Event Detection of Muscle Activation Using an Electromyogram
Kimoon Kang, Kiwon Rhee, Hyun‐Chool Shin
Abstract
In this study, we proposed a precise onset and offset detection algorithm for muscle activation by using an electromyogram (EMG). The adaptive threshold was determined using the constant false alarm rate algorithm. The EMG signal was refined by morphological hole filling, which is used to close up and fill out missing information. By exploiting the EMG amplitude ratio in two channels, we significantly improved the offset detection performance. The proposed method does not require a training process, unlike conventional methods. The experimental results indicated that the estimated errors for both the onset and offset detection are lower than those obtained using two of the conventional methods.
Topics & Concepts
Offset (computer science)Computer scienceElectromyographyArtificial intelligencePattern recognition (psychology)Speech recognitionPhysical medicine and rehabilitationMedicineProgramming languageMuscle activation and electromyography studiesEEG and Brain-Computer InterfacesAdvanced Sensor and Energy Harvesting Materials