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The combination of increased life expectancy and falling birth rates is resulting in an aging population.
M. Gabel, R. Gilad-Bachrach, E. Renshaw, and A. Schuster, “Full body gait analysis with kinect,” in EMBC . IEEE, 2012, pp. 1964–1967
1967
Earlier work this paper cites.
A. F. Bobick and J. W. Davis, “The recognition of human movement using temporal templates,” TPAMI , vol. 23, no. 3, pp. 257–267, 2001
2001
Earlier work this paper cites.
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “Smote: synthetic minority over-sampling technique,” JAIR , vol. 16, pp. 321–357, 2002
2002
Earlier work this paper cites.
J. Chen, A. H. Kam, J. Zhang, N. Liu, and L. Shue, “Bathroom activity monitoring based on sound,” in PERVASIVE . Springer, 2005, pp. 47–61
2005
Earlier work this paper cites.
A. J. Eronen, V. T. Peltonen, J. T. Tuomi, A. P. Klapuri, S. Fagerlund, T. Sorsa, G. Lorho, and J. Huopaniemi, “Audio-based context recognition,” ICASSP , vol. 14, no. 1, pp. 321–329, 2005
2005
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in CVPR . Ieee, 2009, pp. 248–255
2009
Earlier work this paper cites.
R. Poppe, “A survey on vision-based human action recognition,” Image and Vision Computing , vol. 28, no. 6, pp. 976–990, 2010
2010
Earlier work this paper cites.
J. K. Aggarwal and M. S. Ryoo, “Human activity analysis: A review,” CSUR , vol. 43, no. 3, pp. 1–43, 2011
2011
Earlier work this paper cites.
A. Fathi, A. Farhadi, and J. M. Rehg, “Understanding egocentric activities,” in ICCV . IEEE, 2011, pp. 407–414
2011
Earlier work this paper cites.
O. D. Lara and M. A. Labrador, “A survey on human activity recognition using wearable sensors,” IEEE communications surveys & tutorials , vol. 15, no. 3, pp. 1192–1209, 2012
2012
Earlier work this paper cites.
K. Yatani and K. N. Truong, “Bodyscope: a wearable acoustic sensor for activity recognition,” in UbiComp , 2012, pp. 341–350
2012
Earlier work this paper cites.
O. D. Incel, M. Kose, and C. Ersoy, “A review and taxonomy of activity recognition on mobile phones,” BioNanoScience , vol. 3, no. 2, pp. 145–171, 2013
2013
Earlier work this paper cites.
F. Ofli, R. Chaudhry, G. Kurillo, R. Vidal, and R. Bajcsy, “Berkeley mhad: A comprehensive multimodal human action database,” in WACV . IEEE, 2013, pp. 53–60
2013
Earlier work this paper cites.
B. Delachaux, J. Rebetez, A. Perez-Uribe, and H. F. Satizábal Mejia, “Indoor activity recognition by combining one-vs.-all neural network classifiers exploiting wearable and depth sensors,” in IWANN . Springer, 2013, pp. 216–223
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
R. Rawassizadeh, B. A. Price, and M. Petre, “Wearables: Has the age of smartwatches finally arrived?” Communications of the ACM , vol. 58, no. 1, pp. 45–47, 2014
2014
Earlier work this paper cites.
A. Bulling, U. Blanke, and B. Schiele, “A tutorial on human activity recognition using body-worn inertial sensors,” CSUR , vol. 46, no. 3, pp. 1–33, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
S. Narayan, M. S. Kankanhalli, and K. R. Ramakrishnan, “Action and interaction recognition in first-person videos,” in CVPR Workshops , 2014, pp. 512–518
2014
Earlier work this paper cites.
C. Chen, R. Jafari, and N. Kehtarnavaz, “Improving human action recognition using fusion of depth camera and inertial sensors,” THMS , vol. 45, no. 1, pp. 51–61, 2014
2014
Earlier work this paper cites.
K. Liu, C. Chen, R. Jafari, and N. Kehtarnavaz, “Fusion of inertial and depth sensor data for robust hand gesture recognition,” IEEE Sensors Journal , vol. 14, no. 6, pp. 1898–1903, 2014
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” vol. 27, 2014
2014
Earlier work this paper cites.
