[1]YI Chunzhi,JIA Yicheng,JIANG Feng,et al.Human motion intention recognition method based on inertial measurement unit: current situation, and challenges[J].CAAI Transactions on Intelligent Systems,2025,20(4):763-775.[doi:10.11992/tis.202407012]
Copy

Human motion intention recognition method based on inertial measurement unit: current situation, and challenges

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 4 Page number: 763-775 Column: 综述 Public date: 2025-08-05
Title:
Human motion intention recognition method based on inertial measurement unit: current situation, and challenges
Author(s):
YI Chunzhi1;  JIA Yicheng1;  JIANG Feng2;  WANG Xiulai3
1. Department of Life Science and Medicine, Harbin Institute of Technology, Harbin 150001, China;
2. Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China;
3. General Hospital of Eastern Theater, Nanjing 210018, China
Keywords:
human activity recognition; deep learning; inertial sensors; ubiquitous computing; data privacy; model deployment; transfer learning; data quality
CLC:
TP181; Q81
DOI:
10.11992/tis.202407012
Abstract:
Human activity recognition (HAR) utilizes wearable computing, machine learning, and other technologies to identify and understand human behaviors, which remarkably enhances current human living standards in areas such as behavior tracking, health monitoring, and human–computer interaction. Inertial sensors have increasingly become the mainstream devices in wearable computing due to their highly compact size, low cost, and stable signal characteristics. Consequently, much research in the HAR field employs inertial signals as data sources and applies deep learning algorithms to address challenges in data utilization, privacy protection, and model deployment. This paper systematically introduces deep learning approaches for HAR and categorizes and summarizes existing work, and comprehensively analyzes current advancements, development trends, and key challenges. First, this paper introduces mainstream wearable devices used in HAR research and their data modalities, and details the characteristics of each modality. Second, this paper compiles commonly used HAR datasets in recent years and summarizes the data modalities, sensor placements, movement types, and citation frequencies within each dataset. Furthermore, the paper reviews the progress of several deep learning methods commonly applied in the HAR field from the perspectives of algorithm characteristics and application scenarios. Finally, this paper discusses the challenges currently confronting deep learning in the HAR field and the potential solutions.
References:
[1] BROPHY E, VEIGA J J D, WANG Zhengwei, et al. An interpretable machine vision approach to human activity recognition using photoplethysmograph sensor data[EB/OL]. (2018-12-03)[2024-07-10]. https://arxiv.org/abs/1812.00668v1.
[2] WANG Jindong, CHEN Yiqiang, HAO Shuji, et al. Deep learning for sensor-based activity recognition: a survey[J]. Pattern recognition letters, 2019, 119: 3-11.
[3] KWAPISZ J R, WEISS G M, MOORE S A. Activity recognition using cell phone accelerometers[J]. ACM SIGKDD explorations newsletter, 2011, 12(2): 74-82.
[4] ANGUITA D, GHIO A, ONETO L, et al. A public domain dataset for human activity recognition using smartphones[C]//ESANN 2013 Proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges: ESANN, 2013: 437-442.
[5] VAN KASTEREN T, NOULAS A, ENGLEBIENNE G, et al. Accurate activity recognition in a home setting[C]//Proceedings of the 10th International Conference on Ubiquitous Computing. Seoul: ACM, 2008: 21-24.
[6] SHOAIB M, BOSCH S, INCEL O D, et al. Fusion of smartphone motion sensors for physical activity recognition[J]. Sensors, 2014, 14(6): 10146-10176.
[7] CHEN Chen, JAFARI R, KEHTARNAVAZ N. UTD-MHAD: a multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor[C]//2015 IEEE International Conference on Image Processing. Quebec City: IEEE, 2015: 168-172.
[8] STISEN A, BLUNCK H, BHATTACHARYA S, et al. Smart devices are different: assessing and mitigating mobile sensing heterogeneities for activity recognition[C]//Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems. Seoul: ACM, 2015: 127-140.
[9] ALTUN K, BARSHAN B, TUN?EL O. Comparative study on classifying human activities with miniature inertial and magnetic sensors[J]. Pattern recognition, 2010, 43(10): 3605-3620.
[10] BANOS O, GARCIA R, HOLGADO-TERRIZA J A, et al.MHealthDroid: a novel framework for agile development of mobile health applications[M]//Ambient Assisted Living and Daily Activities. Cham: Springer International Publishing, 2014: 91-98.
