TransNet: Minimally Supervised Deep Transfer Learning for Dynamic Adaptation of Wearable Systems

ing with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2020 Association for Computing Machinery. 1084-4309/2020/09-ART5 $15.00 https://doi.org/10.1145/3414062 ACM Transactions on Design Automation of Electronic Systems, Vol. 26, No. 1, Article 5. Pub. date: September 2020. 5:2 S. A. Rokni et al.

[1]  Paul Lukowicz,et al.  Dealing with sensor displacement in motion-based onbody activity recognition systems , 2008, UbiComp.

[2]  Assefaw Hadish Gebremedhin,et al.  A closed-loop deep learning architecture for robust activity recognition using wearable sensors , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[3]  Razvan Pascanu,et al.  Overcoming catastrophic forgetting in neural networks , 2016, Proceedings of the National Academy of Sciences.

[4]  Thomas Plötz,et al.  Deep, Convolutional, and Recurrent Models for Human Activity Recognition Using Wearables , 2016, IJCAI.

[5]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[6]  Andrey Ignatov,et al.  Real-time human activity recognition from accelerometer data using Convolutional Neural Networks , 2018, Appl. Soft Comput..

[7]  Billur Barshan,et al.  Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units , 2014, Comput. J..

[8]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[9]  Razvan Pascanu,et al.  Deep Learners Benefit More from Out-of-Distribution Examples , 2011, AISTATS.

[10]  Hassan Ghasemzadeh,et al.  Synchronous Dynamic View Learning: A Framework for Autonomous Training of Activity Recognition Models Using Wearable Sensors , 2017, 2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[11]  Frank Hutter,et al.  Neural Architecture Search: A Survey , 2018, J. Mach. Learn. Res..

[12]  ChengXiang Zhai,et al.  Active feedback in ad hoc information retrieval , 2005, SIGIR '05.

[13]  Geoffrey E. Hinton,et al.  Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[14]  Hassan Ghasemzadeh,et al.  A Reliable and Reconfigurable Signal Processing Framework for Estimation of Metabolic Equivalent of Task in Wearable Sensors , 2016, IEEE Journal of Selected Topics in Signal Processing.

[15]  Gary M. Weiss,et al.  Activity recognition using cell phone accelerometers , 2011, SKDD.

[16]  L. Benini,et al.  Activity recognition from on-body sensors by classifier fusion: sensor scalability and robustness , 2007, 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information.

[17]  Hassan Ghasemzadeh,et al.  Mindful Active Learning , 2019, IJCAI.

[18]  Philip S. Yu,et al.  Deep Learning towards Mobile Applications , 2018, 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS).

[19]  Hassan Ghasemzadeh,et al.  Structural Action Recognition in Body Sensor Networks: Distributed Classification Based on String Matching , 2010, IEEE Transactions on Information Technology in Biomedicine.

[20]  I. Budin-Ljøsne,et al.  Patient and interest organizations’ views on personalized medicine: a qualitative study , 2016, BMC Medical Ethics.

[21]  Leonidas J. Guibas,et al.  Deep Knowledge Tracing , 2015, NIPS.

[22]  Hassan Ghasemzadeh,et al.  Patient-centric on-body sensor localization in smart health systems , 2014, 2014 48th Asilomar Conference on Signals, Systems and Computers.

[23]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[24]  Özlem Durmaz Incel,et al.  User, device and orientation independent human activity recognition on mobile phones: challenges and a proposal , 2013, UbiComp.

[25]  Hassan Ghasemzadeh,et al.  Personalization without User Interruption: Boosting Activity Recognition in New Subjects Using Unlabeled Data , 2017, 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems (ICCPS).

[26]  Yi Zhang,et al.  Incorporating Diversity and Density in Active Learning for Relevance Feedback , 2007, ECIR.

[27]  Sung-Bae Cho,et al.  Human activity recognition with smartphone sensors using deep learning neural networks , 2016, Expert Syst. Appl..

[28]  Y. T. Zhou,et al.  Computation of optical flow using a neural network , 1988, IEEE 1988 International Conference on Neural Networks.

[29]  Daniel Roggen,et al.  Automatic Transfer of Activity Recognition Capabilities between Body-Worn Motion Sensors: Training Newcomers to Recognize Locomotion , 2011 .

[30]  Hwee Pink Tan,et al.  Deep Activity Recognition Models with Triaxial Accelerometers , 2015, AAAI Workshop: Artificial Intelligence Applied to Assistive Technologies and Smart Environments.

[31]  Xiang Zhang,et al.  OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.

[32]  Reza Lotfian,et al.  Impact of sensor misplacement on dynamic time warping based human activity recognition using wearable computers , 2012, Wireless Health.

[33]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[34]  Cesare Furlanello,et al.  Convolutional Neural Network for Stereotypical Motor Movement Detection in Autism , 2015, ArXiv.

[35]  Rogério Schmidt Feris,et al.  SpotTune: Transfer Learning Through Adaptive Fine-Tuning , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  Paul Lukowicz,et al.  Collecting complex activity datasets in highly rich networked sensor environments , 2010, 2010 Seventh International Conference on Networked Sensing Systems (INSS).

[37]  Daniel Roggen,et al.  Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition , 2016, Sensors.

[38]  Bernt Schiele,et al.  Activity Recognition from Sparsely Labeled Data Using Multi-Instance Learning , 2009, LoCA.

[39]  Bo Yu,et al.  Convolutional Neural Networks for human activity recognition using mobile sensors , 2014, 6th International Conference on Mobile Computing, Applications and Services.

[40]  Quoc V. Le,et al.  Neural Architecture Search with Reinforcement Learning , 2016, ICLR.

[41]  Ricardo Chavarriaga,et al.  Robust activity recognition combining anomaly detection and classifier retraining , 2013, 2013 IEEE International Conference on Body Sensor Networks.

[42]  Baoding Zhou,et al.  Smartphone-Based Activity Recognition for Indoor Localization Using a Convolutional Neural Network , 2019, Sensors.

[43]  Stefan Wermter,et al.  Continual Lifelong Learning with Neural Networks: A Review , 2018, Neural Networks.

[44]  Silvio Savarese,et al.  Active Learning for Convolutional Neural Networks: A Core-Set Approach , 2017, ICLR.

[45]  Hassan Ghasemzadeh,et al.  Cost-sensitive feature selection for on-body sensor localization , 2014, UbiComp Adjunct.

[46]  Hassan Ghasemzadeh,et al.  Dropout as an Implicit Gating Mechanism For Continual Learning , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[47]  M. Schwab,et al.  Data collection as a barrier to personalized medicine. , 2015, Trends in pharmacological sciences.

[48]  Pengfei Zhang,et al.  High Performance Depthwise and Pointwise Convolutions on Mobile Devices , 2020, AAAI.

[49]  Robert D. Nowak,et al.  Graph-based active learning: A new look at expected error minimization , 2016, 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

[50]  Thomas G. Dietterich Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.

[51]  Mikkel Baun Kjærgaard,et al.  Smart Devices are Different: Assessing and MitigatingMobile Sensing Heterogeneities for Activity Recognition , 2015, SenSys.

[52]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[53]  Wei Niu,et al.  PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-time Execution on Mobile Devices , 2020, AAAI.

[54]  Adrian Burns,et al.  SHIMMER™ – A Wireless Sensor Platform for Noninvasive Biomedical Research , 2010, IEEE Sensors Journal.

[55]  Rong Jin,et al.  Active Learning by Querying Informative and Representative Examples , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[56]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[57]  Daniel Roggen,et al.  Deep convolutional feature transfer across mobile activity recognition domains, sensor modalities and locations , 2016, SEMWEB.

[58]  Rong Jin,et al.  Batch mode active learning and its application to medical image classification , 2006, ICML.

[59]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[60]  Ren Ohmura,et al.  Improving fault tolerance of wearable wearable sensor-based activity recognition techniques , 2013, UbiComp.

[61]  Y. Aloimonos,et al.  Discovering a Language for Human Activity 1 , 2005 .

[62]  Yoshua Bengio,et al.  Unsupervised and Transfer Learning Challenge: a Deep Learning Approach , 2011, ICML Unsupervised and Transfer Learning.

[63]  Yunbin Deng,et al.  Deep learning on mobile devices: a review , 2019, Defense + Commercial Sensing.

[64]  Sivan Sabato,et al.  Interactive Algorithms: from Pool to Stream , 2016, COLT.

[65]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[66]  Trevor Darrell,et al.  DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.

[67]  Billur Barshan,et al.  Comparative study on classifying human activities with miniature inertial and magnetic sensors , 2010, Pattern Recognit..

[68]  Stefan Wermter,et al.  Continual Lifelong Learning with Neural Networks: A Review , 2019, Neural Networks.

[69]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[70]  Hassan Ghasemzadeh,et al.  ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition , 2020, ArXiv.

[71]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[72]  Lin Yang,et al.  2-bit Model Compression of Deep Convolutional Neural Network on ASIC Engine for Image Retrieval , 2019, ArXiv.

[73]  David Meyre,et al.  From big data analysis to personalized medicine for all: challenges and opportunities , 2015, BMC Medical Genomics.

[74]  Sophia Bano,et al.  Deep human activity recognition using wearable sensors , 2019, PETRA.

[75]  Kazuya Murao,et al.  A Context-Aware System that Changes Sensor Combinations Considering Energy Consumption , 2008, Pervasive.

[76]  Jason Weston,et al.  A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.

[77]  Niall Twomey,et al.  Active transfer learning for activity recognition , 2016, ESANN.

[78]  Jason Weston,et al.  Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..

[79]  Hassan Ghasemzadeh,et al.  Toward seamless wearable sensing: Automatic on-body sensor localization for physical activity monitoring , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[80]  Yuhong Li,et al.  A Bi-Directional Co-Design Approach to Enable Deep Learning on IoT Devices , 2019, ArXiv.

[81]  Hassan Ghasemzadeh,et al.  LabelMerger: Learning Activities in Uncontrolled Environments , 2019, 2019 First International Conference on ​Transdisciplinary AI (TransAI).

[82]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[83]  Yoshua Bengio,et al.  Three Factors Influencing Minima in SGD , 2017, ArXiv.

[84]  Hassan Ghasemzadeh,et al.  Proximity-Based Active Learning on Streaming Data: A Personalized Eating Moment Recognition , 2020, ArXiv.