A forecasting framework for predicting perceived fatigue: Using time series methods to forecast ratings of perceived exertion with features from wearable sensors

Hajifar, Sahand; Sun, Hongyue; Megahed, Fadel M.; Jones‐Farmer, L. Allison; Rashedi, Ehsan; Cavuoto, Lora

doi:10.1016/j.apergo.2020.103262

Cited by 34 publications

(17 citation statements)

References 54 publications

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“…In contrast, the authors in Refs. [ 95 , 96 ] used motion data, proposing a novel, non-intrusive method for monitoring the physical fatigue of construction workers using computer vision technology. Motion data were collected using a 3D motion capture algorithm and IMU sensors.…”

Section: Smart Wearables and Occupational Physical Fatigue Detectionmentioning

confidence: 99%

“…In addition, Ref. [ 96 ] used time series methods to predict physical fatigue. To achieve their goal, they used ratings of perceived exertion (RPE) and gait data.…”

Section: Smart Wearables and Occupational Physical Fatigue Detectionmentioning

confidence: 99%

“…Furthermore, in Ref. [ 96 ], the authors compared the use of five time series algorithms for detecting fatigue at work. For this purpose, they applied naïve method, autoregression (AR), autoregressive integrated moving average (ARIMA), vector autoregression (VAR), and vector error correction model (VECM).…”

Section: Smart Wearables and Occupational Physical Fatigue Detectionmentioning

confidence: 99%

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Smart Wearables for the Detection of Occupational Physical Fatigue: A Literature Review

Moshawrab

Adda

Bouzouane

et al. 2022

Sensors

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Today’s world is changing dramatically due to the influence of various factors. Whether due to the rapid development of technological tools, advances in telecommunication methods, global economic and social events, or other reasons, almost everything is changing. As a result, the concepts of a “job” or work have changed as well, with new work shifts being introduced and the office no longer being the only place where work is done. In addition, our non-stop active society has increased the stress and pressure at work, causing fatigue to spread worldwide and becoming a global problem. Moreover, it is medically proven that persistent fatigue is a cause of serious diseases and health problems. Therefore, monitoring and detecting fatigue in the workplace is essential to improve worker safety in the long term. In this paper, we provide an overview of the use of smart wearable devices to monitor and detect occupational physical fatigue. In addition, we present and discuss the challenges that hinder this field and highlight what can be done to advance the use of smart wearables in workplace fatigue detection.

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Section: Smart Wearables and Occupational Physical Fatigue Detectionmentioning

confidence: 99%

“…In addition, Ref. [ 96 ] used time series methods to predict physical fatigue. To achieve their goal, they used ratings of perceived exertion (RPE) and gait data.…”

Section: Smart Wearables and Occupational Physical Fatigue Detectionmentioning

confidence: 99%

Section: Smart Wearables and Occupational Physical Fatigue Detectionmentioning

confidence: 99%

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Smart Wearables for the Detection of Occupational Physical Fatigue: A Literature Review

Moshawrab

Adda

Bouzouane

et al. 2022

Sensors

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“…is section discusses related work from various aspects to understand the problem addressed in this paper. [23,24]. However, the error is relatively large for data with a complex structure.…”

Section: Related Workmentioning

confidence: 99%

Multimodal Data Guided Spatial Feature Fusion and Grouping Strategy for E-Commerce Commodity Demand Forecasting

Cai

Song

Wei

2021

Mobile Information Systems

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E-commerce offers various merchandise for selling and purchasing with frequent transactions and commodity flows. An accurate prediction of customer needs and optimized allocation of goods is required for cost reduction. The existing solutions have significant errors and are unsuitable for addressing warehouse needs and allocation. That is why businesses cannot respond to customer demands promptly, as they need accurate and reliable demand forecasting. Therefore, this paper proposes spatial feature fusion and grouping strategies based on multimodal data and builds a neural network prediction model for e-commodity demand. The designed model extracts order sequence features, consumer emotional features, and facial value features from multimodal data from e-commerce products. Then, a bidirectional long short-term memory network- (BiLSTM-) based grouping strategy is proposed. The proposed strategy fully learns the contextual semantics of time series data while reducing the influence of other features on the group’s local features. The output features of multimodal data are highly spatially correlated, and this paper employs the spatial dimension fusion strategy for feature fusion. This strategy effectively obtains the deep spatial relations among multimodal data by integrating the features of each column in each group across spatial dimensions. Finally, the proposed model’s prediction effect is tested using e-commerce dataset. The experimental results demonstrate the proposed algorithm’s effectiveness and superiority.

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“…Maintenance and prediction as well as M2P interaction based on operator position also require smarter operator typology [ 2 ]. A healthy operator uses a wearable tracker by which his/her health-related metrics are monitored and his/her sudden movements (e.g., fall of the operator) are detected [ 2 , 4 , 5 , 6 ]. Smarter operator, on the other hand, provides intelligent personal assistant to the operator [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Heterogeneity Sources for Occupational Task Recognition via Transfer Learning

Hajifar

Lamooki

Cavuoto

et al. 2021

Sensors

Self Cite

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Human activity recognition has been extensively used for the classification of occupational tasks. Existing activity recognition approaches perform well when training and testing data follow an identical distribution. However, in the real world, this condition may be violated due to existing heterogeneities among training and testing data, which results in degradation of classification performance. This study aims to investigate the impact of four heterogeneity sources, cross-sensor, cross-subject, joint cross-sensor and cross-subject, and cross-scenario heterogeneities, on classification performance. To that end, two experiments called separate task scenario and mixed task scenario were conducted to simulate tasks of electrical line workers under various heterogeneity sources. Furthermore, a support vector machine classifier equipped with domain adaptation was used to classify the tasks and benchmarked against a standard support vector machine baseline. Our results demonstrated that the support vector machine equipped with domain adaptation outperformed the baseline for cross-sensor, joint cross-subject and cross-sensor, and cross-subject cases, while the performance of support vector machine equipped with domain adaptation was not better than that of the baseline for cross-scenario case. Therefore, it is of great importance to investigate the impact of heterogeneity sources on classification performance and if needed, leverage domain adaptation methods to improve the performance.

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A forecasting framework for predicting perceived fatigue: Using time series methods to forecast ratings of perceived exertion with features from wearable sensors

Cited by 34 publications

References 54 publications

Smart Wearables for the Detection of Occupational Physical Fatigue: A Literature Review

Smart Wearables for the Detection of Occupational Physical Fatigue: A Literature Review

Multimodal Data Guided Spatial Feature Fusion and Grouping Strategy for E-Commerce Commodity Demand Forecasting

Investigation of Heterogeneity Sources for Occupational Task Recognition via Transfer Learning

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