Real-time HR Estimation from wrist PPG using Binary LSTMs
暂无分享,去创建一个
Sergio Bampi | Chris Van Hoof | Nick Van Helleputte | Chris H. Kim | Dwaipayan Biswas | Bram-Ernst Verhoef | Marian Verhelst | Muqing Liu | Leandro Mateus Giacomini Rocha | Mario Konijnenburg
[1] Eugenio Culurciello,et al. Hardware accelerators for recurrent neural networks on FPGA , 2017, 2017 IEEE International Symposium on Circuits and Systems (ISCAS).
[2] Colin Raffel,et al. Lasagne: First release. , 2015 .
[3] Zhilin Zhang,et al. TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise , 2014, IEEE Transactions on Biomedical Engineering.
[4] John Salvatier,et al. Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.
[5] James T. Kwok,et al. Loss-aware Binarization of Deep Networks , 2016, ICLR.
[6] Yu Wang,et al. Software-Hardware Codesign for Efficient Neural Network Acceleration , 2017, IEEE Micro.
[7] Mario Konijnenburg,et al. CorNET: Deep Learning Framework for PPG-Based Heart Rate Estimation and Biometric Identification in Ambulant Environment , 2019, IEEE Transactions on Biomedical Circuits and Systems.
[8] Yoshua Bengio,et al. BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 , 2016, ArXiv.
[9] Zhongfeng Wang,et al. An Energy-Efficient Architecture for Binary Weight Convolutional Neural Networks , 2018, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.
[10] Katsuyuki Miyasaka,et al. Pulse oximetry: its invention, contribution to medicine, and future tasks. , 2002, Anesthesia and analgesia.
[11] Luca Benini,et al. YodaNN: An Ultra-Low Power Convolutional Neural Network Accelerator Based on Binary Weights , 2016, 2016 IEEE Computer Society Annual Symposium on VLSI (ISVLSI).