TY - GEN
T1 - Implementation of a batch normalized deep LSTM recurrent network on a smartphone for human activity recognition
AU - Zebin, Tahmina
AU - Balaban, Ertan
AU - Ozanyan, Krikor B.
AU - Casson, Alexander J.
AU - Peek, Niels
N1 - Funding Information:
This work was supported by the UK Engineering and Physical Sciences Research Council grant number EP/P010148/1 and EP/P02713X/1.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - In this paper we present a Long-Short Term Memory (LSTM) deep recurrent neural network (RNN) model for the classification of human daily life activities by using the accelerometer and gyroscope data of a smartphone. The proposed model was trained by using the open-source TensorFlow library, optimised and deployed on an Android smartphone. Hardware resource requirements for the implementation are empirically investigated and the effect of data quantization on the accuracy of the implementation is discussed. In addition, we profile the power budget for running the proposed model on smartphone. Results of this work will be of use for deep learning implemented on edge computing devices, which leverages the user privacy as the raw data never leaves the person.
AB - In this paper we present a Long-Short Term Memory (LSTM) deep recurrent neural network (RNN) model for the classification of human daily life activities by using the accelerometer and gyroscope data of a smartphone. The proposed model was trained by using the open-source TensorFlow library, optimised and deployed on an Android smartphone. Hardware resource requirements for the implementation are empirically investigated and the effect of data quantization on the accuracy of the implementation is discussed. In addition, we profile the power budget for running the proposed model on smartphone. Results of this work will be of use for deep learning implemented on edge computing devices, which leverages the user privacy as the raw data never leaves the person.
UR - http://www.scopus.com/inward/record.url?scp=85073016763&partnerID=8YFLogxK
U2 - 10.1109/BHI.2019.8834480
DO - 10.1109/BHI.2019.8834480
M3 - Conference contribution
AN - SCOPUS:85073016763
T3 - 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings
BT - 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019 - Proceedings
PB - The Institute of Electrical and Electronics Engineers (IEEE)
T2 - 2019 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2019
Y2 - 19 May 2019 through 22 May 2019
ER -