.png){kind=icon}
返回
deepGesture: Deep learning-based gesture recognition scheme using motion sensors
.png){kind=icon}
PRE
AI
DOI：10.1016/j.displa.2018.08.001.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recent advancement in smart phones and sensor technology has promoted research in gesture recognition. This has made designing of efficient gesture interface easy. However, human activity recognition (HAR) through gestures is not trivial since each person may pose the same gesture differently. In this paper, we propose deepGesture algorithm, a new arm gesture recognition method based on gyroscope and accelerometer sensors using deep convolution and recurrent neural networks. This method uses four deep convolution layers to automate feature learning in raw sensor data. The features of the convolution layers are used as input of the gated recurrent unit (GRU) which is based on the state-of-the-art recurrent neural network (RNN) structure to capture long-term dependency and model sequential data. The input data of the proposed algorithm is obtained through motion sequence data extracted using a wrist-type smart band device equipped with gyroscope and accelerometer sensors. The data is initially segmented in fixed length segments. The segmented data is labeled and we construct the database. Then the labeled data is used in our learning algorithm. To verify the applicability of the algorithm, several experiments have been performed to measure the accuracy of gesture classification. Compared to the human activity recognition method, our experimental results show that the proposed deepGesture algorithm can increase the average Fl-score for recognition of nine defined arm gestures by 6%.
Keyword:
Human activity recognition
Wearable sensors
Deep learning
GRU
Neural network
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.4
论文数:
2.3K
被引数:
3.2K
机构
引用论文
分享收藏Fusion of Inertial and Depth Sensor Data for Robust Hand Gesture Recognition融合惯性和深度传感器数据的鲁棒手势识别.png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏Min-max vs. max-min flow control algorithms for optimal computer network capacity assignment优化计算机网络容量分配的最小-最大vs.最大-最小流量控制算法
OAAI
OAAI分享收藏Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition用于多模式可穿戴活动识别的深度卷积和LSTM循环神经网络SENSORSOAAI
SENSORSIF3.5
OAAI分享收藏
查看更多内容