Privacy-preserving activity recognition using multimodal sensors in smart office
Tài liệu tham khảo
Parry, 2013, The contribution of office work to sedentary behaviour associated risk, BMC Public Health, 13, 1, 10.1186/1471-2458-13-296
Thorp, 2012, Prolonged sedentary time and physical activity in workplace and non-work contexts: a cross-sectional study of office, customer service and call centre employees, Int. J. Behav. Nutr. Phys. Activity, 9, 1, 10.1186/1479-5868-9-128
Zhang, 2022, Promoting employee health in smart office: A survey, Adv. Eng. Inform., 51, 10.1016/j.aei.2021.101518
Čulić, 2022, Investigation of personal thermal comfort in office building by implementation of smart bracelet: A case study, Energy, 260, 10.1016/j.energy.2022.124973
Catarinucci, 2022, Smart IoT system empowered by customized energy-aware wireless sensors integrated in graphene-based tissues to improve workers thermal comfort, J. Clean. Prod., 360, 10.1016/j.jclepro.2022.132132
Sergi, 2021, An IoT-aware smart system to detect thermal comfort in industrial environments, 1
Alberdi, 2018, Using smart offices to predict occupational stress, Int. J. Ind. Ergon., 67, 13, 10.1016/j.ergon.2018.04.005
Despenic, 2017, Lighting preference profiles of users in an open office environment, Build. Environ., 116, 89, 10.1016/j.buildenv.2017.01.033
Zhou, 2020, Device-free occupant activity recognition in smart offices using intrinsic wi-fi components, Build. Environ., 172, 10.1016/j.buildenv.2020.106737
Tien, 2021, Vision-based human activity recognition for reducing building energy demand, Build. Serv. Eng. Res. Technol., 42, 691, 10.1177/01436244211026120
Andrade-Ambriz, 2022, Human activity recognition using temporal convolutional neural network architecture, Expert Syst. Appl., 191, 10.1016/j.eswa.2021.116287
Abdel-Basset, 2020, Deep learning for heterogeneous human activity recognition in complex iot applications, IEEE Internet Things J.
Han, 2022, Human activity recognition using wearable sensors by heterogeneous convolutional neural networks, Expert Syst. Appl., 198, 10.1016/j.eswa.2022.116764
Muhammad, 2021, Human action recognition using attention based LSTM network with dilated CNN features, Future Gener. Comput. Syst., 125, 820, 10.1016/j.future.2021.06.045
Rodríguez-Gallego, 2023, A collaborative semantic framework based on activities for the development of applications in smart home living labs, Future Gener. Comput. Syst., 140, 450, 10.1016/j.future.2022.10.027
Gravina, 2019, Emotion-relevant activity recognition based on smart cushion using multi-sensor fusion, Inf. Fusion, 48, 1, 10.1016/j.inffus.2018.08.001
Rawashdeh, 2020, A knowledge-driven approach for activity recognition in smart homes based on activity profiling, Future Gener. Comput. Syst., 107, 924, 10.1016/j.future.2017.10.031
Li, 2021, Toward proactive human–robot collaborative assembly: A multimodal transfer-learning-enabled action prediction approach, IEEE Trans. Ind. Electron., 69, 8579, 10.1109/TIE.2021.3105977
Gao, 2020, A survey on deep learning for multimodal data fusion, Neural Comput., 32, 829, 10.1162/neco_a_01273
Abdel-Basset, 2020, ST-deephar: Deep learning model for human activity recognition in IoHT applications, IEEE Internet Things J., 8, 4969, 10.1109/JIOT.2020.3033430
Lin, 2022, Adaptive multi-modal fusion framework for activity monitoring of people with mobility disability, IEEE J. Biomed. Health Inf., 26, 4314, 10.1109/JBHI.2022.3168004
Malawski, 2019, Improving multimodal action representation with joint motion history context, J. Vis. Commun. Image Represent., 61, 198, 10.1016/j.jvcir.2019.03.026
Franco, 2020, A multimodal approach for human activity recognition based on skeleton and RGB data, Pattern Recognit. Lett., 131, 293, 10.1016/j.patrec.2020.01.010
ul Haq, 2020, Opportunistic sensing for inferring in-the-wild human contexts based on activity pattern recognition using smart computing, Future Gener. Comput. Syst., 106, 374, 10.1016/j.future.2020.01.003
Ziaeefard, 2015, Semantic human activity recognition: A literature review, Pattern Recognit., 48, 2329, 10.1016/j.patcog.2015.03.006
Park, 2023, Multicnn-filterlstm: Resource-efficient sensor-based human activity recognition in IoT applications, Future Gener. Comput. Syst., 139, 196, 10.1016/j.future.2022.09.024
H. Ma, W. Li, X. Zhang, S. Gao, S. Lu, AttnSense: Multi-level Attention Mechanism For Multimodal Human Activity Recognition, in: IJCAI, 2019, pp. 3109–3115.
