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

Xugang Xi,Wenjun Jiang,Zhi-Zeng Luo,Zhong Lü,Seyed M Miran

doi:10.1155/2020/9532067

Abstract

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%.

Highlights

Surface electromyography provides information about the movement of different muscle groups during various activities [17]
Chen et al [23] fabricated a pressure insole that consisted of a footwearable interface and four force-sensing resistor (FSR) pressure sensors. e discrete contact force distribution signal was used to recognize the pattern of motions
A relatively complete description of features is formed by using complementary information, which can improve the reliability of recognition

Summary

Materials and Methods

Walking, going upstairs, and going downstairs are controlled at a speed of about 1 m/s. As it can be seen, the participant is instructed to sit on the ground, keep the upper body straight, and the buttocks are about 20 cm apart from the heel. E going upstairs-falling activity is shown, in which the test pad is placed on the above steps of the tripped step, and the participants fall forward and land on their knees. E walkingfalling is shown in Figures 1(g)–1(i), in which the participant falls on the ground when tripping over an obstacle on the ground. Inc., Natick, MA, USA) is used to record sEMG signals. Four sEMG electrodes are used to capture sEMG signals from the vastus lateralis (VL), tibialis anterior (TA), semitendinosus (ST), and gastrocnemius (GT), as shown in Figure 2. e sEMG signals are sampled at 1000 Hz

Walking on a flat surface Going upstairs going downstairs Running

Cn T

WGA GCPV

Gait feature fusion gd group r