K. Van Laerhoven, M. Borazio, and J. H. Burdinski, “Wear is your mobile? investigating phone carrying and use habits with a wearable device,” Frontiers in ICT , vol. 2, p. 10, 2015
2015
Earlier work this paper cites.
I. Bornkessel-Schlesewsky, M. Schlesewsky, S. L. Small, and J. P. Rauschecker, “Neurobiological roots of language in primate audition: common computational properties,” Trends in cognitive sciences , vol. 19, no. 3, pp. 142–150, 2015
2015
Earlier work this paper cites.
Y. Li, Z. Ye, and J. M. Rehg, “Delving into egocentric actions,” in CVPR , 2015, pp. 287–295
2015
Earlier work this paper cites.
Y. Du, W. Wang, and L. Wang, “Hierarchical recurrent neural network for skeleton based action recognition,” in CVPR , 2015, pp. 1110–1118
2015
Earlier work this paper cites.
N. D. Lane, P. Georgiev, and L. Qendro, “Deepear: robust smartphone audio sensing in unconstrained acoustic environments using deep learning,” in UbiComp , 2015, pp. 283–294
2015
Earlier work this paper cites.
Y. Chen and Y. Xue, “A deep learning approach to human activity recognition based on single accelerometer,” in SMC . IEEE, 2015, pp. 1488–1492
2015
Earlier work this paper cites.
W. Jiang and Z. Yin, “Human activity recognition using wearable sensors by deep convolutional neural networks,” in ACM MM , 2015, pp. 1307–1310
2015
Earlier work this paper cites.
S. Venugopalan, M. Rohrbach, J. Donahue, R. Mooney, T. Darrell, and K. Saenko, “Sequence to sequence-video to text,” in ICCV , 2015, pp. 4534–4542
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
C. Chen, R. Jafari, and N. Kehtarnavaz, “Utd-mhad: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor,” in ICIP . IEEE, 2015, pp. 168–172
2015
Earlier work this paper cites.
J. Yang, M. N. Nguyen, P. P. San, X. Li, and S. Krishnaswamy, “Deep convolutional neural networks on multichannel time series for human activity recognition.” in IJCAI , vol. 15. Buenos Aires, Argentina, 2015, pp. 3995–4001
2015
Earlier work this paper cites.
G. Chetty and M. Yamin, “Intelligent human activity recognition scheme for ehealth applications,” Malaysian Journal of Computer Science , vol. 28, no. 1, pp. 59–69, 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
I. Rodomagoulakis, N. Kardaris, V. Pitsikalis, E. Mavroudi, A. Katsamanis, A. Tsiami, and P. Maragos, “Multimodal human action recognition in assistive human-robot interaction,” in ICASSP . IEEE, 2016, pp. 2702–2706
2016
Earlier work this paper cites.
Y. Poleg, A. Ephrat, S. Peleg, and C. Arora, “Compact cnn for indexing egocentric videos,” in WACV . IEEE, 2016, pp. 1–9
2016
Earlier work this paper cites.
J. Wang, X. Zhang, Q. Gao, H. Yue, and H. Wang, “Device-free wireless localization and activity recognition: A deep learning approach,” TVT , vol. 66, no. 7, pp. 6258–6267, 2016
2016
Earlier work this paper cites.
F. J. Ordóñez and D. Roggen, “Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition,” Sensors , vol. 16, no. 1, p. 115, 2016
2016
Earlier work this paper cites.
D. Ravì, C. Wong, F. Deligianni, M. Berthelot, J. Andreu-Perez, B. Lo, and G.-Z. Yang, “Deep learning for health informatics,” JBHI , vol. 21, no. 1, pp. 4–21, 2016
2016
Earlier work this paper cites.
T. Von Marcard, G. Pons-Moll, and B. Rosenhahn, “Human pose estimation from video and imus,” TPAMI , vol. 38, no. 8, pp. 1533–1547, 2016
2016
Earlier work this paper cites.
H. Guo, L. Chen, L. Peng, and G. Chen, “Wearable sensor based multimodal human activity recognition exploiting the diversity of classifier ensemble,” in UbiComp , 2016, pp. 1112–1123
2016
Earlier work this paper cites.
Y. Wei, Y. Zhu, C. Leung, Y. Song, and Q. Yang, “Instilling social to physical: Co-regularized heterogeneous transfer learning,” in AAAI , vol. 30, no. 1, 2016
2016
Earlier work this paper cites.
