi N(2024)Hybrid Deep Learning Approaches for Human Activity Recognition and Postural Transitions Using Mobile Device SensorsSN Computer Science10.1007/s42979-024-03300-75:7Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s42979-024-03300-7

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