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

Abstract

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.

Değiştirilebilir konum süresine sahip takip cihazlarında kümeleme parametrelerinin tahmini ve anomali tespiti

“…Farklı girdi parametrelerine göre DBSCAN algoritması çıktıları (Dbscan algorithm outputs according to different input parameters) algoritmasıdır çünkü bu yaklaşım makine öğrenimi yöntemleri ile önemli ölçüde örtüşür [1]. Basit regresyon fonksiyonun birden fazla bağımsız değişkene ve tek bir çıktıya sahip olan geliştirilmiş yöntemine ise çoklu regresyon yöntemi adı verilir [43]. Eş.…”