Burst detection in gesture recognization for emergency communication using wearable data gloves

The clinical emergency communication between a patient and medical agents is crucial and requires accurate interpretations. A novel approach using wearable devices come to address this issue in recent days. The accuracy of the system's output depends on the precise representation of two important things namely correct gestures and burst timings. The proposed burst detection algorithm calculates the intensity and latency of the burst occurrence in the signal generated during the gesture process. The finding of bursts helps to interpret the gesture signal and plays a vital role in identification and patterning the communication and response. This burst helps in preprocessing of the signals and selecting the important feature of the signals for further classification and interpretations. The more active channels that produce effective bursts are identified by the proposed algorithm and helps in hardware optimization.