An Adaptive Neural-Fuzzy Inference System for Prediction of Muscle Strength of Farmers in India: An Approach for E-Healthcare 4.0 Prevention and Analysis

Debesh Mishra, Suchismita Satapathy, V. K. Jain

Source Title: International Journal of Service Science, Management, Engineering, and Technology (IJSSMET)13(1)

ISSN: 1947-959X|EISSN: 1947-9603|EISBN13: 9781799884378|DOI: 10.4018/IJSSMET.297497

MLA

Mishra, Debesh, et al. "An Adaptive Neural-Fuzzy Inference System for Prediction of Muscle Strength of Farmers in India: An Approach for E-Healthcare 4.0 Prevention and Analysis." IJSSMET vol.13, no.1 2022: pp.1-21. http://doi.org/10.4018/IJSSMET.297497

APA

Mishra, D., Satapathy, S., & Jain, V. K. (2022). An Adaptive Neural-Fuzzy Inference System for Prediction of Muscle Strength of Farmers in India: An Approach for E-Healthcare 4.0 Prevention and Analysis. International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 13(1), 1-21. http://doi.org/10.4018/IJSSMET.297497

Chicago

Mishra, Debesh, Suchismita Satapathy, and V. K. Jain. "An Adaptive Neural-Fuzzy Inference System for Prediction of Muscle Strength of Farmers in India: An Approach for E-Healthcare 4.0 Prevention and Analysis," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET) 13, no.1: 1-21. http://doi.org/10.4018/IJSSMET.297497

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Abstract

In the present study, 13 anthropometric hand dimensions, hand grip strength, push strength, and pull strength of 90 male farmers of Odisha in India were statistically analyzed and then, fuzzy logic toolbox of MATLAB version 2010 was used in order to create the fuzzy inference system (FIS) using ANFIS. The mean hand grip strength with standard deviation was found to be 255.21 N ± 75.46. The average push strength in standing posture for farmers was found to be 193.12 N ± 76.12, whereas pull strength in standing posture was 200.59 N ± 64.02. Very high correlation coefficient i.e. 0.977, 0.994 and 0.990 was obtained between “hand length and hand grip strength”, “hand breadth with thumb and push strength”, and “hand length and pull strength”, respectively. Finally, from the obtained ANFIS models for the prediction of muscle strength, it was concluded that ANFIS could well predict the farmers muscle strength with minimum errors. This will help to evaluate muscle capabilities to avoid musculoskeletal disorders, and in ergonomic design of tools and equipment as a health-care initiative.