Author(s):
Preeti Agarwal
Mansaf Alam
Abstract:
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.
Documentation:
https://doi.org/10.1016/j.compeleceng.2022.107884
References:
- L. Syed et al.Smart healthcare framework for ambient assisted living using IoMT and big data analytics techniquesFuture Gener Comput Syst(2019)
- A.R. Javed et al.A smartphone sensors-based personalized human activity recognition system for sustainable smart citiesSustain Cities Soc(2021)
- P. Agarwal et al.Lightweight deep learning model for human activity recognition on edge devicesProcedia Comput Sci(2020)
- G. Chetty et al.Smart phone based data mining for human activity recognitionProcedia Comput Sci(2015)
- T. Zebin et al.Human activity recognition with inertial sensors using a deep learning approach
- S.W. Pienaar et al.Human activity recognition using LSTM-RNN deep neural network architecture
- K. Xia et al.LSTM-CNN architecture for human activity recognitionIEEE Access(2020)
- D. Anguita et al.A public domain dataset for human activity recognition using smartphones
- S. Khatun et al.Fully-automated human activity recognition with transition awareness from wearable sensor data for MHealth
- S. Ha et al.Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors
- Foundation W. Wireshark Go deep. Wireshark Found 2016. https://www.wireshark.org/ (accessed September 22,…
- Banos O, Garcia R, Saez A. UCI Machine Learning Repository: MHEALTH Dataset Data Set 2019….
- A.V. EremeevA geetic algorithm with tournament selection as a local search methodJ Appl Ind Math(2012)
- Z.W. Geem et al.A new heuristic optimization algorithm: harmony searchSimulation(2001)
- D. Karaboga et al.A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithmJ Glob Optim(2007)