A Context-Aware Accurate Wellness Determination (CAAWD) Model for Elderly People Using Lazy Associative Classification

Abstract

Wireless Sensor Network (WSN) based smart homes are proving to be an ideal candidate to provide better healthcare facilities to elderly people in their living areas. Several currently proposed techniques have implementation and usage complexities (such as wearable devices and the charging of these devices) which make these proposed techniques less acceptable for elderly people, while the behavioral analysis based on visual techniques lacks privacy. In this paper, a context-aware accurate wellness determination (CAAWD) model for elderly people is presented, where behavior monitoring information is extracted by using simple sensor nodes attached to household objects and appliances for the analysis of daily, frequent behavior patterns of elderly people in a simple and non-obtrusive manner. A contextual data extraction algorithm (CDEA) is proposed for the generation of contextually comprehensive behavior-training instances for accurate wellness classification. The CDEA presents an activity’s spatial–temporal information along with behavioral contextual correlation aspects (such as the object/appliance of usage and sub-activities of an activity) which are vital for accurate wellness analysis and determination. As a result, the classifier is trained in a more logical manner in the context of behavior parameters which are more relevant for wellness determination. The frequent behavioral patterns are classified using the lazy associative classifier (LAC) for wellness determination. The associative nature of LAC helps to integrate spatial–temporal and related contextual attributes (provided by CDEA) of elderly behavior to generate behavior-focused classification rules. Similarly, LAC provides high accuracy with less training time of the classifier, includes minimum-support behavior patterns, and selects highly accurate classification rules for the classification of a test instance. CAAWD further introduces the ability to contextually validate the authenticity of the already classified instance by taking behavioral contextual information (of the elderly person) from the caregiver. Due to the consideration of spatial–temporal behavior contextual attributes, the use of an efficient classifier, and the ability to contextually validate the classified instances, it has been observed that the CAAWD model out-performs currently proposed techniques in terms of accuracy, precision, and f-measure.

Implementing Rule-based Healthcare Edits

“…H1 (Alternate Hypothesis, µR-µC ≠0 or µR≠µC (2) Where µR denotes the average implementation time of system checks (healthcare edits) through rule-based programming and µC denotes the average implementation time of system checks (healthcare edits) through conventional programming. Implementation time depicts total time spent on each check from analysis to coding and making it operational.…”

“…Healthcare overtime has become an important domain in the field of information technology due to the conjunction of technology and its use in different applications. Use of software technology seem becoming a need for management and processing of healthcare data [1,2] and clinical documents [3]. Quality of healthcare is directly proportional to the quality of data stored in healthcare related software like EHRs, Billing Management Systems, Patient Health Records, Integrated Healthcare Systems [4][5][6].…”

Context Reasoning and Decision Modelling System for IoT Scenario using Fuzzy Logic

“…Rule generation at minimum-support values to ensure that, even with fewer occurrences, training data is not ignored for useful rules; the extracted rules are stored in a database for efficient retrieval when needed. Rule pruning is not done in proposed system [25]. In the test instance arrives at the classifiers for the classification, projections of classification rules are made on the basis of the attributes on test instances from the already derived association rules.…”

Special Issue “Internet of Things for Smart Homes”

“…The proposed system, composed of several smart objects to be incorporated into everyday life, is tested on both final users, i.e., self-sufficient and non-self-sufficient seniors, and caregivers, and the assessment is reasonably satisfactory. A smart solution, based on wirelessly interconnected sensors, for proper wellness ascertainment of older adults, living alone in smart homes, is proposed in [14]. This system strives to afford healthcare monitoring of older people, along with the main aim of higher wellness measurement classification accuracy and precision for better healthcare.…”