Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors

Abstract

As an essential subfield of context awareness, activity awareness, especially daily activity monitoring and fall detection, plays a significant role for elderly or frail people who need assistance in their daily activities. This study investigates the feature extraction and pattern recognition of surface electromyography (sEMG), with the purpose of determining the best features and classifiers of sEMG for daily living activities monitoring and fall detection. This is done by a serial of experiments. In the experiments, four channels of sEMG signal from wireless, wearable sensors located on lower limbs are recorded from three subjects while they perform seven activities of daily living (ADL). A simulated trip fall scenario is also considered with a custom-made device attached to the ankle. With this experimental setting, 15 feature extraction methods of sEMG, including time, frequency, time/frequency domain and entropy, are analyzed based on class separability and calculation complexity, and five classification methods, each with 15 features, are estimated with respect to the accuracy rate of recognition and calculation complexity for activity monitoring and fall detection. It is shown that a high accuracy rate of recognition and a minimal calculation time for daily activity monitoring and fall detection can be achieved in the current experimental setting. Specifically, the Wilson Amplitude (WAMP) feature performs the best, and the classifier Gaussian Kernel Support Vector Machine (GK-SVM) with Permutation Entropy (PE) or WAMP results in the highest accuracy for activity monitoring with recognition rates of 97.35% and 96.43%. For fall detection, the classifier Fuzzy Min-Max Neural Network (FMMNN) has the best sensitivity and specificity at the cost of the longest calculation time, while the classifier Gaussian Kernel Fisher Linear Discriminant Analysis (GK-FDA) with the feature WAMP guarantees a high sensitivity (98.70%) and specificity (98.59%) with a short calculation time (65.586 ms), making it a possible choice for pre-impact fall detection. The thorough quantitative comparison of the features and classifiers in this study supports the feasibility of a wireless, wearable sEMG sensor system for automatic activity monitoring and fall detection.

Keywords: activity monitoring; classifier; fall detection; feature extraction; surface electromyography (sEMG).

Figure 1

Muscles channels and sEMG sensors placement. (a) Forward muscle; (b) Backward muscle; (c) Forward sensors placement; (d) Backward sensors placement.

Figure 2

Seven activities of daily living and trip-fall in the experiment. (a) stand-to-squat; (b) squat-to-stand; (c) stand-to-sit ; (d) sit-to-stand; (e) walking, stair-ascending and stair-descending; (f) trip-fall.

Figure 3

Examples of raw sEMG signals of some typical activities.

Figure 4

Class separability index values (Error bar: standard error).

Figure 5

The separability index and calculation time of fifteen features.

Figure 6

The value of the performance index with various w values.

Figure 7

Average of Recognition Accuracy Rates (error bar: standard error).

Figure 8

The average recognition accurate rates vs. the average calculation time of 15 features across the classifiers.

Figure 9

Sensitivity (SEN) and specificity (SPE). (a) Average sensitivity (error bar: standard error), (b) Average specificity (error bar: standard error).

Figure 9

Sensitivity (SEN) and specificity (SPE). (a) Average sensitivity (error bar: standard error), (b) Average specificity (error bar: standard error).

Figure 10

Sensitivity, Specificity, and recognition accurate rate of two specific feature types. (a) Sensitivity, Specificity, and whole recognition rate of WAMP. (b) Sensitivity, Specificity, and Recognition Rate of MA.