J. Hoffman, S. Gupta, and T. Darrell, “Learning with side information through modality hallucination,” in CVPR , 2016, pp. 826–834
2016
Earlier work this paper cites.
Y. Aytar, C. Vondrick, and A. Torralba, “Soundnet: Learning sound representations from unlabeled video,” vol. 29, pp. 892–900, 2016
2016
Earlier work this paper cites.
U. DESA, “World population prospects: Key findings and advance tables,” New York: UN DESA , 2017
2017
Earlier work this paper cites.
A. Kuerbis, A. Mulliken, F. Muench, A. A. Moore, and D. Gardner, “Older adults and mobile technology: Factors that enhance and inhibit utilization in the context of behavioral health,” 2017
2017
Earlier work this paper cites.
M. Á. Á. de la Concepción, L. M. S. Morillo, J. A. Á. García, and L. González-Abril, “Mobile activity recognition and fall detection system for elderly people using ameva algorithm,” PMC , vol. 34, pp. 3–13, 2017
2017
Earlier work this paper cites.
L. Lyu, X. He, Y. W. Law, and M. Palaniswami, “Privacy-preserving collaborative deep learning with application to human activity recognition,” in CIKM , 2017, pp. 1219–1228
2017
Earlier work this paper cites.
A. Ullah, J. Ahmad, K. Muhammad, M. Sajjad, and S. W. Baik, “Action recognition in video sequences using deep bi-directional lstm with cnn features,” IEEE access , vol. 6, pp. 1155–1166, 2017
2017
Earlier work this paper cites.
S. Zhang, X. Liu, and J. Xiao, “On geometric features for skeleton-based action recognition using multilayer lstm networks,” in WACV . IEEE, 2017, pp. 148–157
2017
Earlier work this paper cites.
Q. Gao, J. Wang, X. Ma, X. Feng, and H. Wang, “Csi-based device-free wireless localization and activity recognition using radio image features,” TVT , vol. 66, no. 11, pp. 10 346–10 356, 2017
2017
Earlier work this paper cites.
C. Malleson, A. Gilbert, M. Trumble, J. Collomosse, A. Hilton, and M. Volino, “Real-time full-body motion capture from video and imus,” in 3DV . IEEE, 2017, pp. 449–457
2017
Earlier work this paper cites.
S. Yao, S. Hu, Y. Zhao, A. Zhang, and T. Abdelzaher, “Deepsense: A unified deep learning framework for time-series mobile sensing data processing,” in WWW , 2017, pp. 351–360
2017
Earlier work this paper cites.
C. Zhang, A. Waghmare, P. Kundra, Y. Pu, S. Gilliland, T. Ploetz, T. E. Starner, O. T. Inan, and G. D. Abowd, “Fingersound: Recognizing unistroke thumb gestures using a ring,” IMWUT , vol. 1, no. 3, pp. 1–19, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
T. R. Mauldin, M. E. Canby, V. Metsis, A. H. Ngu, and C. C. Rivera, “Smartfall: A smartwatch-based fall detection system using deep learning,” Sensors , vol. 18, no. 10, p. 3363, 2018
2018
Earlier work this paper cites.
S. Yan, Y. Xiong, and D. Lin, “Spatial temporal graph convolutional networks for skeleton-based action recognition,” in AAAI , 2018
2018
Earlier work this paper cites.
G. Laput, K. Ahuja, M. Goel, and C. Harrison, “Ubicoustics: Plug-and-play acoustic activity recognition,” in UIST , 2018, pp. 213–224
2018
Earlier work this paper cites.
Z. Chen, L. Zhang, C. Jiang, Z. Cao, and W. Cui, “Wifi csi based passive human activity recognition using attention based blstm,” TMC , vol. 18, no. 11, pp. 2714–2724, 2018
2018
Earlier work this paper cites.
X. Wu, Z. Chu, P. Yang, C. Xiang, X. Zheng, and W. Huang, “Tw-see: Human activity recognition through the wall with commodity wi-fi devices,” TVT , vol. 68, no. 1, pp. 306–319, 2018
2018
Earlier work this paper cites.
N. E. D. Elmadany, Y. He, and L. Guan, “Multimodal learning for human action recognition via bimodal/multimodal hybrid centroid canonical correlation analysis,” TMM , vol. 21, no. 5, pp. 1317–1331, 2018
2018
Earlier work this paper cites.