[11] BANOS O, VILLALONGA C, GARCIA R, et al. Design, implementation and validation of a novel open framework for agile development of mobile health applications[J]. Biomedical engineering online, 2015, 14(Suppl 2): S6.
[12] CHAVARRIAGA R, SAGHA H, CALATRONI A, et al. The opportunity challenge: a benchmark database for on-body sensor-based activity recognition[J]. Pattern recognition letters, 2013, 34(15): 2033-2042.
[13] REISS A, STRICKER D. Introducing a new benchmarked dataset for activity monitoring[C]//2012 16th International Symposium on Wearable Computers. Newcastle: IEEE, 2012: 108-109.
[14] SHOAIB M, SCHOLTEN H, HAVINGA P J M. Towards physical activity recognition using smartphone sensors[C]//2013 IEEE 10th International Conference on Ubiquitous Intelligence and Computing and 2013 IEEE 10th International Conference on Autonomic and Trusted Computing. Vietri sul Mare: IEEE, 2013: 80-87.
[15] MICUCCI D, MOBILIO M, NAPOLETANO P. UniMiB SHAR: a dataset for human activity recognition using acceleration data from smartphones[J]. Applied sciences, 2017, 7(10): 1101.
[16] ZHANG Mi, SAWCHUK A A. USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors[C]//Proceedings of the 2012 ACM Conference on Ubiquitous Computing. Pittsburgh: ACM, 2012: 1036-1043.
[17] VAIZMAN Y, ELLIS K, LANCKRIET G. Recognizing detailed human context in the wild from smartphones and smartwatches[J]. IEEE pervasive computing, 2017, 16(4): 62-74.
[18] KAWAGUCHI N, OGAWA N, IWASAKI Y, et al. HASC Challenge: gathering large scale human activity corpus for the real-world activity understandings[C]//Proceedings of the 2nd Augmented Human International Conference. Tokyo: ACM, 2011: 271-275.
[19] WEISS G M, LOCKHART J W, PULICKAL T T, et al. Actitracker: a smartphone-based activity recognition system for improving health and well-being[C]//2016 IEEE International Conference on Data Science and Advanced Analytics. Montreal: IEEE, 2016: 682-688.
[20] BRUNO B, MASTROGIOVANNI F, SGORBISSA A. A public domain dataset for ADL recognition using wrist-placed accelerometers[C]//The 23rd IEEE International Symposium on Robot and Human Interactive Communication. Edinburgh: IEEE, 2014: 738-743.
[21] ZHANG Zhilin, PI Zhouyue, LIU Benyuan. TROIKA: a general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise[J]. IEEE transactions on biomedical engineering, 2015, 62(2): 522-531.
[22] 邓淼磊, 高振东, 李磊, 等. 基于深度学习的人体行为识别综述[J]. 计算机工程与应用, 2022, 58(13): 14-26.
DENG Miaolei, GAO Zhendong, LI Lei, et al. Overview of human behavior recognition based on deep learning[J]. Computer engineering and applications, 2022, 58(13): 14-26.
[23] KYRITSIS K, TATLI C L, DIOU C, et al. Automated analysis of in meal eating behavior using a commercial wristband IMU sensor[C]//2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Jeju: IEEE, 2017: 2843-2846.
[24] CHAUHAN J, HU Yining, SENEVIRATNE S, et al. BreathPrint: breathing acoustics-based user authentication[C]//Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services. New York: ACM, 2017: 278-291.
[25] ZAPPI P, LOMBRISER C, STIEFMEIER T, et al. Activity recognition from on-body sensors: accuracy-power trade-off by dynamic sensor selection[M]//Wireless Sensor Networks. Berlin: Springer Berlin Heidelberg, 2008: 17-33.
[26] B?CHLIN M, PLOTNIK M, ROGGEN D, et al. Wearable assistant for Parkinson’s disease patients with the freezing of gait symptom[J]. IEEE transactions on information technology in biomedicine, 2010, 14(2): 436-446.
[27] CHEN Y, KEOGH E, HU B, et al. The UCR time series classification archive[EB/OL]. (2022-02-10)[2024-07-10]. http://www.cs.ucr.edu/~eamonn/time_series_data/.
