+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

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

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

Sensors 17(6):

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.

Please choose payment method:

(PDF emailed within 0-6 h: $19.90)

Accession: 059705522

Download citation: RISBibTeXText

PMID: 28555016

DOI: 10.3390/s17061229

Related references

Shift invariant feature extraction for sEMG-based speech recognition with electrode grid. Conference Proceedings 2013: 5797-5800, 2013

Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors. Sensors 18(5):, 2018

Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors. Sensors 18(2):, 2018

Human activity monitoring system based on wearable sEMG and accelerometer wireless sensor nodes. Biomedical Engineering Online 17(Suppl 1): 132, 2018

Covariance matrix based fall detection from multiple wearable sensors. Journal of Biomedical Informatics 94: 103189, 2019

Novel feature modelling the prediction and detection of sEMG muscle fatigue towards an automated wearable system. Sensors 10(5): 4838-4854, 2010

Assessing fall risk using wearable sensors: a practical discussion. A review of the practicalities and challenges associated with the use of wearable sensors for quantification of fall risk in older people. Zeitschrift für Gerontologie und Geriatrie 45(8): 694-706, 2012

Survey on fall detection and fall prevention using wearable and external sensors. Sensors 14(10): 19806-19842, 2014

Activity Monitoring and Heart Rate Variability as Indicators of Fall Risk: Proof-of-Concept for Application of Wearable Sensors in the Acute Care Setting. Journal of Gerontological Nursing 43(7): 53-62, 2017

Wearable sensors for reliable fall detection. Conference Proceedings 4: 3551-3554, 2005

Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients. Plos one 10(4): E0124414, 2015

Evaluation of novel algorithm embedded in a wearable sEMG device for seizure detection. Conference Proceedings 2012: 2048-2051, 2012

A two-dimensional matrix image based feature extraction method for classification of sEMG: A comparative analysis based on SVM, KNN and RBF-NN. Journal of X-Ray Science and Technology 25(2): 287-300, 2017

An incremental learning method based on probabilistic neural networks and adjustable fuzzy clustering for human activity recognition by using wearable sensors. IEEE Transactions on Information Technology in Biomedicine 16(4): 691-699, 2012

A new feature extraction method based on autoregressive power spectrum for improving sEMG classification. Conference Proceedings 2013: 5746-5749, 2013