N. Dawar and N. Kehtarnavaz, “A convolutional neural network-based sensor fusion system for monitoring transition movements in healthcare applications,” in ICCA . IEEE, 2018, pp. 482–485
2018
Earlier work this paper cites.
N. Dawar and N. Kehtarnavaz, “Action detection and recognition in continuous action streams by deep learning-based sensing fusion,” IEEE Sensors Journal , vol. 18, no. 23, pp. 9660–9668, 2018
2018
Earlier work this paper cites.
A. Manzi, A. Moschetti, R. Limosani, L. Fiorini, and F. Cavallo, “Enhancing activity recognition of self-localized robot through depth camera and wearable sensors,” IEEE Sensors Journal , vol. 18, no. 22, pp. 9324–9331, 2018
2018
Earlier work this paper cites.
M. Guo, Z. Wang, N. Yang, Z. Li, and T. An, “A multisensor multiclassifier hierarchical fusion model based on entropy weight for human activity recognition using wearable inertial sensors,” THMS , vol. 49, no. 1, pp. 105–111, 2018
2018
Earlier work this paper cites.
S. Yu and L. Qin, “Human activity recognition with smartphone inertial sensors using bidir-lstm networks,” in ICMCCE . IEEE, 2018, pp. 219–224
2018
Earlier work this paper cites.
E. Garcia-Ceja, C. E. Galván-Tejada, and R. Brena, “Multi-view stacking for activity recognition with sound and accelerometer data,” Information Fusion , vol. 40, pp. 45–56, 2018
2018
Earlier work this paper cites.
Y. Huang, M. Kaufmann, E. Aksan, M. J. Black, O. Hilliges, and G. Pons-Moll, “Deep inertial poser: Learning to reconstruct human pose from sparse inertial measurements in real time,” TOG , vol. 37, no. 6, pp. 1–15, 2018
2018
Earlier work this paper cites.
J. Wang, Y. Chen, Y. Gu, Y. Xiao, and H. Pan, “Sensorygans: An effective generative adversarial framework for sensor-based human activity recognition,” in IJCNN . IEEE, 2018, pp. 1–8
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
G. Douzas and F. Bacao, “Effective data generation for imbalanced learning using conditional generative adversarial networks,” Expert Systems with applications , vol. 91, pp. 464–471, 2018
2018
Earlier work this paper cites.
J. Wang, V. W. Zheng, Y. Chen, and M. Huang, “Deep transfer learning for cross-domain activity recognition,” in ICCSE , 2018, pp. 1–8
2018
Earlier work this paper cites.
J. Wang, Y. Chen, L. Hu, X. Peng, and S. Y. Philip, “Stratified transfer learning for cross-domain activity recognition,” in PerCom . IEEE, 2018, pp. 1–10
2018
Earlier work this paper cites.
N. C. Garcia, P. Morerio, and V. Murino, “Modality distillation with multiple stream networks for action recognition,” in ECCV , 2018, pp. 103–118
2018
Earlier work this paper cites.
N. Sengupta, C. B. McNabb, N. Kasabov, and B. R. Russell, “Integrating space, time, and orientation in spiking neural networks: a case study on multimodal brain data modeling,” TNNLS , vol. 29, no. 11, pp. 5249–5263, 2018
2018
Earlier work this paper cites.
A. Galán-Mercant, A. Ortiz, E. Herrera-Viedma, M. T. Tomas, B. Fernandes, and J. A. Moral-Munoz, “Assessing physical activity and functional fitness level using convolutional neural networks,” Knowledge-Based Systems , vol. 185, p. 104939, 2019
2019
Earlier work this paper cites.
Y. Wang, S. Cang, and H. Yu, “A survey on wearable sensor modality centred human activity recognition in health care,” Expert Systems with Applications , vol. 137, pp. 167–190, 2019
2019
Earlier work this paper cites.
C. Feichtenhofer, H. Fan, J. Malik, and K. He, “Slowfast networks for video recognition,” in ICCV , 2019, pp. 6202–6211
2019
Earlier work this paper cites.
J. Lin, C. Gan, and S. Han, “Tsm: Temporal shift module for efficient video understanding,” in ICCV , 2019, pp. 7083–7093
2019
Earlier work this paper cites.