[28] BAGNALL A, DAU H A, LINES J, et al. The UEA multivariate time series classification archive[EB/OL]. (2018-11-31)[2024-07-10]. https://arxiv.org/abs/1811.00075v1.
[29] GJORESKI H, CILIBERTO M, WANG Lin, et al. The University of Sussex-Huawei locomotion and transportation dataset for multimodal analytics with mobile devices[J]. IEEE access, 2018, 6: 42592-42604.
[30] GIORGI G, MARTINELLI F, SARACINO A, et al. Try walking in my shoes, if you can: accurate gait recognition through deep learning[M]//Computer Safety, Reliability, and Security. Cham: Springer International Publishing, 2017: 384-395.
[31] ISMAIL F H, FORESTIER G, WEBER J, et al. Data augmentation using synthetic data for time series classification with deep residual networks[C]//Proceedings of the International Workshop on Advanced Analytics and Learning on Temporal Data. Dublin: ECML PKDD, 2018.
[32] WANG Jiwei, CHEN Yiqiang, GU Yang, et al. SensoryGANs: an effective generative adversarial framework for sensor-based human activity recognition[C]//2018 International Joint Conference on Neural Networks. Rio de Janeiro: IEEE, 2018: 1-8.
[33] RAMPONI G, PROTOPAPAS P, BRAMBILLA M, et al. T-CGAN: conditional generative adversarial network for data augmentation in noisy time series with irregular sampling[EB/OL]. (2018-11-20)[2024-07-10]. https://arxiv.org/abs/1811.08295v2.
[34] ALZANTOT M, CHAKRABORTY S, SRIVASTAVA M. SenseGen: a deep learning architecture for synthetic sensor data generation[C]//2017 IEEE International Conference on Pervasive Computing and Communications Workshops. Kona: IEEE, 2017: 188-193.
[35] SAHA S S, SANDHA S S, SRIVASTAVA M. Deep convolutional bidirectional LSTM for complex activity recognition with missing data[M]//Human Activity Recognition Challenge. Singapore: Springer Singapore, 2020: 39-53.
[36] WU Donghui, XU Jing, CHEN Jibin, et al. Human activity recognition algorithm based on CNN-LSTM with attention mechanism[J]. Science technology and engineering, 2023, 23(2): 681-689.
[37] KWON H, TONG C, HARESAMUDRAM H, et al. IMUTube: automatic extraction of virtual on-body accelerometry from video for human activity recognition[EB/OL]. (2020-06-29)[2024-07-10]. https://arxiv.org/abs/2006.05675v2.
[38] ALHARBI F, OUARBYA L, WARD J A. Synthetic sensor data for human activity recognition[C]//2020 International Joint Conference on Neural Networks. Glasgow: IEEE, 2020: 1-9.
[39] CHAN Manghong, NOOR M H M. A unified generative model using generative adversarial network for activity recognition[J]. Journal of ambient intelligence and humanized computing, 2021, 12(7): 8119-8128.
[40] LI Xiang, LUO Jinqi, YOUNES R. ActivityGAN: generative adversarial networks for data augmentation in sensor-based human activity recognition[C]//Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers. Virtual Event: ACM, 2020: 249-254.
[41] 许芬, 史鹏飞. 人体动作与行为识别研究综述[J]. 工业控制计算机, 2023, 36(9): 58-59.
XU Fen, SHI Pengfei. Research on review on human action recognition[J]. Industrial control computer, 2023, 36(9): 58-59.
[42] SIIRTOLA P, R?NING J. Incremental learning to personalize human activity recognition models: the importance of human AI collaboration[J]. Sensors, 2019, 19(23): 5151.
[43] QIAN Hangwei, PAN S J, MIAO Chunyan. Latent independent excitation for generalizable sensor-based cross-person activity recognition[C]//Proceedings of the AAAI Conference on Artificial Intelligence. Virtual: AAAI, 2021: 11921-11929.
[44] ARJOVSKY M, BOTTOU L, GULRAJANI I, et al. Invariant risk minimization[EB/OL]. (2019-07-05)[2024-07-10]. https://arxiv.org/abs/1907.02893v3.
[45] ZENG Ming, YU Tong, WANG Xiao, et al. Semi-supervised convolutional neural networks for human activity recognition[C]//2017 IEEE International Conference on Big Data. Boston: IEEE, 2017: 522-529.
