Daily Activity Monitoring and Fall Detection Based on Surface Electromyography and Plantar Pressure

Abstract EN

Falls among the elderly comprise a major health problem.

Daily activity monitoring and fall detection using wearable sensors provide an important healthcare system for elderly or frail individuals.

We investigated the classification accuracy of daily activity and fall data based on surface electromyography (sEMG) and plantar pressure signals.

sEMG and plantar pressure signals were collected, and their features were extracted.

Suitable features were selected and combined for posture transition, gait, and fall using the Fisher class separability index.

A feature-level fusion method, named as the global canonical correlation analysis of weighting genetic algorithm, was proposed to reduce dimensions.

For the problem in which the number of daily activities is considerably more than the number of fall activities, Weighted Kernel Fisher Linear Discriminant Analysis (WKFDA) was proposed to classify gait and fall.

Double Parameter Kernel Optimization based on Extreme Learning Machine (DPK-OMELM) was used to classify activities.

Results showed that the classification accuracy of the posture transition is 100%, and the accuracy of gait and fall classified using WKFDA can reach 98%.

For all types of posture transition, gait, and fall, sensitivity, specificity, and accuracy are over 96%.