Edge optimized and personalized lifelogging framework using ensembled metaheuristic algorithms.

• A four-layer edge optimized and user-personalized framework for life-logging human activities is proposed. • A lightweight edge intelligence module requiring low computation is designed, which reduces data transmission to the cloud. • A novel Max Score Pooling (MSP) algorithm based on ensembled metaheuristic algorithms is developed for the user-specific parameter selection. Additionally, it makes the framework resilient to certain sensor failures. • The MSP optimized Decision Tree classifier is developed for real-time activity recognition in the Spark environment. • Experimental evaluation demonstrates the outperformance of the proposed model with existing ones. The fostered use of smart wearables for lifelogging daily activities has fuelled massive data generation. Lack of personalization, massive network traffic, increased latency, and high vulnerability to missing and noisy data are the significant impediments that existing frameworks face. This paper proposes a user-personalized and edge-optimized four-layer framework for lifelogging activities to address these impediments. A lightweight Edge Intelligence (EI) module with low computation requirements is designed to reduce data transmission to the cloud, lowering energy consumption. A novel Max Score Pooling (MSP) algorithm based on ensembled metaheuristic algorithms is proposed to provide a user-specific and optimized set of features. MSP optimized Decision Tree (MSP-DT) classifier is developed for real-time activity recognition in the Spark environment. The classifier's performance is calibrated regularly, making the framework resilient to sensor failure. Experiments demonstrate that the proposed framework can recognize 12 physical activities of different subjects with a mean accuracy of 97.67% and 47.66% reduction in transmitted data. [Display omitted] [ABSTRACT FROM AUTHOR]