[46] BALABKA D. Semi-supervised learning for human activity recognition using adversarial autoencoders[C]//Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers. London: ACM, 2019: 685-688.
[47] CHEN Kaixuan, YAO Lina, ZHANG Dalin, et al. Distributionally robust semi-supervised learning for people-centric sensing[J]. Proceedings of the AAAI conference on artificial intelligence, 2019, 33(1): 3321-3328.
[48] GUDUR G K, SUNDARAMOORTHY P, UMAASHANKAR V. ActiveHARNet: towards on-device deep Bayesian active learning for human activity recognition[C]//The 3rd International Workshop on Deep Learning for Mobile Systems and Applications. Seoul: ACM, 2019: 7-12.
[49] BETTINI C, CIVITARESE G, PRESOTTO R. Personalized semi-supervised federated learning for human activity recognition[EB/OL]. (2021-04-05)[2024-07-10]. https://arxiv.org/abs/2104.08094v2.
[50] 马偲臆, 刘晓薇, 谢雪琴, 等. 迁移学习在生物医学领域的应用[J]. 生物医学工程学进展, 2023, 44(4): 347-356.
MA Caiyi, LIU Xiaowei, XIE Xueqin, et al. The application of transfer learning in biomedicine[J]. Progress in biomedical engineering, 2023, 44(4): 347-356.
[51] MOHAMMED S, TASHEV I. Unsupervised deep representation learning to remove motion artifacts in free-mode body sensor networks[C]//2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks. Eindhoven: IEEE, 2017: 183-188.
[52] CAO Wei, WANG Dong, LI Jian, et al. BRITS: bidirectional recurrent imputation for time series[J]. Advances in neural information processing systems, 2018, 31: 6775-6785.
[53] LUO Y, CAI X, ZHANG Y, et al. Multivariate time series imputation with generative adversarial networks[J]. Advances in neural information processing systems, 2018, 31: 1596-1607.
[54] SAEED A, OZCELEBI T, LUKKIEN J. Synthesizing and reconstructing missing sensory modalities in behavioral context recognition[J]. Sensors, 2018, 18(9): 2967.
[55] GAO Yang, ZHANG Ning, WANG Honghao, et al. iHear food: eating detection using commodity bluetooth headsets[C]//2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies. Washington: IEEE, 2016: 163-172.
[56] ZHOU Yipin, WANG Zhaowen, FANG Chen, et al. Visual to sound: generating natural sound for videos in the wild[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 3550-3558.
[57] HOSSAIN M Z, SOHEL F, SHIRATUDDIN M F, et al. A comprehensive survey of deep learning for image captioning[J]. ACM computing surveys, 2019, 51(6): 1-36.
[58] 梁绪, 李文新, 张航宁. 人体行为识别方法研究综述[J]. 计算机应用研究, 2022, 39(3): 651-660.
LIANG Xu, LI Wenxin, ZHANG Hangning. Review of research on human action recognition methods[J]. Application research of computers, 2022, 39(3): 651-660.
[59] MALEKZADEH M, CLEGG R G, CAVALLARO A, et al. Protecting sensory data against sensitive inferences[C]//Proceedings of the 1st Workshop on Privacy by Design in Distributed Systems. Porto Portugal: ACM, 2018: 21-26.
[60] MALEKZADEH M, CLEGG R G, CAVALLARO A, et al. Mobile sensor data anonymization[C]//Proceedings of the International Conference on Internet of Things Design and Implementation. Montrea: ACM, 2019: 49-58.
[61] MALEKZADEH M, CLEGG R G, HADDADI H. Replacement AutoEncoder: a privacy-preserving algorithm for sensory data analysis[C]//2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation. Orlando: IEEE, 2018: 165-176.
[62] VAVOULAS G, CHATZAKI C, MALLIOTAKIS T, et al. The MobiAct dataset: recognition of activities of daily living using smartphones[C]//Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and E-Health. Rome: SCITEPRESS-Science and and Technology Publications, 2016: 143-151.
[63] 梁朝晖, 朱笑笑, 曹其新, 等. 基于联邦学习的下肢康复评估算法与实现[J]. 计算机工程与设计, 2023, 44(8): 2548-2554.
LIANG Zhaohui, ZHU Xiaoxiao, CAO Xinqi, et al. Algorithm and implementation of lower limb rehabilitation evaluation based on federated learning[J]. Computer engineering and design, 2023, 44(8): 2548-2554.