Singh, 2020, Deep convlstm with self-attention for human activity decoding using wearable sensors, IEEE Sens. J., 21, 8575, 10.1109/JSEN.2020.3045135
Gao, 2021, Danhar: Dual attention network for multimodal human activity recognition using wearable sensors, Appl. Soft Comput., 111, 10.1016/j.asoc.2021.107728
Tien, 2021, Vision-based human activity recognition for reducing building energy demand, Build. Serv. Eng. Res. Technol., 42, 691, 10.1177/01436244211026120
Abebe Tadesse, 2022
Mekruksavanich, 2018, Smartwatch-based sitting detection with human activity recognition for office workers syndrome, 160
Cha, 2018, Towards a well-planned, activity-based work environment: Automated recognition of office activities using accelerometers, Build. Environ., 144, 86, 10.1016/j.buildenv.2018.07.051
Zhong, 2020, Multilocation human activity recognition via MIMO-OFDM-based wireless networks: An IoT-inspired device-free sensing approach, IEEE Internet Things J., 8, 15148, 10.1109/JIOT.2020.3038899
Zou, 2019, Multiple kernel semi-representation learning with its application to device-free human activity recognition, IEEE Internet Things J., 6, 7670, 10.1109/JIOT.2019.2901927
Alruban, 2022
Han, 2022, Human activity recognition using wearable sensors by heterogeneous convolutional neural networks, Expert Syst. Appl., 198, 10.1016/j.eswa.2022.116764
Ran, 2021, A portable sitting posture monitoring system based on a pressure sensor array and machine learning, Sensors Actuators A, 331, 10.1016/j.sna.2021.112900
Jeong, 2020, Developing and evaluating a mixed sensor smart chair system for real-time posture classification: Combining pressure and distance sensors, IEEE J. Biomed. Health Inf., 25, 1805, 10.1109/JBHI.2020.3030096
Ma, 2017, Activity level assessment using a smart cushion for people with a sedentary lifestyle, Sensors, 17, 2269, 10.3390/s17102269
Naser, 2022, Privacy-preserving, thermal vision with human in the loop fall detection alert system, IEEE Trans. Hum.-Mach. Syst., 1
Zhang, 2022, A privacy-preserving and unobtrusive sitting posture recognition system via pressure array sensor and infrared array sensor for office workers, Adv. Eng. Inform., 53, 10.1016/j.aei.2022.101690
Elmadany, 2018, Multimodal learning for human action recognition via bimodal/multimodal hybrid centroid canonical correlation analysis, IEEE Trans. Multimed., 21, 1317, 10.1109/TMM.2018.2875510
Chen, 2015, A real-time human action recognition system using depth and inertial sensor fusion, IEEE Sens. J., 16, 773, 10.1109/JSEN.2015.2487358
Arevalo, 2017
Changzhou Rouxi Electronic Technology Co., Ltd., 2023
SHARP, 2023
Melexis, 2023
STMicroelectronics, 2023
Veit, 2016, Residual networks behave like ensembles of relatively shallow networks, Adv. Neural Inf. Process. Syst., 29
Guo, 2021
Sun, 2023, Human action recognition from various data modalities: A review, IEEE Trans. Pattern Anal. Mach. Intell., 45, 3200
Arevalo, 2017
Tao, 2020, Multi-modal recognition of worker activity for human-centered intelligent manufacturing, Eng. Appl. Artif. Intell., 95, 10.1016/j.engappai.2020.103868
Gadzicki, 2020, Early vs late fusion in multimodal convolutional neural networks, 1
D. Tran, H. Wang, L. Torresani, M. Feiszli, Video classification with channel-separated convolutional networks, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 5552–5561.
