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Diabetic sensorimotor polyneuropathy (DSPN) is one of the prevalent forms ofneuropathy affected by diabetic patients that involves alterations inbiomechanical changes in human gait. In literature, for the last 50 years,researchers are trying to observe the biomechanical changes due to DSPN bystudying muscle electromyography (EMG), and ground reaction forces (GRF).However, the literature is contradictory. In such a scenario, we are proposingto use Machine learning techniques to identify DSPN patients by using EMG, andGRF data. We have collected a dataset consists of three lower limb muscles EMG(tibialis anterior (TA), vastus lateralis (VL), gastrocnemius medialis (GM) and3-dimensional GRF components (GRFx, GRFy, and GRFz). Raw EMG and GRF signalswere preprocessed, and a newly proposed feature extraction technique schemefrom literature was applied to extract the best features from the signals. Theextracted feature list was ranked using Relief feature ranking techniques, andhighly correlated features were removed. We have trained different ML models tofind out the best-performing model and optimized that model. We trained theoptimized ML models for different combinations of muscles and GRF componentsfeatures, and the performance matrix was evaluated. This study has foundensemble classifier model was performing in identifying DSPN Severity, and weoptimized it before training. For EMG analysis, we have found the best accuracyof 92.89% using the Top 14 features for features from GL, VL and TA musclescombined. In the GRF analysis, the model showed 94.78% accuracy by using theTop 15 features for the feature combinations extracted from GRFx, GRFy and GRFzsignals. The performance of ML-based DSPN severity classification models,improved significantly, indicating their reliability in DSPN severityclassification, for biomechanical data.

Evaluating Performance of Machine Learning Models for Diabetic  Sensorimotor Polyneuropathy Severity Classification using Biomechanical  Signals during Gait