L. Shi, Y. Zhang, J. Cheng, and H. Lu, “Two-stream adaptive graph convolutional networks for skeleton-based action recognition,” in CVPR , 2019, pp. 12 026–12 035
2019
Earlier work this paper cites.
D. Liang and E. Thomaz, “Audio-based activities of daily living (adl) recognition with large-scale acoustic embeddings from online videos,” IMWUT , vol. 3, no. 1, pp. 1–18, 2019
2019
Earlier work this paper cites.
J. Wang, Q. Long, K. Liu, Y. Xie et al. , “Human action recognition on cellphone using compositional bidir-lstm-cnn networks,” in CNCI 2019 . Atlantis Press, 2019, pp. 687–692
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
J. Lu and K.-Y. Tong, “Robust single accelerometer-based activity recognition using modified recurrence plot,” IEEE Sensors Journal , vol. 19, no. 15, pp. 6317–6324, 2019
2019
Earlier work this paper cites.
T. Qiao, J. Zhang, D. Xu, and D. Tao, “Mirrorgan: Learning text-to-image generation by redescription,” in CVPR , 2019, pp. 1505–1514
2019
Earlier work this paper cites.
H. Wei, R. Jafari, and N. Kehtarnavaz, “Fusion of video and inertial sensing for deep learning–based human action recognition,” Sensors , vol. 19, no. 17, p. 3680, 2019
2019
Earlier work this paper cites.
Z. Ahmad and N. Khan, “Human action recognition using deep multilevel multimodal ( M 2 {M}^{2} ) fusion of depth and inertial sensors,” IEEE Sensors Journal , vol. 20, no. 3, pp. 1445–1455, 2019
2019
Earlier work this paper cites.
Q. Kong, Z. Wu, Z. Deng, M. Klinkigt, B. Tong, and T. Murakami, “Mmact: A large-scale dataset for cross modal human action understanding,” in ICCV , 2019, pp. 8658–8667
2019
Earlier work this paper cites.
M. Ullah, H. Ullah, S. D. Khan, and F. A. Cheikh, “Stacked lstm network for human activity recognition using smartphone data,” in EUVIP . IEEE, 2019, pp. 175–180
2019
Earlier work this paper cites.
R. Morais, V. Le, T. Tran, B. Saha, M. Mansour, and S. Venkatesh, “Learning regularity in skeleton trajectories for anomaly detection in videos,” in CVPR , 2019, pp. 11 996–12 004
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, p. 9, 2019
2019
Earlier work this paper cites.
S. Yun, D. Han, S. J. Oh, S. Chun, J. Choe, and Y. Yoo, “Cutmix: Regularization strategy to train strong classifiers with localizable features,” in ICCV , 2019, pp. 6023–6032
2019
Earlier work this paper cites.
A. Saeed, T. Ozcelebi, and J. Lukkien, “Multi-task self-supervised learning for human activity detection,” IMWUT , vol. 3, no. 2, pp. 1–30, 2019
2019
Cited alongside, same era.
X. Qin, Y. Chen, J. Wang, and C. Yu, “Cross-dataset activity recognition via adaptive spatial-temporal transfer learning,” IMWUT , vol. 3, no. 4, pp. 1–25, 2019
2019
Cited alongside, same era.
F. M. Thoker and J. Gall, “Cross-modal knowledge distillation for action recognition,” in ICIP , 2019, pp. 6–10
2019
Cited alongside, same era.
T. Elsken, J. H. Metzen, and F. Hutter, “Neural architecture search: A survey,” Journal of Machine Learning Research , vol. 20, no. 55, pp. 1–21, 2019
2019
Cited alongside, same era.
J.-M. Pérez-Rúa, V. Vielzeuf, S. Pateux, M. Baccouche, and F. Jurie, “Mfas: Multimodal fusion architecture search,” in CVPR , 2019, pp. 6966–6975
2019
Y. Li, H. Liu, and H. Tang, “Multi-modal perception attention network with self-supervised learning for audio-visual speaker tracking,” in AAAI , vol. 36, no. 2, 2022, pp. 1456–1463
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Jain, C. I. Tang, C. Min, F. Kawsar, and A. Mathur, “Collossl: Collaborative self-supervised learning for human activity recognition,” IMWUT , vol. 6, no. 1, pp. 1–28, 2022
2022
Later among the works it cites.
