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.
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Tan, S.L., Ng, H.F., Ooi, B.Y., Tan, H.K., Ang, J.L.F. (2019). Hand Posture Detection of Smartphone Users Using LSTM Networks. In: Zawawi, M., Teoh, S., Abdullah, N., Mohd Sazali, M. (eds) 10th International Conference on Robotics, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 547. Springer, Singapore. https://doi.org/10.1007/978-981-13-6447-1_3
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DOI: https://doi.org/10.1007/978-981-13-6447-1_3
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