[64] XIAO Zhiwen, XU Xin, XING Huanlai, et al. A federated learning system with enhanced feature extraction for human activity recognition[J]. Knowledge-based systems, 2021, 229: 107338.
[65] TU Linlin, OUYANG Xiaomin, ZHOU Jiayu, et al. FedDL: federated learning via dynamic layer sharing for human activity recognition[C]//Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems. Coimbra Portugal: ACM, 2021: 15-28.
[66] GUDUR G K, PEREPU S K. Resource-constrained federated learning with heterogeneous labels and models for human activity recognition[M]//Deep Learning for Human Activity Recognition. Singapore: Springer Singapore, 2021: 57-69.
[67] 薛开平, 范茂, 王峰, 等. 区块链隐私众包中的数据验证与可控匿名方案[J]. 电子与信息学报, 2024, 46(2): 748-756.
XUE Kaiping, FAN Mao, WANG Feng, et al. Privacy crowdsourcing on blockchain with data verification and controllable anonymity[J]. Journal of electronics & information technology, 2024, 46(2): 748-756.
[68] LYU Mingqi, XU Wei, CHEN Tieming. A hybrid deep convolutional and recurrent neural network for complex activity recognition using multimodal sensors[J]. Neurocomputing, 2019, 362: 33-40.
[69] 赵海, 陈佳伟, 施瀚, 等. 一种应用于人体活动识别的迁移学习算法[J]. 东北大学学报(自然科学版), 2022, 43(6): 776-782.
ZHAO Hai, CHEN Jiawei, SHI Han, et al. A transfer learning algorithm applied to human activity recognition[J]. Journal of northeastern university (natural science edition), 2022, 43(6): 776-782.
[70] SOLEIMANI E, NAZERFARD E. Cross-subject transfer learning in human activity recognition systems using generative adversarial networks[J]. Neurocomputing, 2021, 426: 26-34.
[71] ABEDIN A, REZATOFIGHI H, RANASINGHE D C. Guided-GAN: adversarial representation learning for activity recognition with wearables[EB/OL]. (2021-10-12)[2024-07-10]. https://arxiv.org/abs/2110.05732v1.
[72] SANABRIA A R, ZAMBONELLI F, DOBSON S, et al. ContrasGAN: unsupervised domain adaptation in human activity recognition via adversarial and contrastive learning[J]. Pervasive and mobile computing, 2021, 78: 101477.
[73] VEPAKOMMA P, DE D, DAS S K, et al. A-wristocracy: deep learning on wrist-worn sensing for recognition of user complex activities[C]//2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks. Cambridge: IEEE, 2015: 1-6.
[74] KYRITSIS K, DIOU C, DELOPOULOS A. Modeling wrist micromovements to measure in-meal eating behavior from inertial sensor data[J]. IEEE journal of biomedical and health informatics, 2019, 23(6): 2325-2334.
[75] LIU Cihang, ZHANG Lan, LIU Zongqian, et al. Lasagna: towards deep hierarchical understanding and searching over mobile sensing data[C]//Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking. New York: ACM, 2016: 334-347.
[76] PENG Liangying, CHEN Ling, YE Zhenan, et al. AROMA: a deep multi-task learning based simple and complex human activity recognition method using wearable sensors[C]// Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. New York: ACM, 2018, 2: 74.
[77] GRZESZICK R, LENK J M, RUEDA F M, et al. Deep neural network based human activity recognition for the order picking process[C]//Proceedings of the 4th International Workshop on Sensor-based Activity Recognition and Interaction. Rostock: ACM, 2017: 1-6.
[78] MATSUI S, INOUE N, AKAGI Y, et al. User adaptation of convolutional neural network for human activity recognition[C]//2017 25th European Signal Processing Conference. Kos: IEEE, 2017: 753-757.
[79] LANE N D, BHATTACHARYA S, GEORGIEV P, et al. DeepX: a software accelerator for low-power deep learning inference on mobile devices[C]//2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks. Vienna: IEEE, 2016: 1-12.
[80] LANE N D, GEORGIEV P, QENDRO L. DeepEar: robust smartphone audio sensing in unconstrained acoustic environments using deep learning[C]//Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Osaka: ACM, 2015: 283-294.