R. Brinzea, B. Khaertdinov, and S. Asteriadis, “Contrastive learning with cross-modal knowledge mining for multimodal human activity recognition,” in IJCNN . IEEE, 2022, pp. 01–08
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
X. Wang, J.-F. Hu, J.-H. Lai, J. Zhang, and W.-S. Zheng, “Progressive teacher-student learning for early action prediction,” in CVPR , 2019, pp. 3556–3565
2019
Cited alongside, same era.
L. M. Dang, K. Min, H. Wang, M. J. Piran, C. H. Lee, and H. Moon, “Sensor-based and vision-based human activity recognition: A comprehensive survey,” Pattern Recognition , vol. 108, p. 107561, 2020
2020
Cited alongside, same era.
D. R. Beddiar, B. Nini, M. Sabokrou, and A. Hadid, “Vision-based human activity recognition: a survey,” Multimedia Tools and Applications , vol. 79, no. 41-42, pp. 30 509–30 555, 2020
2020
Cited alongside, same era.
B. Sheng, F. Xiao, L. Sha, and L. Sun, “Deep spatial–temporal model based cross-scene action recognition using commodity wifi,” IoT-J , vol. 7, no. 4, pp. 3592–3601, 2020
2020
Cited alongside, same era.
K. Xia, J. Huang, and H. Wang, “Lstm-cnn architecture for human activity recognition,” IEEE Access , vol. 8, pp. 56 855–56 866, 2020
2020
Cited alongside, same era.
T. Nagarajan, Y. Li, C. Feichtenhofer, and K. Grauman, “Ego-topo: Environment affordances from egocentric video,” in CVPR , 2020, pp. 163–172
2020
Cited alongside, same era.
F. Al Machot, M. R. Elkobaisi, and K. Kyamakya, “Zero-shot human activity recognition using non-visual sensors,” Sensors , vol. 20, no. 3, p. 825, 2020
2020
Cited alongside, same era.
2022
Later among the works it cites.
W. C. Sleeman IV, R. Kapoor, and P. Ghosh, “Multimodal classification: Current landscape, taxonomy and future directions,” CSUR , vol. 55, no. 7, pp. 1–31, 2022
2022
Later among the works it cites.
H. K. Lee, J. Lee, and S. B. Kim, “Boundary-focused generative adversarial networks for imbalanced and multimodal time series,” TKDE , vol. 34, no. 9, pp. 4102–4118, 2022
2022
Later among the works it cites.
Y.-L. Sung, J. Cho, and M. Bansal, “Vl-adapter: Parameter-efficient transfer learning for vision-and-language tasks,” in CVPR , 2022, pp. 5227–5237
2022
Later among the works it cites.
B. Khaertdinov and S. Asteriadis, “Temporal feature alignment in contrastive self-supervised learning for human activity recognition,” in IJCB . IEEE, 2022, pp. 1–9
2022
Later among the works it cites.
2022
Later among the works it cites.
W. Lu, J. Wang, Y. Chen, S. J. Pan, C. Hu, and X. Qin, “Semantic-discriminative mixup for generalizable sensor-based cross-domain activity recognition,” IMWUT , vol. 6, no. 2, pp. 1–19, 2022
2022
Later among the works it cites.
A. Andonian, S. Chen, and R. Hamid, “Robust cross-modal representation learning with progressive self-distillation,” in CVPR , 2022, pp. 16 430–16 441
2022
Later among the works it cites.
Y. Zhou, H. Zhao, Y. Huang, T. Riedel, M. Hefenbrock, and M. Beigl, “Tinyhar: A lightweight deep learning model designed for human activity recognition,” in UbiComp , 2022, pp. 89–93
2022
Later among the works it cites.
V. Mazzia, S. Angarano, F. Salvetti, F. Angelini, and M. Chiaberge, “Action transformer: A self-attention model for short-time pose-based human action recognition,” Pattern Recognition , vol. 124, p. 108487, 2022
2022
Later among the works it cites.
Y.-T. Hsieh, K. Anjum, and D. Pompili, “Ultra-low power analog recurrent neural network design approximation for wireless health monitoring,” in MASS . IEEE, 2022, pp. 211–219
2022
Later among the works it cites.
Q. Liu, D. Xing, L. Feng, H. Tang, and G. Pan, “Event-based multimodal spiking neural network with attention mechanism,” in ICASSP . IEEE, 2022, pp. 8922–8926
2022
Later among the works it cites.
