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.
Highlights
As a result of an aging population, the number of elderly or frail people who need help in their daily activities is rapidly increasing [1,2,3]
A few unexpected simulated trip falls induced by a custom-made device attached to the ankle, were interspersed among the normal walking trials
The participants repeated the procedure for 10 times in each experiment day, making sure that the total times of each activity and trip fall was at least 30, and the order of activities stayed same for each experiment
Summary
As a result of an aging population, the number of elderly or frail people who need help in their daily activities is rapidly increasing [1,2,3]. This leads to a series of problems in caring for older people and people with medical disabilities. Falls are the leading cause of trauma and death among people 65 or older and the resulting health care costs represent a serious public burden [1] Helping this group of Sensors 2017, 17, 1229; doi:10.3390/s17061229 www.mdpi.com/journal/sensors. The other important applications are fall detection [5,6] and rehabilitation [7]
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