Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IEEE Transactions on Knowledge and Data Engineering
The Cyclic Model Analysis on Sequential Patterns
IEEE Transactions on Knowledge and Data Engineering
Adaptive Body Posture Analysis for Elderly-Falling Detection with Multisensors
IEEE Intelligent Systems
An intelligent telecardiology system using a wearable and wireless ECG to detect atrial fibrillation
IEEE Transactions on Information Technology in Biomedicine - Special section on new and emerging technologies in bioinformatics and bioengineering
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
Applying wearable solutions in dependent environments
IEEE Transactions on Information Technology in Biomedicine
The Escort System: A Safety Monitor for People Living with Alzheimer's Disease
IEEE Pervasive Computing
PARM—An Efficient Algorithm to Mine Association Rules From Spatial Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Human Body Posture Classification by a Neural Fuzzy Network and Home Care System Application
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Due to the pressure from work load and daily life, there is an increase in geriatric depression and arrhythmia population. However, some people may not notice or have no idea about the symptom of depression and arrhythmia. More research input is needed to diagnose severity of depression and arrhythmia at an early stage. To help users examine their physical fitness and mental health condition before outpatient service, we apply data mining strategy to discover association rules from responded questionnaire, including geriatric depression, BAI, ASRM, and PSQI. To obtain informative analytical results, multitudes of simulations are performed on 25,000 data stored in our database. We also propose an effective heartbeat monitoring ECG real-time detection system for homecare service, which uses the ECG sensors and a wireless sensor network technology to detect the subject's heartbeats and their variations. In addition, the MIT-BIH database is used to analyze arrhythmia. A fuzzy model is proposed to discriminate between normal heartbeats and arrhythmia. Experimental results show that an average accuracy of 95.42% is achieved by the proposed system. This evidence verifies that the hybrid intelligent model is effective in medical related applications.