V. Fra, E. Forno, R. Pignari, T. C. Stewart, E. Macii, and G. Urgese, “Human activity recognition: suitability of a neuromorphic approach for on-edge aiot applications,” Neuromorphic Computing and Engineering , vol. 2, no. 1, p. 014006, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
N. Maray, A. H. Ngu, J. Ni, M. Debnath, and L. Wang, “Transfer learning on small datasets for improved fall detection,” Sensors , vol. 23, no. 3, p. 1105, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
C. Zhang, A. Gupta, and A. Zisserman, “Helping hands: An object-aware ego-centric video recognition model,” in ICCV , 2023, pp. 13 901–13 912
2023
Later among the works it cites.
H. Duan, M. Xu, B. Shuai, D. Modolo, Z. Tu, J. Tighe, and A. Bergamo, “Skeletr: Towards skeleton-based action recognition in the wild,” in ICCV , 2023, pp. 13 634–13 644
2023
Later among the works it cites.
2023
Later among the works it cites.
W. Peebles and S. Xie, “Scalable diffusion models with transformers,” in ICCV , 2023, pp. 4195–4205
2023
Later among the works it cites.
2023
Later among the works it cites.
M. Martínez-Zarzuela, J. González-Alonso, M. Antón-Rodríguez, F. J. Díaz-Pernas, H. Müller, and C. Simón-Martínez, “Multimodal video and imu kinematic dataset on daily life activities using affordable devices,” Scientific Data , vol. 10, no. 1, p. 648, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
M. Loper, N. Mahmood, J. Romero, G. Pons-Moll, and M. J. Black, “Smpl: A skinned multi-person linear model,” in Seminal Graphics Papers: Pushing the Boundaries, Volume 2 , 2023, pp. 851–866
2023
Later among the works it cites.
2023
Later among the works it cites.
I. Gavier, Y. Liu, and S. I. Lee, “Virtualimu: Generating virtual wearable inertial data from video for deep learning applications,” in BSN . IEEE, 2023, pp. 1–4
2023
Later among the works it cites.
J. Li, L. Huang, S. Shah, S. J. Jones, Y. Jin, D. Wang, A. Russell, S. Choi, Y. Gao, J. Yuan et al. , “Signring: Continuous american sign language recognition using imu rings and virtual imu data,” IMWUT , vol. 7, no. 3, pp. 1–29, 2023
2023
Later among the works it cites.
P. S. Santhalingam, P. Pathak, H. Rangwala, and J. Kosecka, “Synthetic smartwatch imu data generation from in-the-wild asl videos,” IMWUT , vol. 7, no. 2, pp. 1–34, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Hua, X. Cui, X. Li, K. Tang, and P. Zhu, “Multimodal fake news detection through data augmentation-based contrastive learning,” Applied Soft Computing , vol. 136, p. 110125, 2023
2023
Later among the works it cites.
A. Josi, M. Alehdaghi, R. M. Cruz, and E. Granger, “Multimodal data augmentation for visual-infrared person reid with corrupted data,” in WACV , 2023, pp. 32–41
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
R. Girdhar, A. El-Nouby, Z. Liu, M. Singh, K. V. Alwala, A. Joulin, and I. Misra, “Imagebind: One embedding space to bind them all,” in CVPR , 2023, pp. 15 180–15 190
2023
Later among the works it cites.
M. Junaid, S. Ali, F. Eid, S. El-Sappagh, and T. Abuhmed, “Explainable machine learning models based on multimodal time-series data for the early detection of parkinson’s disease,” Computer Methods and Programs in Biomedicine , vol. 234, p. 107495, 2023
2023
Later among the works it cites.
S. G. Dhekane, H. Haresamudram, M. Thukral, and T. Plötz, “How much unlabeled data is really needed for effective self-supervised human activity recognition?” in ISWC , 2023, pp. 66–70
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Quan, Q. Chen, W. Wang, M. Zhang, X. Li, Y. Li, and Z. Liu, “Smtdkd: A semantic-aware multimodal transformer fusion decoupled knowledge distillation method for action recognition,” IEEE Sensors Journal , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
J. K. Eshraghian, M. Ward, E. O. Neftci, X. Wang, G. Lenz, G. Dwivedi, M. Bennamoun, D. S. Jeong, and W. D. Lu, “Training spiking neural networks using lessons from deep learning,” Proceedings of the IEEE , 2023
2023
Later among the works it cites.
