Deep Learning Enabled Perceptive Wearable Sensor: An Interactive Gadget for Tracking Movement Disorder
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[1] Arpitam Chatterjee,et al. Deep Learning Enabled Early Predicting Cardiovascular Status Using Highly Sensitive Piezoelectric Sensor of Solution‐Processable Nylon‐11 , 2023, Advanced Materials Technologies.
[2] Yanchao Mao,et al. Deep‐Learning‐Assisted Noncontact Gesture‐Recognition System for Touchless Human‐Machine Interfaces , 2022, Advanced Functional Materials.
[3] A. Mannodi-Kanakkithodi,et al. High-throughput computations and machine learning for halide perovskite discovery , 2022, MRS Bulletin.
[4] Vasileios Moysiadis,et al. An Integrated Real-Time Hand Gesture Recognition Framework for Human–Robot Interaction in Agriculture , 2022, Applied Sciences.
[5] Xiuhan Li,et al. Self-Powered Tactile Sensor for Gesture Recognition Using Deep Learning Algorithms. , 2022, ACS applied materials & interfaces.
[6] Nityananda Das,et al. Surface Potential Tuned Single Active Material Comprised Triboelectric Nanogenerator for a High Performance Voice Recognition Sensor. , 2022, Small.
[7] Puchuan Tan,et al. Self‐Powered Gesture Recognition Wristband Enabled by Machine Learning for Full Keyboard and Multicommand Input , 2022, Advanced materials.
[8] S. Rana,et al. Siloxene/PVDF Composite Nanofibrous Membrane for High‐Performance Triboelectric Nanogenerator and Self‐Powered Static and Dynamic Pressure Sensing Applications , 2022, Advanced Functional Materials.
[9] W. Li,et al. Active‐Matrix Sensing Array Assisted with Machine‐Learning Approach for Lumbar Degenerative Disease Diagnosis and Postoperative Assessment , 2022, Advanced Functional Materials.
[10] Haiwu Zheng,et al. Intelligent Sound Monitoring and Identification System Combining Triboelectric Nanogenerator‐Based Self‐Powered Sensor with Deep Learning Technique , 2022, Advanced Functional Materials.
[11] Zeyuan Cao,et al. Theoretical Study on the Output of Contact-Separation Triboelectric Nanogenerators with Arbitrary Charging and Grounding Conditions , 2021, Nano Energy.
[12] M. S. Mahbub,et al. Plastic pollution during COVID-19: Plastic waste directives and its long-term impact on the environment , 2021, Environmental Advances.
[13] Zihan Wang,et al. TriboGait: A deep learning enabled triboelectric gait sensor system for human activity recognition and individual identification , 2021, UbiComp/ISWC Adjunct.
[14] T. Hsiai,et al. Ambulatory Cardiovascular Monitoring Via a Machine‐Learning‐Assisted Textile Triboelectric Sensor , 2021, Advanced materials.
[15] Toluwanimi Oluwadara Akinyemi,et al. A Hybrid Microstructure Piezoresistive Sensor with Machine Learning Approach for Gesture Recognition , 2021, Applied Sciences.
[16] Yuanjie Su,et al. Self‐Powered Respiration Monitoring Enabled By a Triboelectric Nanogenerator , 2021, Advanced materials.
[17] Xiaodong Chen,et al. Fusing Stretchable Sensing Technology with Machine Learning for Human–Machine Interfaces , 2021, Advanced Functional Materials.
[18] Jun Chen,et al. Textile Triboelectric Nanogenerators for Wearable Pulse Wave Monitoring. , 2021, Trends in biotechnology.
[19] Chenguo Hu,et al. Wearable triboelectric sensors for biomedical monitoring and human-machine interface , 2021, iScience.
[20] J. Rabaey,et al. A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition , 2020, Nature Electronics.
[21] Chengkuo Lee,et al. Machine Learning Glove Using Self‐Powered Conductive Superhydrophobic Triboelectric Textile for Gesture Recognition in VR/AR Applications , 2020, Advanced science.
[22] W. Kupolati,et al. Challenges of plastic waste generation and management in sub-Saharan Africa: A review. , 2020, Waste management.
[23] Xiaoyi Li,et al. On the Maximal Output Energy Density of Nanogenerators. , 2019, ACS nano.
[24] Herbert Kimura,et al. Literature review: Machine learning techniques applied to financial market prediction , 2019, Expert Syst. Appl..
[25] Xiaohui Peng,et al. Deep Learning for Sensor-based Activity Recognition: A Survey , 2017, Pattern Recognit. Lett..
[26] Kaushik Parida,et al. Skin-touch-actuated textile-based triboelectric nanogenerator with black phosphorus for durable biomechanical energy harvesting , 2018, Nature Communications.
[27] Meng Wang,et al. Air-Flow-Driven Triboelectric Nanogenerators for Self-Powered Real-Time Respiratory Monitoring. , 2018, ACS nano.
[28] Jingquan Liu,et al. Flexible Single-Electrode Triboelectric Nanogenerator and Body Moving Sensor Based on Porous Na2CO3/Polydimethylsiloxane Film. , 2018, ACS applied materials & interfaces.
[29] Dipankar Mandal,et al. Sustainable Energy Generation from Piezoelectric Biomaterial for Noninvasive Physiological Signal Monitoring , 2017 .
[30] C. Menchaca-Campos,et al. Nylon/Porphyrin/Graphene Oxide Fiber Ternary Composite, Synthesis and Characterization , 2017 .
[31] Zhong‐Lin Wang,et al. Single‐Thread‐Based Wearable and Highly Stretchable Triboelectric Nanogenerators and Their Applications in Cloth‐Based Self‐Powered Human‐Interactive and Biomedical Sensing , 2017 .
[32] H. Tse,et al. Plastic waste in the marine environment: A review of sources, occurrence and effects. , 2016, The Science of the total environment.
[33] J. Jung,et al. Enhanced triboelectrification of the polydimethylsiloxane surface by ultraviolet irradiation , 2016 .
[34] Michael I. Jordan,et al. Machine learning: Trends, perspectives, and prospects , 2015, Science.
[35] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[36] Zhong Lin Wang,et al. Theoretical study of contact-mode triboelectric nanogenerators as an effective power source , 2013 .
[37] B. Grzybowski,et al. The Mosaic of Surface Charge in Contact Electrification , 2011, Science.
[38] J Maxwell Donelan,et al. Dynamic Principles of Gait and Their Clinical Implications , 2010, Physical Therapy.
[39] Arthur D Kuo,et al. The six determinants of gait and the inverted pendulum analogy: A dynamic walking perspective. , 2007, Human movement science.
[40] W. Nichols. Clinical measurement of arterial stiffness obtained from noninvasive pressure waveforms. , 2005, American journal of hypertension.
[41] Arthur L. Samuel,et al. Some Studies in Machine Learning Using the Game of Checkers , 1967, IBM J. Res. Dev..
[42] H RAHN,et al. Mechanics of breathing in man. , 1950, Journal of applied physiology.