Multiscale DCNN Ensemble Applied to Human Activity Recognition Based on Wearable Sensors

Sena, Jessica; Santos, Jesimon Barreto; Schwartz, William Robson

doi:10.23919/eusipco.2018.8553354

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…During the development of this work, a technical paper entitled ´´Multiscale DCNN Ensemble Applied to Human Activity Recognition Based on Wearable Sensors" containing the contributions of this thesis was published in the proceedings of the 26th European Signal Processing Conference (EUSIPCO) [25]. Additionally, we contribute as co-author in the journal paper ´´Human Activity Recognition based on Wearable Sensor Data -A Benchmark", which created a significant standardization of metrics and protocols on seven important datasets and made an extensive evaluation of several methods for human activity recognition based on wearable sensor domain.…”

Section: Discussionmentioning

confidence: 99%

Human Activity Recognition based on Wearable Sensors using Multiscale DCNN Ensemble

Sena

Schwartz

2019

Anais Estendidos Do XXXII Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2019)

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Sensor-based Human Activity Recognition (HAR) provides valuable knowledge to many areas. Recently, wearable devices have gained space as a relevant source of data. However, there are two issues: large number of heterogeneous sensors available and the temporal nature of the sensor data. To handle these issues, we propose a multimodal approach that processes each sensor separately and, through an ensemble of Deep Convolution Neural Networks (DCNN), extracts information from multiple temporal scales of the sensor data. In this ensemble, we use a convolutional kernel with a different height for each DCNN. Considering that the number of rows in the sensor data reflects the data captured over time, each kernel height reflects a temporal scale from which we can extract patterns. Consequently, our approach is able to extract information from simple movement patterns such as a wrist twist when picking up a spoon, to complex movements such as the human gait. This multimodal and multi-temporal approach outperforms previous state-of-the-art works in seven important datasets using two different protocols. In addition, we demonstrate that the use of our proposed set of kernels improves sensor-based HAR in another multi-kernel approach, the widely employed inception network.

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Section: Discussionmentioning

confidence: 99%

Human Activity Recognition based on Wearable Sensors using Multiscale DCNN Ensemble

Sena

Schwartz

2019

Anais Estendidos Do XXXII Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2019)

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“…Although there are many HAR studies based on ensemble learning technology [39,40,41,42,43,44], to our best knowledge, there is still no work attempting to improve the performance of HAR through a selective ensemble approach. Most of the ensemble learning-based HAR studies [17,30,39] combined all the trained base classifiers for recognition.…”

Section: Introductionmentioning

confidence: 99%

Selective Ensemble Based on Extreme Learning Machine for Sensor-Based Human Activity Recognition

Tian

Jie

Geng

et al. 2019

Sensors

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Sensor-based human activity recognition (HAR) has attracted interest both in academic and applied fields, and can be utilized in health-related areas, fitness, sports training, etc. With a view to improving the performance of sensor-based HAR and optimizing the generalizability and diversity of the base classifier of the ensemble system, a novel HAR approach (pairwise diversity measure and glowworm swarm optimization-based selective ensemble learning, DMGSOSEN) that utilizes ensemble learning with differentiated extreme learning machines (ELMs) is proposed in this paper. Firstly, the bootstrap sampling method is utilized to independently train multiple base ELMs which make up the initial base classifier pool. Secondly, the initial pool is pre-pruned by calculating the pairwise diversity measure of each base ELM, which can eliminate similar base ELMs and enhance the performance of HAR system by balancing diversity and accuracy. Then, glowworm swarm optimization (GSO) is utilized to search for the optimal sub-ensemble from the base ELMs after pre-pruning. Finally, majority voting is utilized to combine the results of the selected base ELMs. For the evaluation of our proposed method, we collected a dataset from different locations on the body, including chest, waist, left wrist, left ankle and right arm. The experimental results show that, compared with traditional ensemble algorithms such as Bagging, Adaboost, and other state-of-the-art pruning algorithms, the proposed approach is able to achieve better performance (96.7% accuracy and F1 from wrist) with fewer base classifiers.

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Designing Efficient and Lightweight Deep Learning Models for Healthcare Analysis

Baltabay

Yazıcı

Sterling

et al. 2023

Neural Process Lett

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Multiscale DCNN Ensemble Applied to Human Activity Recognition Based on Wearable Sensors

Cited by 6 publications

References 16 publications

Human Activity Recognition based on Wearable Sensors using Multiscale DCNN Ensemble

Human Activity Recognition based on Wearable Sensors using Multiscale DCNN Ensemble

Selective Ensemble Based on Extreme Learning Machine for Sensor-Based Human Activity Recognition

Designing Efficient and Lightweight Deep Learning Models for Healthcare Analysis

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