Context-based ensemble method for human energy expenditure estimation

Gjoreski, Hristijan; Kaluža, Boštjan; Gams, Matjać; Milić, Radoje; Luštrek, Mitja

doi:10.1016/j.asoc.2015.05.001

Cited by 46 publications

(60 citation statements)

References 22 publications

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“…5. Extensive experiments to explore the effectiveness of the proposed methods using two publicly available datasets and compare the significance of the multi-view stacking ensemble with weighted majority voting, Bagging and Random Subspace ensemble [16,26,27] based multiple classifier system methods.…”

Section: To Demonstrate the Impact Of Synthetic Minority Over-samplinmentioning

confidence: 99%

“…Here, we review some of the studies that recently developed multiple classifiers for human activity detection to set the stage and need for multiple classifier systems when developing human activity detection and health monitoring system. Gjoreski et al [27] proposed multiple context decision ensemble for energy expenditure estimation from physical activity details. The authors trained multiple regression-based algorithms on different contexts (features) extracted from multiple sensors and combined the individual approach using majority voting.…”

Section: Multiple Classifier Systemsmentioning

confidence: 99%

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Multi-sensor fusion based on multiple classifier systems for human activity identification

Nweke

Wah

Mujtaba

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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Multimodal sensors in healthcare applications have been increasingly researched because it facilitates automatic and comprehensive monitoring of human behaviors, high-intensity sports management, energy expenditure estimation, and postural detection. Recent studies have shown the importance of multi-sensor fusion to achieve robustness, high-performance generalization, provide diversity and tackle challenging issue that maybe difficult with single sensor values. The aim of this study is to propose an innovative multi-sensor fusion framework to improve human activity detection performances and reduce misrecognition rate. The study proposes a multiview ensemble algorithm to integrate predicted values of different motion sensors. To this end, computationally efficient classification algorithms such as decision tree, logistic regression and k-Nearest Neighbors were used to implement diverse, flexible and dynamic human activity detection systems. To provide compact feature vector representation, we studied hybrid bio-inspired evolutionary search algorithm and correlation-based feature selection method and evaluate their impact on extracted feature vectors from individual sensor modality. Furthermore, we utilized Synthetic Over-sampling minority Techniques (SMOTE) algorithm to reduce the impact of class imbalance and improve performance results. With the above methods, this paper provides unified framework to resolve major challenges in human activity identification. The performance results obtained using two publicly available datasets showed significant improvement over baseline methods in the detection of specific activity details and reduced error rate. The performance results of our evaluation showed 3% to 24% improvement in accuracy, recall, precision, F-measure and detection ability (AUC) compared to single sensors and feature-level fusion. The benefit of the proposed multisensor fusion is the ability to utilize distinct feature characteristics of individual sensor and multiple classifier systems to improve recognition accuracy. In addition, the study suggests a promising potential of hybrid feature selection approach, diversity-based multiple classifier systems to improve mobile and wearable sensor-based human activity detection and health monitoring system.

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Section: To Demonstrate the Impact Of Synthetic Minority Over-samplinmentioning

confidence: 99%

Section: Multiple Classifier Systemsmentioning

confidence: 99%

Multi-sensor fusion based on multiple classifier systems for human activity identification

Nweke

Wah

Mujtaba

et al. 2019

Hum. Cent. Comput. Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Hence, we expect to see more and more research from a data processing perspective, to try to more accurately estimate EE in daily life. This may lead to the inclusion of several different sensors alongside activity predictors (heart rate, breathing rate and others) and novel data processing techniques, similar or better to the ones presented in [90] in order to better estimate EE.…”

Section: Posture and Activity Recognition And Energy Expenditure Estmentioning

confidence: 99%

“…Electronics 2016, 5, x FOR PEER REVIEW 13 of 29 different sensors alongside activity predictors (heart rate, breathing rate and others) and novel data processing techniques, similar or better to the ones presented in [90] in order to better estimate EE. Excessive body weight is the factor which defines obesity and body weight is also one of the most significant factors in calculating energy expenditure.…”

Section: Biofeedbackmentioning

confidence: 99%

A Comparative Review of Footwear-Based Wearable Systems

2016

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Abstract:Footwear is an integral part of daily life. Embedding sensors and electronics in footwear for various different applications started more than two decades ago. This review article summarizes the developments in the field of footwear-based wearable sensors and systems. The electronics, sensing technologies, data transmission, and data processing methodologies of such wearable systems are all principally dependent on the target application. Hence, the article describes key application scenarios utilizing footwear-based systems with critical discussion on their merits. The reviewed application scenarios include gait monitoring, plantar pressure measurement, posture and activity classification, body weight and energy expenditure estimation, biofeedback, navigation, and fall risk applications. In addition, energy harvesting from the footwear is also considered for review. The article also attempts to shed light on some of the most recent developments in the field along with the future work required to advance the field.

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“…We evaluate the method on two datasets: the JSI-ADL dataset comprising 14 activates recorded by 10 subjects [15] and the REALDISP [16] dataset comprising 33 fitness activities recorded by 17 subjects. We compare the method against Density-based spatial clustering of applications with noise (DBSCAN) [17] and online agglomerative clustering [14].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised online activity discovery using temporal behaviour assumption

Gjoreski

Roggen

2017

Proceedings of the 2017 ACM International Symposium on Wearable Computers

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We present a novel unsupervised approach, UnADevs, for discovering activity clusters corresponding to periodic and stationary activities in streaming sensor data. Such activities usually last for some time, which is exploited by our method; it includes mechanisms to regulate sensitivity to brief outliers and can discover multiple clusters overlapping in time to better deal with deviations from nominal behaviour. The method was evaluated on two activity datasets containing large number of activities (14 and 33 respectively) against online agglomerative clustering and DBSCAN. In a multi-criteria evaluation, our approach achieved significantly better performance on majority of the measures, with the advantages that: (i) it does not require to specify the number of clusters beforehand (it is open ended); (ii) it is online and can find clusters in real time; (iii) it has constant time complexity; (iv) and it is memory efficient as it does not keep the data samples in memory. Overall, it has managed to discover 616 of the total 717 activities. Because it discovers clusters of activities in real time, it is ideal to work alongside an active learning system.

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Context-based ensemble method for human energy expenditure estimation

Cited by 46 publications

References 22 publications

Multi-sensor fusion based on multiple classifier systems for human activity identification

Multi-sensor fusion based on multiple classifier systems for human activity identification

A Comparative Review of Footwear-Based Wearable Systems

Unsupervised online activity discovery using temporal behaviour assumption

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