S. Nooruddin, M. M. Islam, F. Karray, and G. Muhammad, “A multi-resolution fusion approach for human activity recognition from video data in tiny edge devices,” Information Fusion , vol. 100, p. 101953, 2023
2023
Later among the works it cites.
Z. Gao, Y. Wang, J. Chen, J. Xing, S. Patel, X. Liu, and Y. Shi, “Mmtsa: Multi-modal temporal segment attention network for efficient human activity recognition,” IMWUT , vol. 7, no. 3, pp. 1–26, 2023
2023
Later among the works it cites.
Q. Cai, X. Liu, K. Zhang, X. Xie, X. Tong, and K. Li, “Acf: An adaptive compression framework for multimodal network in embedded devices,” TMC , 2023
2023
Later among the works it cites.
S. Ö. Bursa, Ö. Durmaz İncel, and G. Işıklar Alptekin, “Building lightweight deep learning models with tensorflow lite for human activity recognition on mobile devices,” Annals of Telecommunications , vol. 78, no. 11, pp. 687–702, 2023
2023
Later among the works it cites.
M.-S. Kang, D. Kang, and H. Kim, “Efficient skeleton-based action recognition via joint-mapping strategies,” in WACV , 2023, pp. 3403–3412
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Wang, B. Dong, Y. Zhang, Y. Zhou, H. Mei, Z. Wei, and X. Yang, “Event-enhanced multi-modal spiking neural network for dynamic obstacle avoidance,” in ACM MM , 2023, pp. 3138–3148
2023
Later among the works it cites.
L. Guo, Z. Gao, J. Qu, S. Zheng, R. Jiang, Y. Lu, and H. Qiao, “Transformer-based spiking neural networks for multimodal audio-visual classification,” TCDS , 2023
2023
Later among the works it cites.
A. R. Khan, H. U. Manzoor, F. Ayaz, M. A. Imran, and A. Zoha, “A privacy and energy-aware federated framework for human activity recognition,” Sensors , vol. 23, no. 23, p. 9339, 2023
2023
Later among the works it cites.
D. Si, Q. Ye, J. Lv, Y. Zhou, and J. Lv, “Violence-mfas: Audio-visual violence detection using multimodal fusion architecture search,” in ICONIP . Springer, 2023, pp. 205–216
2023
Later among the works it cites.
W.-S. Lim, W. Seo, D.-W. Kim, and J. Lee, “Efficient human activity recognition using lookup table-based neural architecture search for mobile devices,” IEEE Access , 2023
2023
Later among the works it cites.
N. Zheng, X. Song, T. Su, W. Liu, Y. Yan, and L. Nie, “Egocentric early action prediction via adversarial knowledge distillation,” TOMM , vol. 19, no. 2, pp. 1–21, 2023
2023
Later among the works it cites.
H. Gammulle, D. Ahmedt-Aristizabal, S. Denman, L. Tychsen-Smith, L. Petersson, and C. Fookes, “Continuous human action recognition for human-machine interaction: a review,” CSUR , vol. 55, no. 13s, pp. 1–38, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2024
Closest in time.
L. Yang, O. Amin, and B. Shihada, “Intelligent wearable systems: Opportunities and challenges in health and sports,” CSUR , 2024
2024
Closest in time.
T. Shiota, M. Takagi, K. Kumagai, H. Seshimo, and Y. Aono, “Egocentric action recognition by capturing hand-object contact and object state,” in WACV , 2024, pp. 6541–6551
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
J. Li, C. Liu, S. Cheng, R. Arcucci, and S. Hong, “Frozen language model helps ecg zero-shot learning,” in Medical Imaging with Deep Learning . PMLR, 2024, pp. 402–415
2024
Closest in time.
K. Xia, W. Li, S. Gan, and S. Lu, “Ts2act: Few-shot human activity sensing with cross-modal co-learning,” IMWUT , vol. 7, no. 4, pp. 1–22, 2024
2024
Closest in time.
Y. Li, X. Sun, Z. Yang, and H. Huang, “Snnauth: Sensor-based continuous authentication on smartphones using spiking neural networks,” IoT-J , 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
M. Ijaz, R. Diaz, and C. Chen, “Multimodal transformer for nursing activity recognition,” in CVPR Workshop , 2022, pp. 2065–2074
2074
Closest in time.