Multicolumn Bidirectional Long Short-Term Memory for Mobile Devices-Based Human Activity Recognition

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Deep Learning for Sensor-based Human Activity Recognition (2020, background): “al. 2017 [94]; Saeedi et al. 2017 [130]; Morales et al. 2016 [99]; Jiang et al. 2018 [66]; Khan et al. 2018 [72]; Zheng et al. 2019 [192]; Gjoreski et al. 2019 [43] Time Concept Drift Tao et al. 2016 [143]; Gudur et al.2019 [50] Concept Evolution Nair et al. 2019 [102] Open-Set Yang et al. 2019 [165”
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2021, methodology): “Bidirectional LSTMs, having both past and future recurrent connections, were used in [166, 271] to classify activities.”
Smartphone Sensors for Health Monitoring and Diagnosis (2019, methodology): “In addition, it should be noted that there are published research results that used external inertial measurement units (IMUs) for activity recognition [7,22,142].”
Human Activity Recognition using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey (2020, methodology): “The secondmost usedmodel was the Long Short-TermMemory (LSTM)model, whichwas used in 17 papers [35, 40, 67, 70, 82, 99, 119, 125, 126, 137, 142, 158, 167, 174, 177, 211, 217].”