Power consumption reduction for IoT devices thanks to Edge-AI: Application to human activity recognition

Alessandrini, 2021, Recurrent neural network for human activity recognition in embedded systems using ppg and accelerometer data, Electronics (Switzerland), 10 Mekruksavanich, 2021, Deep convolutional neural network with rnns for complex activity recognition using wrist-worn wearable sensor data, Electronics (Switzerland), 10 Xu, 2020, Human activity recognition and embedded application based on convolutional neural network, J. Artif. Intell. Technol., 1, 51, 10.37965/jait.2020.0051 Shiranthika, 2020, Human activity recognition using CNN & LSTM, 1 Wang, 2019, Deep learning for sensor-based activity recognition: A survey, Pattern Recognit. Lett., 119, 3, 10.1016/j.patrec.2018.02.010 Lee, 2017, Human activity recognition from accelerometer data using convolutional neural network, 131 Tan, 2021, Human activity recognition for people with knee osteoarthritis—A proof-of-concept, Sensors, 21, 3381, 10.3390/s21103381 Novac, 2021, Quantization and deployment of deep neural networks on microcontrollers, Sensors, 21, 2984, 10.3390/s21092984 De Vita, 2020, Low-power HWAccelerator for AI edge-computing in human activity recognition systems, 291 Georgakakis, 2011, An analysis of bluetooth, zigbee and bluetooth low energy and their use in WBANs, 168 Siekkinen, 2012, How low energy is bluetooth low energy? Comparative measurements with ZigBee/802.15.4, 232 Dementyev, 2013, Power consumption analysis of bluetooth low energy, ZigBee and ANT sensor nodes in a cyclic sleep scenario, 1 Caiazza, 2022, Edge computing vs centralized cloud: Impact of communication latency on the energy consumption of LTE terminal nodes, Comput. Commun., 194, 213, 10.1016/j.comcom.2022.07.026 David, 2021, Tensorflow lite micro: Embedded machine learning for tinyml systems, Proc. Mach. Learn. Syst., 3, 800 Shoaib, 2013, Towards physical activity recognition using smartphone sensors, 80 Kwapisz, 2011, Moore, 2011 Kwapisz JR, Weiss GM, Moore SA, activity recognition using cell phone accelerometers, ACM SigKDD Explor. Newslett., 12, 74, 10.1145/1964897.1964918 Anjum, 2013, Activity recognition using smartphone sensors, 914 Khalifa, 2018, HARKE: Human activity recognition from kinetic energy harvesting data in wearable devices, IEEE Trans. Mob. Comput., 17, 1353, 10.1109/TMC.2017.2761744 Aazam, 2021, Task offloading in edge computing for machine learning-based smart healthcare, Comput. Netw., 191, 10.1016/j.comnet.2021.108019 Guinea, 2021, Image-based activity recognition from IMU data, 14 Bianchi, 2019, IoT wearable sensor and deep learning: An integrated approach for personalized human activity recognition in a smart home environment, IEEE Internet Things J., 6, 8553, 10.1109/JIOT.2019.2920283 Zebin, 2019, Design and implementation of a convolutional neural network on an edge computing smartphone for human activity recognition, IEEE Access, 7, 133509, 10.1109/ACCESS.2019.2941836 A. Mhalla, J.-M. Favreau, SMC HAR: Toward Personalized Human Activity Recognition System with Auto-Supervised Learning Framework, Tech. Rep., 2021, pp. 1–14, Publication Title: Procedia Computer Science Volume: 00. Bhat, 2018, Online human activity recognition using low-power wearable devices, 1 Zhu, 2021, Lightweight deep learning model in mobile-edge computing for radar-based human activity recognition, IEEE Internet Things J., 8, 12350, 10.1109/JIOT.2021.3063504 Novac, 2020, Toward unsupervised human activity recognition on microcontroller units, 542 Antonsson, 1985, The frequency content of gait, J. Biomechanics, 18, 39, 10.1016/0021-9290(85)90043-0 Vavoulas, 2016, The mobiact dataset: Recognition of activities of daily living using smartphones, 143 Muaaz, 2020, WiWeHAR: Multimodal human activity recognition using Wi-Fi and wearable sensing modalities, IEEE Access, 8, 164453, 10.1109/ACCESS.2020.3022287 Srivastava, 2014, Dropout: A simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., 15, 1929 Hinton, 2012, Overview of mini-batch gradient descent, Neural Networks Mach. Learn., 575 . Google, TensorFlow lite for microcontrollers, URL https://www.tensorflow.org/lite/microcontrollers. Bulić, 2019, Data transmission efficiency in bluetooth low energy versions, Sensors, 19, 3746, 10.3390/s19173746 2021