[81] CAO Qingqing, BALASUBRAMANIAN N, BALASUBRAMANIAN A. MobiRNN: efficient recurrent neural network execution on mobile GPU[C]//Proceedings of the 1st International Workshop on Deep Learning for Mobile Systems and Applications. New York: ACM, 2017: 1-6.
[82] YAO Shuochao, HU Shaohan, ZHAO Yiran, et al. DeepSense: a unified deep learning framework for time-series mobile sensing data processing[C]//Proceedings of the 26th International Conference on World Wide Web. Perth: International World Wide Web Conferences Steering Committee, 2017: 351-360.
[83] BHATTACHARYA S, LANE N D. Sparsification and separation of deep learning layers for constrained resource inference on wearables[C]//Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM. Stanford: ACM, 2016: 176-189.
[84] EDEL M, K?PPE E. Binarized-BLSTM-RNN based human activity recognition[C]//2016 International Conference on Indoor Positioning and Indoor Navigation. Alcala de Henares: IEEE, 2016: 1-7.
[85] 赵冬冬, 赖亮, 陈朋, 等. 基于昇腾处理器的边端人体动作识别算法设计与实现[J]. 光电工程, 2024, 51(6): 66-80.
ZHAO Dongdong, LAI Liang, CHEN Peng, et al. Design and implementation of edge-based human action recognition algorithm based on ascend processor[J]. Opto-electronic engineering, 2024, 51(6): 66-80.
[86] BHAT G, TUNCEL Y, AN Sizhe, et al. An ultra-low energy human activity recognition accelerator for wearable health applications[J]. ACM transactions on embedded computing systems, 2019, 18(5s): 1-22.
[87] ISLAM B, NIRJON S. Zygarde: time-sensitive on-device deep inference and adaptation on intermittently-powered systems[EB/OL]. (2019-05-05)[2024-07-10]. https://arxiv.org/abs/1905.03854v2.
[88] XIA S, NIE Jingping, JIANG Xiaofan. CSafe: an intelligent audio wearable platform for improving construction worker safety in urban environments[C]//Proceedings of the 20th International Conference on Information Processing in Sensor Networks. Nashville: ACM, 2021: 207-221.
[89] XIA S, DE GODOY PEIXOTO D, ISLAM B, et al. Improving pedestrian safety in cities using intelligent wearable systems[J]. IEEE internet of things journal, 2019, 6(5): 7497-7514.
[90] DE GODOY D, ISLAM B, XIA S, et al. PAWS: a wearable acoustic system for pedestrian safety[C]//2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation. Orlando: IEEE, 2018: 237-248.
[91] NIE Jingping, HU Yigong, WANG Y, et al. SPIDERS: low-cost wireless glasses for continuous in situ bio-signal acquisition and emotion recognition[C]//2020 IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation. Sydney: IEEE, 2020: 27-39.
[92] NIE Jingping, LIU Yanchen, HU Yigong, et al. SPIDERS +: a light-weight, wireless, and low-cost glasses-based wearable platform for emotion sensing and bio-signal acquisition[J]. Pervasive and mobile computing, 2021, 75: 101424.
[93] HU Yigong, NIE Jingping, WANG Y, et al. Demo abstract: wireless glasses for non-contact facial expression monitoring[C]//2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks. Sydney: IEEE, 2020: 367-368.
[94] 万涛, 李婉琦, 葛晶晶. 基于区块链的边缘移动群智感知声誉更新方案[J]. 计算机应用研究, 2023, 40(6): 1636-1640.
WAN Tao, LI Wanqi, GE Jingjing. Reputation update scheme for blockchain-based edge mobile crowdsensing[J]. Application research of computers, 2023, 40(6): 1636-1640.
[95] XIA S, DE GODOY D, ISLAM B, et al. A smartphone-based system for improving pedestrian safety[C]//2018 IEEE Vehicular Networking Conference. Taipei: IEEE, 2018: 1-2.
[96] LANE N D, GEORGIEV P. Can deep learning revolutionize mobile sensing?[C]//Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications. Santa Fe New Mexico: ACM, 2015: 117-122.
[97] ZHANG Dalin, YAO Lina, ZHANG Xiang, et al. Cascade and parallel convolutional recurrent neural networks on EEG-based intention recognition for brain computer interface[J]. Proceedings of the AAAI conference on artificial intelligence, 2018, 32(1): 11496.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems