Fetching the paper…
Reading the bibliography…
Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human--computer interaction, that measure and improve our daily lives.
Invariant Risk Minimization. arXiv 2020
Arjovsky, M.; Bottou, L.; Gulrajani, I.; Lopez-Paz, D · 1907
Earlier work this paper cites.
Modular Learning in Neural Networks
Ballard, D.H · 1987
Earlier work this paper cites.
Approximation by superpositions of a sigmoidal function
Cybenko, G · 1989
Earlier work this paper cites.
Gesture Recognition Using Recurrent Neural Networks
Murakami, K.; Taguchi, H · 1991
Earlier work this paper cites.
Recognition and anticipation of hand motions using a recurrent neural network
Vamplew, P.; Adams, A · 1995
Earlier work this paper cites.
Long short-term memory
Hochreiter, S.; Schmidhuber, J · 1997
Earlier work this paper cites.
Gans may have no nash equilibria
Farnia, F.; Ozdaglar, A · 2002
Earlier work this paper cites.
A dynamic recurrent neural network for multiple muscles electromyographic mapping to elevation angles of the lower limb in human locomotion
Chéron, G.; Leurs, F.; Bengoetxea, A.; Draye, J.; Destrée, M.; Dan, B · 2003
Earlier work this paper cites.
EMG-based motion discrimination using a novel recurrent neural network
Bu, N.; Fukuda, O.; Tsuji, T · 2003
Earlier work this paper cites.
Recurrent Neural Networks Are Universal Approximators
Schäfer, A.M.; Zimmermann, H.G · 2006
Earlier work this paper cites.
A DBN-based multi-level stochastic spoken language understanding system
Lefevre, F · 2006
Earlier work this paper cites.
Cheng, X.; Zhang, L.; Tang, Y.; Liu, Y.; Wu, H.; He, J · 2006
Earlier work this paper cites.
Real time gesture recognition using continuous time recurrent neural networks
Bailador, G.; Roggen, D.; Tröster, G.; Triviño, G · 2007
Earlier work this paper cites.
Recognition of the physiological actions of the triphasic EMG pattern by a dynamic recurrent neural network
Cheron, G.; Cebolla, A.M.; Bengoetxea, A.; Leurs, F.; Dan, B · 2007
Earlier work this paper cites.
Accurate Activity Recognition in a Home Setting
van Kasteren, T.; Noulas, A.; Englebienne, G.; Kröse, B · 2008
Earlier work this paper cites.
Activity Recognition from On-body Sensors: Accuracy-power Trade-off by Dynamic Sensor Selection
Zappi, P.; Lombriser, C.; Stiefmeier, T.; Farella, E.; Roggen, D.; Benini, L.; Tröster, G · 2008
Earlier work this paper cites.
Toward a mixed-signal reconfigurable ASIC for real-time activity recognition
Wang, L.; Thiemjarus, S.; Lo, B.; Yang, G.Z · 2008
Earlier work this paper cites.
Wearable Assistant for Parkinson’s Disease Patients With the Freezing of Gait Symptom
Bachlin, M.; Plotnik, M.; Roggen, D.; Maidan, I.; Hausdorff, J.M.; Giladi, N.; Troster, G · 2009
Earlier work this paper cites.
uWave: Accelerometer-based personalized gesture recognition and its applications
Liu, J.; Wang, Z.; Zhong, L.; Wickramasuriya, J.; Vasudevan, V · 2009
Earlier work this paper cites.
Learning Deep Architectures for AI
Bengio, Y · 2009
Earlier work this paper cites.
Estimation of finger joint angles from sEMG using a recurrent neural network with time-delayed input vectors
Hioki, M.; Kawasaki, H · 2009
Earlier work this paper cites.
Comparative Study on Classifying Human Activities with Miniature Inertial and Magnetic Sensors
Altun, K.; Barshan, B.; Tunçel, O · 2010
Earlier work this paper cites.
Khatiwada, P.; Subedi, M.; Chatterjee, A.; Gerdes, M.W · 2010
Earlier work this paper cites.
Lack of exercise is a major cause of chronic diseases
Booth, F.W.; Roberts, C.K.; Laye, M.J · 2011
Earlier work this paper cites.
Activity Recognition Using Cell Phone Accelerometers
Kwapisz, J.R.; Weiss, G.M.; Moore, S.A · 2011
Earlier work this paper cites.
HASC Challenge: Gathering Large Scale Human Activity Corpus for the Real-world Activity Understandings
Kawaguchi, N.; Ogawa, N.; Iwasaki, Y.; Kaji, K.; Terada, T.; Murao, K.; Inoue, S.; Kawahara, Y.; Sumi, Y.; Nishio, N · 2011
Earlier work this paper cites.
HASC2011corpus: Towards the Common Ground of Human Activity Recognition
Kawaguchi, N.; Yang, Y.; Yang, T.; Ogawa, N.; Iwasaki, Y.; Kaji, K.; Terada, T.; Murao, K.; Inoue, S.; Kawahara, Y.; et al · 2011
Earlier work this paper cites.
Correlates of physical activity: Why are some people physically active and others not?
Bauman, A.E.; Reis, R.S.; Sallis, J.F.; Wells, J.C.; Loos, R.J.; Martin, B.W · 2012
Earlier work this paper cites.
The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition
Chavarriaga, R.; Sagha, H.; Calatroni, A.; Digumarti, S.T.; Tröster, G.; del R. Millán, J.; Roggen, D · 2012
Earlier work this paper cites.
Introducing a New Benchmarked Dataset for Activity Monitoring
Reiss, A.; Stricker, D · 2012
Earlier work this paper cites.
USC-HAD: A Daily Activity Dataset for Ubiquitous Activity Recognition Using Wearable Sensors
Zhang, M.; Sawchuk, A.A · 2012
Earlier work this paper cites.
A practical guide to training restricted Boltzmann machines. In Neural Networks: Tricks of the Trade
Hinton, G.E · 2012
Earlier work this paper cites.
Introduction to Various Reinforcement Learning Algorithms. Part I (Q-Learning, SARSA, DQN, DDPG). 2018
Kung-Hsiang (Steeve), H · 2012
Earlier work this paper cites.
First-Person Activity Recognition: What Are They Doing to Me?
Ryoo, M.S.; Matthies, L · 2013
Earlier work this paper cites.
A public domain dataset for human activity recognition using smartphones
Anguita, D.; Ghio, A.; Oneto, L.; Parra, X.; Reyes-Ortiz, J.L · 2013
Earlier work this paper cites.
Towards Physical Activity Recognition Using Smartphone Sensors
Shoaib, M.; Scholten, H.; Havinga, P.J.M · 2013
Earlier work this paper cites.
Convolutional Neural Networks for human activity recognition using mobile sensors
Zeng, M.; Nguyen, L.T.; Yu, B.; Mengshoel, O.J.; Zhu, J.; Wu, P.; Zhang, J · 2014
Earlier work this paper cites.
Fusion of smartphone motion sensors for physical activity recognition
Shoaib, M.; Bosch, S.; Incel, O.; Scholten, H.; Havinga, P · 2014
Earlier work this paper cites.
mHealthDroid: A novel framework for agile development of mobile health applications
Banos, O.; Garcia, R.; Holgado-Terriza, J.A.; Damas, M.; Pomares, H.; Rojas, I.; Saez, A.; Villalonga, C · 2014
Earlier work this paper cites.
A public domain dataset for ADL recognition using wrist-placed accelerometers
Bruno, B.; Mastrogiovanni, F.; Sgorbissa, A · 2014
Earlier work this paper cites.
TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise
Zhang, Z.; Pi, Z.; Liu, B · 2014
Earlier work this paper cites.
A Tutorial on Human Activity Recognition Using Body-worn Inertial Sensors
Bulling, A.; Blanke, U.; Schiele, B · 2014
Earlier work this paper cites.
Unsupervised Feature Learning for Human Activity Recognition Using Smartphone Sensors
Li, Y.; Shi, D.; Ding, B.; Liu, D · 2014
Earlier work this paper cites.
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J.; Gülçehre, Ç.; Cho, K.; Bengio, Y · 2014
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; Bengio, Y · 2014
Earlier work this paper cites.
Fitbit®: An accurate and reliable device for wireless physical activity tracking
Diaz, K.M.; Krupka, D.J.; Chang, M.J.; Peacock, J.; Ma, Y.; Goldsmith, J.; Schwartz, J.E.; Davidson, K.W · 2015
Earlier work this paper cites.
Using Deep Learning for Energy Expenditure Estimation with wearable sensors
Zhu, J.; Pande, A.; Mohapatra, P.; Han, J.J · 2015
Earlier work this paper cites.
A Deep Learning Approach to Human Activity Recognition Based on Single Accelerometer
Chen, Y.; Xue, Y · 2015
Earlier work this paper cites.
Human Activity Recognition Using Wearable Sensors by Deep Convolutional Neural Networks
Jiang, W.; Yin, Z · 2015
Earlier work this paper cites.
PD Disease State Assessment in Naturalistic Environments Using Deep Learning
Hammerla, N.Y.; Fisher, J.M.; Andras, P.; Rochester, L.; Walker, R.; Plotz, T · 2015
Earlier work this paper cites.
Monitoring eating habits using a piezoelectric sensor-based necklace
Kalantarian, H.; Alshurafa, N.; Le, T.; Sarrafzadeh, M · 2015
Earlier work this paper cites.
Improving activity recognition using a wearable barometric pressure sensor in mobility-impaired stroke patients
Massé, F.; Gonzenbach, R.R.; Arami, A.; Paraschiv-Ionescu, A.; Luft, A.R.; Aminian, K · 2015
Earlier work this paper cites.
UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor
Chen, C.; Jafari, R.; Kehtarnavaz, N · 2015
Earlier work this paper cites.
Smart Devices Are Different: Assessing and MitigatingMobile Sensing Heterogeneities for Activity Recognition
Stisen, A.; Blunck, H.; Bhattacharya, S.; Prentow, T.S.; Kjærgaard, M.B.; Dey, A.; Sonne, T.; Jensen, M.M · 2015
Earlier work this paper cites.
Design, implementation and validation of a novel open framework for agile development of mobile health applications
Banos, O.; Villalonga, C.; Garcia, R.; Saez, A.; Damas, M.; Holgado-Terriza, J.A.; Lee, S.; Pomares, H.; Rojas, I · 2015
Earlier work this paper cites.
Deep convolutional neural networks on multichannel time series for human activity recognition
Yang, J.; Nguyen, M.N.; San, P.P.; Li, X.L.; Krishnaswamy, S · 2015
Earlier work this paper cites.
Deep Activity Recognition Models with Triaxial Accelerometers
Abu Alsheikh, M.; Selim, A.; Niyato, D.; Doyle, L.; Lin, S.; Tan, H.P · 2015
Earlier work this paper cites.
Real-Time Activity Recognition on Smartphones Using Deep Neural Networks
Zhang, L.; Wu, X.; Luo, D · 2015
Earlier work this paper cites.
Recognizing Human Activities from Raw Accelerometer Data Using Deep Neural Networks
Zhang, L.; Wu, X.; Luo, D · 2015
Earlier work this paper cites.
Deep convolutional neural networks for human activity recognition with smartphone sensors
Ronao, C.A.; Cho, S.B · 2015
Earlier work this paper cites.
Unsupervised and semi-supervised learning with categorical generative adversarial networks
Springenberg, J.T · 2015
Earlier work this paper cites.
Automated Synchronization of Driving Data Using Vibration and Steering Events
Fridman, L.; Brown, D.E.; Angell, W.; Abdic, I.; Reimer, B.; Noh, H.Y · 2015
Earlier work this paper cites.
Federated optimization: Distributed optimization beyond the datacenter
Konečnỳ, J.; McMahan, B.; Ramage, D · 2015
Earlier work this paper cites.
DeepEar: Robust Smartphone Audio Sensing in Unconstrained Acoustic Environments Using Deep Learning
Lane, N.D.; Georgiev, P.; Qendro, L · 2015
Earlier work this paper cites.
Can Deep Learning Revolutionize Mobile Sensing?
Lane, N.D.; Georgiev, P · 2015
Earlier work this paper cites.
Behavior Change with Fitness Technology in Sedentary Adults: A Review of the Evidence for Increasing Physical Activity
Bisson, A.; E. Lachman, M · 2016
Earlier work this paper cites.
Human Activity Recognition with Smartphone Sensors Using Deep Learning Neural Networks
Ronao, C.A.; Cho, S.B · 2016
Earlier work this paper cites.
Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson’s disease assessment
Eskofier, B.M.; Lee, S.I.; Daneault, J.; Golabchi, F.N.; Ferreira-Carvalho, G.; Vergara-Diaz, G.; Sapienza, S.; Costante, G.; Klucken, J.; Kautz, T.; et al · 2016
Earlier work this paper cites.
Dynamic hand gesture recognition for wearable devices with low complexity recurrent neural networks
Shin, S.; Sung, W · 2016
Earlier work this paper cites.
The hRing: A wearable haptic device to avoid occlusions in hand tracking
Pacchierotti, C.; Salvietti, G.; Hussain, I.; Meli, L.; Prattichizzo, D · 2016
Earlier work this paper cites.
Development of a wearable HCI controller through sEMG & IMU sensor fusion
Kim, J.; Kim, M.; Kim, K · 2016
Earlier work this paper cites.
ViBand: High-Fidelity Bio-Acoustic Sensing Using Commodity Smartwatch Accelerometers
Laput, G.; Xiao, R.; Harrison, C · 2016
Earlier work this paper cites.
Using respiratory signals for the recognition of human activities
Ramos-Garcia, R.I.; Tiffany, S.; Sazonov, E · 2016
Earlier work this paper cites.
Activity recognition in a home setting using off the shelf smart watch technology
Filippoupolitis, A.; Takand, B.; Loukas, G · 2016
Earlier work this paper cites.
Actitracker: A Smartphone-Based Activity Recognition System for Improving Health and Well-Being
Weiss, G.M.; Lockhart, J.W.; Pulickal, T.T.; McHugh, P.T.; Ronan, I.H.; Timko, J.L · 2016
Earlier work this paper cites.
Recognition of human activities using continuous autoencoders with wearable sensors
Wang, L · 2016
Earlier work this paper cites.
The MobiAct Dataset: Recognition of Activities of Daily Living using Smartphones
Vavoulas, G.; Chatzaki, C.; Malliotakis, T.; Pediaditis, M.; Tsiknakis, M · 2016
Earlier work this paper cites.
Towards Multimodal Deep Learning for Activity Recognition on Mobile Devices
Radu, V.; Lane, N.D.; Bhattacharya, S.; Mascolo, C.; Marina, M.K.; Kawsar, F · 2016
Earlier work this paper cites.
iHear Food: Eating Detection Using Commodity Bluetooth Headsets
Gao, Y.; Zhang, N.; Wang, H.; Ding, X.; Ye, X.; Chen, G.; Cao, Y · 2016
Earlier work this paper cites.
Deep learning for human activity recognition: A resource efficient implementation on low-power devices
Ravi, D.; Wong, C.; Lo, B.; Yang, G.Z · 2016
Earlier work this paper cites.
Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors
Ha, S.; Choi, S · 2016
Earlier work this paper cites.
Reram crossbar based recurrent neural network for human activity detection
Long, Y.; Jung, E.M.; Kung, J.; Mukhopadhyay, S · 2016
Earlier work this paper cites.
Deep, convolutional, and recurrent models for human activity recognition using wearables
Hammerla, N.Y.; Halloran, S.; Plötz, T · 2016
Earlier work this paper cites.
Multicolumn bidirectional long short-term memory for mobile devices-based human activity recognition
Tao, D.; Wen, Y.; Hong, R · 2016
Earlier work this paper cites.
Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition
Ordóñez, F.J.; Roggen, D · 2016
Earlier work this paper cites.
Semi-supervised learning with generative adversarial networks
Odena, A · 2016
Earlier work this paper cites.
Mode regularized generative adversarial networks
Che, T.; Li, Y.; Jacob, A.P.; Bengio, Y.; Li, W · 2016
Earlier work this paper cites.
Generative Adversarial Text to Image Synthesis
Reed, S.; Akata, Z.; Yan, X.; Logeswaran, L.; Schiele, B.; Lee, H · 2016
Earlier work this paper cites.
Lasagna: Towards Deep Hierarchical Understanding and Searching over Mobile Sensing Data
Liu, C.; Zhang, L.; Liu, Z.; Liu, K.; Li, X.; Liu, Y · 2016
Earlier work this paper cites.
DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices
Lane, N.D.; Bhattacharya, S.; Georgiev, P.; Forlivesi, C.; Jiao, L.; Qendro, L.; Kawsar, F · 2016
Earlier work this paper cites.
Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables
Bhattacharya, S.; Lane, N.D · 2016
Earlier work this paper cites.
Binarized-BLSTM-RNN based Human Activity Recognition
Edel, M.; Köppe, E · 2016
Earlier work this paper cites.
SEUS: A Wearable Multi-Channel Acoustic Headset Platform to Improve Pedestrian Safety: Demo Abstract ; Association for Computing Machinery: New York, NY, USA, 2016; pp. 330–331
Chandrasekaran, R.; de Godoy, D.; Xia, S.; Islam, M.T.; Islam, B.; Nirjon, S.; Kinget, P.; Jiang, X · 2016
Earlier work this paper cites.
Physical activity recognition by smartphones, a survey
Morales, J.; Akopian, D · 2017
Earlier work this paper cites.
Active transport and obesity prevention–a transportation sector obesity impact scoping review and assessment for Melbourne, Australia
Brown, V.; Moodie, M.; Herrera, A.M.; Veerman, J.; Carter, R · 2017
Earlier work this paper cites.
Human activity recognition from accelerometer data using Convolutional Neural Network
Lee, S.M.; Yoon, S.M.; Cho, H · 2017
Earlier work this paper cites.
Weakly-supervised learning for Parkinson’s Disease tremor detection
Zhang, A.; Cebulla, A.; Panev, S.; Hodgins, J.; De la Torre, F · 2017
Earlier work this paper cites.
Data Augmentation of Wearable Sensor Data for Parkinson’s Disease Monitoring Using Convolutional Neural Networks
Um, T.T.; Pfister, F.M.J.; Pichler, D.; Endo, S.; Lang, M.; Hirche, S.; Fietzek, U.; Kulić, D · 2017
Earlier work this paper cites.
Objective assessment of depressive symptoms with machine learning and wearable sensors data
Ghandeharioun, A.; Fedor, S.; Sangermano, L.; Ionescu, D.; Alpert, J.; Dale, C.; Sontag, D.; Picard, R · 2017
Earlier work this paper cites.
Try Walking in My Shoes, if You Can: Accurate Gait Recognition Through Deep Learning
Giorgi, G.; Martinelli, F.; Saracino, A.; Sheikhalishahi, M · 2017
Earlier work this paper cites.
Harke: Human activity recognition from kinetic energy harvesting data in wearable devices
Khalifa, S.; Lan, G.; Hassan, M.; Seneviratne, A.; Das, S.K · 2017
Earlier work this paper cites.
A survey of activity recognition in egocentric lifelogging datasets
Hamid, A.; Brahim, A.; Mohammed, O.; et al · 2017
Earlier work this paper cites.
UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones
Micucci, D.; Mobilio, M.; Napoletano, P · 2017
Earlier work this paper cites.
Recognizing Detailed Human Context in the Wild from Smartphones and Smartwatches
Vaizman, Y.; Ellis, K.; Lanckriet, G · 2017
Earlier work this paper cites.
Automated analysis of in meal eating behavior using a commercial wristband IMU sensor
Kyritsis, K.; Tatli, C.L.; Diou, C.; Delopoulos, A · 2017
Earlier work this paper cites.
BreathPrint: Breathing Acoustics-based User Authentication
Chauhan, J.; Hu, Y.; Seneviratne, S.; Misra, A.; Seneviratne, A.; Lee, Y · 2017
Earlier work this paper cites.
A wearable hand gesture recognition device based on acoustic measurements at wrist
Siddiqui, N.; Chan, R.H.M · 2017
Earlier work this paper cites.
An effective deep autoencoder approach for online smartphone-based human activity recognition
Almaslukh, B.; AlMuhtadi, J.; Artoli, A · 2017
Earlier work this paper cites.
Unsupervised deep representation learning to remove motion artifacts in free-mode body sensor networks
Mohammed, S.; Tashev, I · 2017
Earlier work this paper cites.
Time-elastic generative model for acceleration time series in human activity recognition
Munoz-Organero, M.; Ruiz-Blazquez, R · 2017
Earlier work this paper cites.
Smartphone Continuous Authentication Using Deep Learning Autoencoders
Centeno, M.P.; v. Moorsel, A.; Castruccio, S · 2017
Earlier work this paper cites.
Towards automatic feature extraction for activity recognition from wearable sensors: A deep learning approach
Chikhaoui, B.; Gouineau, F · 2017
Cited alongside, same era.
Learning deep and shallow features for human activity recognition
Sani, S.; Massie, S.; Wiratunga, N.; Cooper, K · 2017
Cited alongside, same era.
User adaptation of convolutional neural network for human activity recognition
Matsui, S.; Inoue, N.; Akagi, Y.; Nagino, G.; Shinoda, K · 2017
Cited alongside, same era.
Deep neural network based human activity recognition for the order picking process
Grzeszick, R.; Lenk, J.M.; Rueda, F.M.; Fink, G.A.; Feldhorst, S.; ten Hompel, M · 2017
Cited alongside, same era.
An investigation of recurrent neural network for daily activity recognition using multi-modal signals
Tamamori, A.; Hayashi, T.; Toda, T.; Takeda, K · 2017
Cited alongside, same era.
Human activity recognition using multi-head CNN followed by LSTM
Ahmad, W.; Kazmi, B.M.; Ali, H · 2019
Later among the works it cites.
Hybrid model featuring CNN and LSTM architecture for human activity recognition on smartphone sensor data
Deep, S.; Zheng, X · 2019
Later among the works it cites.
Abnormal gait recognition algorithm based on LSTM-CNN fusion network
Gao, J.; Gu, P.; Ren, Q.; Zhang, J.; Song, X · 2019
Later among the works it cites.
A Novel Distribution-Embedded Neural Network for Sensor-Based Activity Recognition
Qian, H.; Pan, S.J.; Da, B.; Miao, C · 2019
Later among the works it cites.
Know your mind: Adaptive cognitive activity recognition with reinforced CNN
Zhang, X.; Yao, L.; Wang, X.; Zhang, W.; Zhang, S.; Liu, Y · 2019
Later among the works it cites.
Stacked lstm network for human activity recognition using smartphone data
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Murad, A.; Pyun, J.Y · 2017
Cited alongside, same era.
Recognition of human hand activities based on a single wrist imu using recurrent neural networks
Rivera, P.; Valarezo, E.; Choi, M.T.; Kim, T.S · 2017
Cited alongside, same era.
Compressive representation for device-free activity recognition with passive RFID signal strength
Yao, L.; Sheng, Q.Z.; Li, X.; Gu, T.; Tan, M.; Wang, X.; Wang, S.; Ruan, W · 2017
Cited alongside, same era.
Arjovsky, M.; Chintala, S.; Bottou, L · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Gulrajani, I.; Ahmed, F.; Arjovsky, M.; Dumoulin, V.; Courville, A · 2017
Cited alongside, same era.
SenseGen: A deep learning architecture for synthetic sensor data generation
Alzantot, M.; Chakraborty, S.; Srivastava, M · 2017
Cited alongside, same era.
In Mobile Health: Sensors, Analytic Methods, and Applications
Rahman, M.; Ali, N.; Bari, R.; Saleheen, N.; al’Absi, M.; Ertin, E.; Kennedy, A.; Preston, K.L.; Kumar, S., mDebugger: Assessing and Diagnosing the Fidelity and Yield of Mobile Sensor Data · 2017
Cited alongside, same era.
Ullah, M.; Ullah, H.; Khan, S.D.; Cheikh, F.A · 2019
Later among the works it cites.
Human activity recognition on smartphones using a bidirectional lstm network
Hernández, F.; Suárez, L.F.; Villamizar, J.; Altuve, M · 2019
Later among the works it cites.
A Comprehensive Survey of Deep Learning for Image Captioning
Hossain, M.Z.; Sohel, F.; Shiratuddin, M.F.; Laga, H · 2019
Later among the works it cites.
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing
Chen, K.; Yao, L.; Zhang, D.; Chang, X.; Long, G.; Wang, S · 2019
Later among the works it cites.
ActiveHARNet: Towards On-Device Deep Bayesian Active Learning for Human Activity Recognition
Gudur, G.K.; Sundaramoorthy, P.; Umaashankar, V · 2019
Later among the works it cites.
Modeling Wrist Micromovements to Measure In-Meal Eating Behavior from Inertial Sensor Data
Kyritsis, K.; Diou, C.; Delopoulos, A · 2019
Later among the works it cites.
Imaging and fusing time series for wearable sensor-based human activity recognition
Qin, Z.; Zhang, Y.; Meng, S.; Qin, Z.; Choo, K.K.R · 2019
Later among the works it cites.
Incremental Learning to Personalize Human Activity Recognition Models: The Importance of Human AI Collaboration
Siirtola, P.; Röning, J · 2019
Later among the works it cites.
An Ultra-Low Energy Human Activity Recognition Accelerator for Wearable Health Applications. ACM Trans. Embed. Comput. Syst. 2019
Bhat, G.; Tuncel, Y.; An, S.; Lee, H.G.; Ogras, U.Y · 2019
Later among the works it cites.
Improving Pedestrian Safety in Cities Using Intelligent Wearable Systems
Xia, S.; de Godoy Peixoto, D.; Islam, B.; Islam, M.T.; Nirjon, S.; Kinget, P.R.; Jiang, X · 2019
Later among the works it cites.
Universality of deep convolutional neural networks
Zhou, D.X · 2020
Later among the works it cites.
NeckSense: A Multi-Sensor Necklace for Detecting Eating Activities in Free-Living Conditions
Zhang, S.; Zhao, Y.; Nguyen, D.T.; Xu, R.; Sen, S.; Hester, J.; Alshurafa, N · 2020
Later among the works it cites.
Electromyography—Wikipedia, The Free Encyclopedia. 2010
Electromyography · 2020
Later among the works it cites.
An improved performance of deep learning based on convolution neural network to classify the hand motion by evaluating hyper parameter
Triwiyanto, T.; Pawana, I.P.A.; Purnomo, M.H · 2020
Later among the works it cites.
New advances in mechanomyography sensor technology and signal processing: Validity and intrarater reliability of recordings from muscle
Meagher, C.; Franco, E.; Turk, R.; Wilson, S.; Steadman, N.; McNicholas, L.; Vaidyanathan, R.; Burridge, J.; Stokes, M · 2020
Later among the works it cites.
Human activity recognition using deep electroencephalography learning
Salehzadeh, A.; Calitz, A.P.; Greyling, J · 2020
Later among the works it cites.
Interpretable and accurate convolutional neural networks for human activity recognition
Kim, E · 2020
Later among the works it cites.
Layer-Wise Training Convolutional Neural Networks With Smaller Filters for Human Activity Recognition Using Wearable Sensors
Tang, Y.; Teng, Q.; Zhang, L.; Min, F.; He, J · 2020
Later among the works it cites.
Classification of Electromyographic Hand Gesture Signals using Machine Learning Techniques
Jia, G.; Lam, H.K.; Liao, J.; Wang, R · 2020
Later among the works it cites.
A multilayer interval type-2 fuzzy extreme learning machine for the recognition of walking activities and gait events using wearable sensors
Rubio-Solis, A.; Panoutsos, G.; Beltran-Perez, C.; Martinez-Hernandez, U · 2020
Later among the works it cites.
Unsupervised End-to-End Deep Model for Newborn and Infant Activity Recognition
Jun, K.; Choi, S · 2020
Later among the works it cites.
Human activities recognition with a single writs IMU via a Variational Autoencoder and android deep recurrent neural nets
Valarezo, A.E.; Rivera, L.P.; Park, H.; Park, N.; Kim, T.S · 2020
Later among the works it cites.
Deep learning approaches for detecting freezing of gait in Parkinson’s disease patients through on-body acceleration sensors
Sigcha, L.; Costa, N.; Pavón, I.; Costa, S.; Arezes, P.; López, J.M.; De Arcas, G · 2020
Later among the works it cites.
High Accuracy Drug-Target Protein Interaction Prediction Method based on DBN
Gu, W.; Wang, G.; Zhang, Z.; Mao, Y.; Xie, X.; He, Y · 2020
Later among the works it cites.
Transition-Aware Detection of Modes of Locomotion and Transportation Through Hierarchical Segmentation
Akbari, A.; Jafari, R · 2020
Later among the works it cites.
A body sensor data fusion and deep recurrent neural network-based behavior recognition approach for robust healthcare
Uddin, M.Z.; Hassan, M.M.; Alsanad, A.; Savaglio, C · 2020
Later among the works it cites.
DenseNetX and GRU for the Sussex-Huawei locomotion-transportation recognition challenge
Zhu, Y.; Luo, H.; Chen, R.; Zhao, F.; Su, L · 2020
Later among the works it cites.
Harmonic Loss Function for Sensor-Based Human Activity Recognition Based on LSTM Recurrent Neural Networks
Hu, Y.; Zhang, X.Q.; Xu, L.; He, F.X.; Tian, Z.; She, W.; Liu, W · 2020
Later among the works it cites.
Multitask LSTM Model for Human Activity Recognition and Intensity Estimation Using Wearable Sensor Data
Barut, O.; Zhou, L.; Luo, Y · 2020
Later among the works it cites.
LSTM-CNN architecture for human activity recognition
Xia, K.; Huang, J.; Wang, H · 2020
Later among the works it cites.
Combining LSTM and CNN for mode of transportation classification from smartphone sensors
Friedrich, B.; Lübbe, C.; Hein, A · 2020
Later among the works it cites.
A CNN-LSTM neural network for recognition of puffing in smoking episodes using wearable sensors
Senyurek, V.Y.; Imtiaz, M.H.; Belsare, P.; Tiffany, S.; Sazonov, E · 2020
Later among the works it cites.
A CNN-LSTM approach to human activity recognition
Mutegeki, R.; Han, D.S · 2020
Later among the works it cites.
Continuous angular position estimation of human ankle during unconstrained locomotion
Gupta, R.; Dhindsa, I.S.; Agarwal, R · 2020
Later among the works it cites.
Designing deep reinforcement learning systems for musculoskeletal modeling and locomotion analysis using wearable sensor feedback
Zheng, J.; Cao, H.; Chen, D.; Ansari, R.; Chu, K.C.; Huang, M.C · 2020
Later among the works it cites.
Synthetic sensor data for human activity recognition
Alharbi, F.; Ouarbya, L.; Ward, J.A · 2020
Later among the works it cites.
A unified generative model using generative adversarial network for activity recognition
Chan, M.H.; Noor, M.H.M · 2020
Later among the works it cites.
ActivityGAN: Generative adversarial networks for data augmentation in sensor-based human activity recognition
Li, X.; Luo, J.; Younes, R · 2020
Later among the works it cites.
Deep learning models for real-time human activity recognition with smartphones
Wan, S.; Qi, L.; Xu, X.; Tong, C.; Gu, Z · 2020
Later among the works it cites.
IMUTube: Automatic Extraction of Virtual on-Body Accelerometry from Video for Human Activity Recognition
Kwon, H.; Tong, C.; Haresamudram, H.; Gao, Y.; Abowd, G.D.; Lane, N.D.; Plötz, T · 2020
Later among the works it cites.
Deep Generative Cross-Modal on-Body Accelerometer Data Synthesis from Videos
Zhang, S.; Alshurafa, N · 2020
Later among the works it cites.
A Blockchain Platform for User Data Sharing Ensuring User Control and Incentives
Shrestha, A.K.; Vassileva, J.; Deters, R · 2020
Later among the works it cites.
SyncWISE: Window Induced Shift Estimation for Synchronization of Video and Accelerometry from Wearable Sensors
Zhang, Y.C.; Zhang, S.; Liu, M.; Daly, E.; Battalio, S.; Kumar, S.; Spring, B.; Rehg, J.M.; Alshurafa, N · 2020
Later among the works it cites.
A comprehensive survey on graph neural networks
Wu, Z.; Pan, S.; Chen, F.; Long, G.; Zhang, C.; Philip, S.Y · 2020
Later among the works it cites.
Deep learning for Heterogeneous Human Activity Recognition in Complex IoT Applications
Abdel-Basset, M.; Hawash, H.; Chang, V.; Chakrabortty, R.K.; Ryan, M · 2020
Later among the works it cites.
Physical Activity Recognition With Statistical-Deep Fusion Model Using Multiple Sensory Data for Smart Health
Huynh-The, T.; Hua, C.H.; Tu, N.A.; Kim, D.S · 2020
Later among the works it cites.
Zygarde: Time-Sensitive On-Device Deep Inference and Adaptation on Intermittently-Powered Systems
Islam, B.; Nirjon, S · 2020
Later among the works it cites.
SPIDERS: Low-Cost Wireless Glasses for Continuous In-Situ Bio-Signal Acquisition and Emotion Recognition
Nie, J.; Hu, Y.; Wang, Y.; Xia, S.; Jiang, X · 2020
Later among the works it cites.
Demo Abstract: Wireless Glasses for Non-contact Facial Expression Monitoring
Hu, Y.; Nie, J.; Wang, Y.; Xia, S.; Jiang, X · 2020
Later among the works it cites.
Multi-Layered Deep Learning Features Fusion for Human Action Recognition
Kiran, S.; Khan, M.A.; Javed, M.Y.; Alhaisoni, M.; Tariq, U.; Nam, Y.; Damasevicius, R.; Sharif, M · 2021
Closest in time.
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges, and Opportunities
Chen, K.; Zhang, D.; Yao, L.; Guo, B.; Yu, Z.; Liu, Y · 2021
Closest in time.
Human activity recognition with smartphone and wearable sensors using deep learning techniques: A review
Ramanujam, E.; Perumal, T.; Padmavathi, S · 2021
Closest in time.
Listen2Cough: Leveraging End-to-End Deep Learning Cough Detection Model to Enhance Lung Health Assessment Using Passively Sensed Audio
Xu, X.; Nemati, E.; Vatanparvar, K.; Nathan, V.; Ahmed, T.; Rahman, M.M.; McCaffrey, D.; Kuang, J.; Gao, J.A · 2021
Closest in time.
A Novel Multi-Centroid Template Matching Algorithm and Its Application to Cough Detection
Zhang, S.; Nemati, E.; Ahmed, T.; Rahman, M.M.; Kuang, J.; Gao, A · 2021
Closest in time.
CoughBuddy: Multi-Modal Cough Event Detection Using Earbuds Platform
Nemati, E.; Zhang, S.; Ahmed, T.; Rahman, M.M.; Kuang, J.; Gao, A · 2021
Closest in time.
Zhang, S.; Nemati, E.; Dinh, M.; Folkman, N.; Ahmed, T.; Rahman, M.; Kuang, J.; Alshurafa, N.; Gao, A · 2021
Closest in time.
NeckFace: Continuously Tracking Full Facial Expressions on Neck-Mounted Wearables
Chen, T.; Li, Y.; Tao, S.; Lim, H.; Sakashita, M.; Zhang, R.; Guimbretiere, F.; Zhang, C · 2021
Closest in time.
SonicASL: An Acoustic-based Sign Language Gesture Recognizer Using Earphones
Jin, Y.; Gao, Y.; Zhu, Y.; Wang, W.; Li, J.; Choi, S.; Li, Z.; Chauhan, J.; Dey, A.K.; Jin, Z · 2021
Closest in time.
Computer vision techniques in construction: A critical review
Xu, S.; Wang, J.; Shou, W.; Ngo, T.; Sadick, A.M.; Wang, X · 2021
Closest in time.
Atypical sample regularizer autoencoder for cross-domain human activity recognition
Prabono, A.G.; Yahya, B.N.; Lee, S.L · 2021
Closest in time.
An ensemble of autonomous auto-encoders for human activity recognition
Garcia, K.D.; de Sá, C.R.; Poel, M.; Carvalho, T.; Mendes-Moreira, J.; Cardoso, J.M.; de Carvalho, A.C.; Kok, J.N · 2021
Closest in time.
Human Activity Recognition based on Deep Belief Network Classifier and Combination of Local and Global Features
Mahmoodzadeh, A · 2021
Closest in time.
Shallow Convolutional Neural Networks for Human Activity Recognition Using Wearable Sensors
Huang, W.; Zhang, L.; Gao, W.; Min, F.; He, J · 2021
Closest in time.
Deep neural networks for sensor-based human activity recognition using selective kernel convolution
Gao, W.; Zhang, L.; Huang, W.; Min, F.; He, J.; Song, A · 2021
Closest in time.
A time-efficient convolutional neural network model in human activity recognition
Gholamrezaii, M.; AlModarresi, S.M.T · 2021
Closest in time.
Human Activity Recognition and Embedded Application Based on Convolutional Neural Network
Xu, Y.; Qiu, T.T · 2021
Closest in time.
DanHAR: Dual Attention Network for multimodal human activity recognition using wearable sensors
Gao, W.; Zhang, L.; Teng, Q.; He, J.; Wu, H · 2021
Closest in time.
Recurrent Neural Network for Human Activity Recognition in Embedded Systems Using PPG and Accelerometer Data
Alessandrini, M.; Biagetti, G.; Crippa, P.; Falaschetti, L.; Turchetti, C · 2021
Closest in time.
Deep Convolutional Bidirectional LSTM for Complex Activity Recognition with Missing Data. In Human Activity Recognition Challenge
Saha, S.S.; Sandha, S.S.; Srivastava, M · 2021
Closest in time.
Placement Effect of Motion Sensors for Human Activity Recognition using LSTM Network
Mekruksavanich, S.; Jitpattanakul, A.; Thongkum, P · 2021
Closest in time.
A GAN-based data augmentation method for human activity recognition via the caching ability
Shi, J.; Zuo, D.; Zhang, Z · 2021
Closest in time.
Cross-subject transfer learning in human activity recognition systems using generative adversarial networks
Soleimani, E.; Nazerfard, E · 2021
Closest in time.
Guided-GAN: Adversarial Representation Learning for Activity Recognition with Wearables. arXiv 2021
Abedin, A.; Rezatofighi, H.; Ranasinghe, D.C · 2021
Closest in time.
ContrasGAN: Unsupervised domain adaptation in Human Activity Recognition via adversarial and contrastive learning
Sanabria, A.R.; Zambonelli, F.; Dobson, S.; Ye, J · 2021
Closest in time.
A multibranch CNN-BiLSTM model for human activity recognition using wearable sensor data
Challa, S.K.; Kumar, A.; Semwal, V.B · 2021
Closest in time.
Multi-input CNN-GRU based human activity recognition using wearable sensors
Dua, N.; Singh, S.N.; Semwal, V.B · 2021
Closest in time.
Voice In Ear: Spoofing-Resistant and Passphrase-Independent Body Sound Authentication
Gao, Y.; Jin, Y.; Chauhan, J.; Choi, S.; Li, J.; Jin, Z · 2021
Closest in time.
When Video Meets Inertial Sensors: Zero-Shot Domain Adaptation for Finger Motion Analytics with Inertial Sensors
Liu, Y.; Zhang, S.; Gowda, M · 2021
Closest in time.
A survey on security and privacy of federated learning
Mothukuri, V.; Parizi, R.M.; Pouriyeh, S.; Huang, Y.; Dehghantanha, A.; Srivastava, G · 2021
Closest in time.
A review of privacy-preserving federated learning for the Internet-of-Things
Briggs, C.; Fan, Z.; Andras, P · 2021
Closest in time.
Meta-HAR: Federated Representation Learning for Human Activity Recognition
Li, C.; Niu, D.; Jiang, B.; Zuo, X.; Yang, J · 2021
Closest in time.
A federated learning system with enhanced feature extraction for human activity recognition
Xiao, Z.; Xu, X.; Xing, H.; Song, F.; Wang, X.; Zhao, B · 2021
Closest in time.
FedDL: Federated Learning via Dynamic Layer Sharing for Human Activity Recognition
Tu, L.; Ouyang, X.; Zhou, J.; He, Y.; Xing, G · 2021
Closest in time.
Personalized Semi-Supervised Federated Learning for Human Activity Recognition
Bettini, C.; Civitarese, G.; Presotto, R · 2021
Closest in time.
Resource-constrained federated learning with heterogeneous labels and models for human activity recognition
Gudur, G.K.; Perepu, S.K · 2021
Closest in time.
On blockchain integration into mobile crowdsensing via smart embedded devices: A comprehensive survey
Chen, Z.; Fiandrino, C.; Kantarci, B · 2021
Closest in time.
Federated learning meets blockchain in edge computing: Opportunities and challenges
Nguyen, D.C.; Ding, M.; Pham, Q.V.; Pathirana, P.N.; Le, L.B.; Seneviratne, A.; Li, J.; Niyato, D.; Poor, H.V · 2021
Closest in time.
Rizve, M.N.; Duarte, K.; Rawat, Y.S.; Shah, M · 2021
Closest in time.
Weakly-supervised sensor-based activity segmentation and recognition via learning from distributions
Qian, H.; Pan, S.J.; Miao, C · 2021
Closest in time.
Latent Independent Excitation for Generalizable Sensor-based Cross-Person Activity Recognition
Qian, H.; Pan, S.J.; Miao, C.; Qian, H.; Pan, S.; Miao, C · 2021
Closest in time.
Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges
Qiu, S.; Zhao, H.; Jiang, N.; Wang, Z.; Liu, L.; An, Y.; Zhao, H.; Miao, X.; Liu, R.; Fortino, G · 2021
Closest in time.
Sensor-based human activity recognition: Challenges ahead. In IoT Sensor-Based Activity Recognition
Ahad, M.A.R.; Antar, A.D.; Ahmed, M · 2021
Closest in time.
Attend and Discriminate: Beyond the State-of-the-Art for Human Activity Recognition Using Wearable Sensors
Abedin, A.; Ehsanpour, M.; Shi, Q.; Rezatofighi, H.; Ranasinghe, D.C · 2021
Closest in time.
Human activity recognition based on smartphone and wearable sensors using multiscale DCNN ensemble
Sena, J.; Barreto, J.; Caetano, C.; Cramer, G.; Schwartz, W.R · 2021
Closest in time.
A Drone-Based System for Intelligent and Autonomous Homes
Xia, S.; Chandrasekaran, R.; Liu, Y.; Yang, C.; Rosing, T.S.; Jiang, X · 2021
Closest in time.
CSafe: An Intelligent Audio Wearable Platform for Improving Construction Worker Safety in Urban Environments
Xia, S.; Nie, J.; Jiang, X · 2021
Closest in time.
SPIDERS+: A light-weight, wireless, and low-cost glasses-based wearable platform for emotion sensing and bio-signal acquisition
Nie, J.; Liu, Y.; Hu, Y.; Wang, Y.; Xia, S.; Preindl, M.; Jiang, X · 2021
Closest in time.
About One-in-five Americans Use a Smart Watch or Fitness Tracker
Vogels, E.A · 2022
Closest in time.
Wearable Devices Market by Product Type (Smartwatch, Earwear, Eyewear, and others), End-Use Industry (Consumer Electronics, Healthcare, Enterprise and Industrial, Media and Entertainment), Connectivity Medium, and Region—Global Forecast to 2025
Research, M · 2022
Closest in time.
Available online: https://data.world/crowdflower/wearable-technology-database
Wearable Technology Database · 2022
Closest in time.
Deep Learning. 2016
Goodfellow, I.; Bengio, Y.; Courville, A · 2022
Closest in time.
Reinforcement Learning: An Introduction. 2018.Available online: http://www.incompleteideas.net/book/the-book-2nd.html (accessed on 10 Feb 2022)
Sutton, R.S.; Barto, A.G · 2022
Closest in time.
Available online: http://www.prisma-statement.org/
Transparent Reporting of Systematic Reviews and Meta-Analyses · 2022
Closest in time.
The UCR Time Series Classification Archive. 2015. Available online: www.cs.ucr.edu/~eamonn/time_series_data/ (accessed on 10 Feb 2022)
Chen, Y.; Keogh, E.; Hu, B.; Begum, N.; Bagnall, A.; Mueen, A.; Batista, G · 2022
Closest in time.
Gradient Flow in Recurrent Nets: The Difficulty of Learning Long-Term D Ependencies* Sepp Hochreiter Fakult at f ur Informatik. Available online: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.24.7321&rep=rep1&type=pdf (accessed on 11 Feb 2022)
Bengio, Y.; Frasconi, P.; urgen Schmidhuber, J.; Elvezia, C · 2022
Closest in time.
Smart Devices Based Multisensory Approach for Complex Human Activity Recognition
Hanif, M.; Akram, T.; Shahzad, A.; Khan, M.; Tariq, U.; Choi, J.; Nam, Y.; Zulfiqar, Z · 2022
Closest in time.