Classification Analysis Of Sleep Quality On The Active Performance - Tan, Hann Woei (Universiti Sains Malaysia, 2019)
Sufficient sleep is an important aspect to maintain active for daily activities. Good sleep quality essentially helps our brain to concentrate, store memory and stay active for physical activities. Most of the previous studies on sleeping analysis focused on the factors affecting sleeping patterns instead of the sleep quality effecting on daily performances. Therefore, this study is conducted to (i) determine the significant factors affecting the sleep qualities, (ii) classify sleeping quality patterns and (iii) investigate the relationship between sleeping patterns and active performances. This research project involved an experimental and a survey study on the sleeping qualities for active performances during the daytime. Data analysis was performed based on data mining approach using the Waikato Environment for Knowledge Analysis (WEKA) software. There were 20 voluntary participants which are the students of University Sains Malaysia Engineering Campus. Two Android sleep tracking apps: PrimeNap and Runtastic were applied to measure the sleeping patterns. The data was then compiled on Google Form along with the active performance survey questionnaires, the classification of the data into three classes show accuracies ranged from 63.2-92.8% on both PrimeNap and Runtastic respectively. The data is further analysed in the knowledge discovery process and the total sleep period, sleep cycle and REM sleep period were identified as the three most significant attributes corresponding to determine the sleep quality. However, the sleeping quality could not describe the conditions of daily active performances.