A Fuzzy Logic-Based Personalized Method to Classify Perceived Exertion in Workplaces Using a Wearable Heart Rate Sensor

Pancardo, Pablo; Hernández-Nolasco, José Adán; Acosta, Francisco

doi:10.1155/2018/4216172

Cited by 11 publications

(15 citation statements)

References 26 publications

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“…In total, they included 406 participants. From them, 17 out of the 19 (two using the same sample (Pancardo, Acosta, Hernandez-Nolasco, Wister, & Lopez-de-Ipina, 2015;Pancardo, Hernández-Nolasco, & Acosta-Escalante, 2018)) indicated gender distributions, denoting 12.86% of women and 87.14% of men participants. All subjects were part of the healthy active working population.…”

Section: Resultsmentioning

confidence: 99%

3rd Symposium on Occupational Safety and Health Proceedings Book

2019

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Introduction: Osteopathy is an emergent health-care profession, present in most of the developed world and since 2013, regulated in Portugal. Osteopaths intervene in a range of health issues in which pain is a very common aspect. Pain is a worldwide problem, affecting all aspects of society. Measurement of pain objectively is yet not possible; even considered unreliable in many circumstances, the standard is selfreported questionnaires. A variety of different scales were used to measure the intensity of pain in Osteopathic research; however, the criteria of the options are not always clear or reported. This review aims to analyze and critically compare the different characteristics of the most used questionnaires by osteopathic researchers for assessing the intensity of pain in randomized controlled trials (RCT). Methodology: A literature search was conducted using 7 electronic databases. This search was conducted for RCT articles studying the efficacy/effectiveness of the osteopathic manipulative treatment (OMT) intervention in pain. The findings followed the PRISMA statement. Included studies were assessed for the risk of bias (RoB) using the Jadad score. Results and Discussion: 123 studies were identified, and after removal of duplicates and application of the eligibility criteria, 21 articles were included for this review. Nine studies used the Visual Analogue Scale (VAS) 10cm scale, seven the Numeric Rating Scale (NRS) 11 points scale, the remaining used other options. Although similar, there are differences in these two scales that may affect the outcomes. Justification of the choice of the evaluation instrument was not always present and not associated with the methodology and the target population. The overall quality of the studies, in terms of RoB, was considered good. Conclusion: VAS and NRS are the choices of most authors. Authors in Osteopathy, or other health care professions should be very clear about the reasons behind the choices for measuring the intensity of pain; these should fit the objectives and study design.

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Section: Resultsmentioning

confidence: 99%

3rd Symposium on Occupational Safety and Health Proceedings Book

2019

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“…Comparing with the proposed model, the past studies had presented many machine learning algorithms for similar approaches [47,48,49]. Our previous work experienced the BPNN method to recognize different types of the frozen shoulder exercises [11].…”

Section: Implementation and Discussionmentioning

confidence: 99%

Personalized Rehabilitation Recognition for Ubiquitous Healthcare Measurements

Kan

Kuo

Lin

2019

Sensors

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The physical therapeutic application needs personalized rehabilitation recognition (PRR) for ubiquitous healthcare measurements (UHMs). This study employed the adaptive neuro-fuzzy inference system (ANFIS) to generate a PRR model for a self-development system of UHM. The subjects wore a sensor-enabled wristband during physiotherapy exercises to measure the scheduled motions of their limbs. In the model, the sampling data collected from the scheduled motions are labeled by an arbitrary number within a defined range. The sample datasets are referred as the design of an initial fuzzy inference system (FIS) with data preprocessing, feature visualizing, fuzzification, and fuzzy logic rules. The ANFIS then processes data training to adjust the FIS for optimization. The trained FIS then can infer the motion labels via defuzzification to recognize the features in the test data. The average recognition rate was higher than 90% for the testing motions if the subject followed the sampling schedule. With model implementation, the middle section of motion datasets in each second is recommended for recognition in the UHM system which also includes a mobile App to retrieve the personalized FIS in order to trace the exercise. This approach contributes a PRR model with trackable diagrams for the physicians to explore the rehabilitation motions in details.

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“…We used the method proposed in [17], where an ergonomic method based on the heart rate of the individual is used, but with a personalized approach to make it more precise. The result represents the particular effort that an activity imposes on each worker.…”

Section: B Personalized Workload Assessmentmentioning

confidence: 99%

“…Another input variable is the individualized Relative Cardiac Cost (RCC), which is the result of the calculation of the personalized Chamoux defined in [17]. The RCC variable contributes to the personalization of the calculation since assigning a generic caloric consumption value to physical activity is not precise nor personalized [18].…”

Section: B Personalized Workload Assessmentmentioning

confidence: 99%

Dynamic Membership Functions for Context-Based Fuzzy Systems

et al. 2021

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In fuzzy systems, membership functions determine the groups to which a variable can belong to, and these groups are static or only have one setting in some aspect. However, fuzzy systems typically require to model the dynamic environment they represent. Still, this behavior does not reflect the membership groups in a conventional way. Thus, conventional fuzzy systems are not capable of reflecting the dynamics of the real-time context. The approach presented consists of a fuzzy system where the membership functions can have dynamic transformations, according to contextual variables that influence them, to have a model that adjusts in real time. The membership functions' dynamism is achieved because the form in the sets can be transformed; the maximum degree of membership of a set is in a range of zero to one; and, the location of the sets in the discourse universe can vary dynamically. The results show the feasibility of a context-based fuzzy system with dynamic membership functions built-in real time, that has been influenced by contextual variables. Therefore, unlike other proposals, this approach allows modeling the influence of the context on a fuzzy system, making it more adjusted to reality. To illustrate our proposed approach, a case study is presented where a fuzzy system estimates the heat stress in a work environment that uses data acquired from wearable devices. This system automatically generates the following indicators: (i) energy level wasted while performing a physical activity, (ii) personalized measurement of workload level, and (iii) measurement of Occupational Heat Stress (OHS).

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A Fuzzy Logic-Based Personalized Method to Classify Perceived Exertion in Workplaces Using a Wearable Heart Rate Sensor

Cited by 11 publications

References 26 publications

3rd Symposium on Occupational Safety and Health Proceedings Book

3rd Symposium on Occupational Safety and Health Proceedings Book

Personalized Rehabilitation Recognition for Ubiquitous Healthcare Measurements

Dynamic Membership Functions for Context-Based Fuzzy Systems

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