The scientist and engineer's guide to digital signal processing
The scientist and engineer's guide to digital signal processing
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Gait Recognition by Gait Dynamics Normalization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Telos: enabling ultra-low power wireless research
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
The evidence framework applied to classification networks
Neural Computation
Proceedings of the 2nd International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments
Multimodal analysis of body sensor network data streams for real-time healthcare
Proceedings of the international conference on Multimedia information retrieval
Mobile Networks and Applications
MARS: a muscle activity recognition system using inertial sensors
Proceedings of the 11th international conference on Information Processing in Sensor Networks
Body sensor network mobile solutions for biofeedback monitoring
Mobile Networks and Applications - Special issue on Wireless and Personal Communications
A survey on fall detection: Principles and approaches
Neurocomputing
[MARS] a real time motion capture and muscle fatigue monitoring tool
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
MARS: a muscle activity recognition system enabling self-configuring musculoskeletal sensor networks
Proceedings of the 12th international conference on Information processing in sensor networks
Hi-index | 0.00 |
The evaluation of the postural control system (PCS) has applications in rehabilitation, sports medicine, gait analysis, fall detection, and diagnosis of many diseases associated with a reduction in balance ability. Standing involves significant muscle use to maintain balance, making standing balance a good indicator of the health of the PCS. Inertial sensor systems have been used to quantify standing balance by assessing displacement of the center of mass, resulting in several standardized measures. Electromyogram (EMG) sensors directly measure the muscle control signals. Despite strong evidence of the potential of muscle activity for balance evaluation, less study has been done on extracting unique features from EMG data that express balance abnormalities. In this paper, we present machine learning and statistical techniques to extract parameters from EMG sensors placed on the tibialis anterior and gastrocnemius muscles, which show a strong correlation to the standard parameters extracted from accelerometer data. This novel interpretation of the neuromuscular system provides a unique method of assessing human balance based on EMG signals. In order to verify the effectiveness of the introduced features in measuring postural sway, we conduct several classification tests that operate on the EMG features and predict significance of different balance measures.