Li, Xiaoyi2023-03-012023-03-012014Thesis 9436https://unbscholar.lib.unb.ca/handle/1882/13654Human cognitive function decreases with aging thereby increasing the risk of fall. A fall can cause severe injury, long hospitalization time, and often affects an individual's quality of life. Fall data obtained from a nursing home in New Brunswick shows that 50% of fall incidents happen during night sleep. In this thesis, a fall detection and prediction system is developed in which sleep brain activity is captured and analyzed in real time. A fall classification method for the brain signals captured as Electroencephalography is developed using Support Vector Machine (SVM) and Time-Frequency Kernels. In this fall-prediction system, a patient wears a hat with a light weight wireless biosensor device to capture EEG signals, and then sent wirelessly to a back-end server for real-time analysis of the data sets. Over the supervised training period, the server gets enough data from a subject and starts to learn the threshold value between normal and abnormal EEG for the subject. When the system is trained with signature data, it gives more accurate detection result.text/xmlix, 67 pages ; illustrations (some colour)electronicen-CAhttp://purl.org/coar/access_right/c_abf2Electroencephalography--Age factors--Data processing.Falls (Accidents) in old age.Support vector machines.Wireless sensor networks.Biosensors.Sleep--Age factors.Sleep--Physiological aspects--Observations.Bioinformatics.master thesis2016-03-14Light, JanetComputer Science