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PCA & HMM Based Arm Gesture Recognition Using Inertial Measurement Unit
Zhang YL(张吟龙); Liang W(梁炜); Tan JD(谈金东); Li Y(李杨); Zeng ZM(曾子铭)
作者部门工业控制网络与系统研究室
会议名称8th International Conference on Body Area Networks
会议日期September 30 - October 2, 2013
会议地点Boston, MA, USA
会议录名称Proceedings of the 8th International Conference on Body Area Networks
出版者ACM
出版地Brussels, Belgium
2013
页码193-196
收录类别EI
EI收录号20144900293421
产权排序1
ISBN号978-1-936968-89-3
关键词Principal Component Analysis Hidden Markov Model Arm Gesture Recognition Inertial Measurement Unit
摘要This paper presents a novel arm gesture recognition approach that is capable of recognizing seven commonly used sequential arm gestures based upon the outputs from Inertial Measurement Unit (IMU) integrated with 3-D accelerometer and 3-D gyroscope. Unlike the traditional gesture recognition methods where the states in the gesture sequence are irrelevant, our proposed recognition system is intentionally designed to recognize the meaningful gesture sequence where each gesture state relates to the contiguous states which is applicable in the specific occasions such as the police directing the traffic and the arm-injured patients performing a set of arm gestures for effective rehabilitation. In the proposed arm gesture recognition system, the waveforms of the inertial outputs, i.e., 3-D accelerations and 3-D angular rates are automatically segmented for each arm gesture trace at first. Then we employ the Principal Component Analysis (PCA) - a computationally efficient feature selection method characteristic of compressing the inertial data and minimizing the influences of gesture variations. These selected features from PCA are compared with those standard features stored in pattern templates to acquire the gesture observation sequence that satisfy the Markov property. Finally, the Hidden Markov Model is applied in deducing the most likely arm gesture sequence. The experimental results show that our arm gesture classifier achieves up to 93% accuracy. By comparing with the other published recognition methods, our approach verifies the robustness and feasibility in arm gesture recognition using wearable MEMS sensors.
语种英语
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/14581
专题工业控制网络与系统研究室
推荐引用方式
GB/T 7714
Zhang YL,Liang W,Tan JD,et al. PCA & HMM Based Arm Gesture Recognition Using Inertial Measurement Unit[C]. Brussels, Belgium:ACM,2013:193-196.
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