Estimating electromyography responses using an adaptive neuro-fuzzy inference system with subtractive clustering

Cakit E., Karwowski W.

HUMAN FACTORS AND ERGONOMICS IN MANUFACTURING & SERVICE INDUSTRIES, cilt.27, sa.4, ss.177-186, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 27 Sayı: 4
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1002/hfm.20701
  • Dergi Adı: HUMAN FACTORS AND ERGONOMICS IN MANUFACTURING & SERVICE INDUSTRIES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Sayfa Sayıları: ss.177-186
  • Anahtar Kelimeler: electromyography, linear regression, muscular efforts, neuro-fuzzy model, posture, TRUNK MUSCLES, EMG SIGNALS, SURFACE EMG, STATISTICAL-METHODS, IDENTIFICATION, CLASSIFICATION, MOMENTS, ANFIS, MODEL
  • Gazi Üniversitesi Adresli: Hayır

Özet

This study aimed to develop an adaptive neuro-fuzzy inference system (ANFIS) approach to estimate the normalized electromyography (NEMG) responses, where the independent variables are demographic variables including population, gender, ethnicity, age, height, weight, posture, and muscle groups. The study groups comprised 75 US-based (54 males and 21 females) and 10 Japan-based (all males) automobile assembly workers. A total of 65 inputs and 1 output reflecting the NEMG values were considered at the beginning. After correlating analysis results, a total of 35 significant predictors were considered for both ANFIS and regression models. The data were partitioned into two datasets, one for training (70% of all data) and one for validation (30% of all data). In addition to a soft-computing approach, a multiple linear regression (MLR) analysis was also performed to evaluate whether or not the ANFIS approach showed superior predictive performance compared to a classical statistical approach. According to the performance comparison, ANFIS had better predictive accuracy than MLR, as demonstrated by the experimental results. Overall, this study demonstrates that the ANFIS approach can predict normalized EMG responses according to subjects' demographic variables, posture, and muscle groups.