Abstract Automatic hand posture detection of smartphone users is important for adaptive user interface design, context aware application development, and activity analysis. This paper presents a method for hand posture and phone placement detection from data produced by accelerometer, magnetometer and gyroscope of a smartphone using LSTM networks. Real-time testing results indicated that LSTM network is effective in hand posture and phone placement prediction, and the proposed method outperformed existing methods by significant margins.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Hand Posture Detection of Smartphone Users Using LSTM Networks


    Contributors:


    Publication date :

    2019-01-01


    Size :

    7 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Hand Posture Detection of Smartphone Users Using LSTM Networks

    Tan, Song Lim / Ng, Hui Fuang / Ooi, Boon Yaik et al. | TIBKAT | 2019


    Direction Detection of Users Independent of Smartphone Orientations

    Kusber, Rico / Memon, Abdul Qudoos / Kroll, Dennis et al. | IEEE | 2015


    A Posture Features Based Pedestrian Trajectory Prediction with LSTM

    Kao, I-Hsi / Zhou, Xiao / Chen, I-Ming et al. | IEEE | 2021


    Smartphone Heading Correction Method Based on LSTM Neural Network

    Huang, Yan / Zeng, Qinghua / Lei, Qiyao et al. | Springer Verlag | 2022


    Smartphone Heading Correction Method Based on LSTM Neural Network

    Huang, Yan / Zeng, Qinghua / Lei, Qiyao et al. | TIBKAT | 2022