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题名:PCA & HMM Based Arm Gesture Recognition Using Inertial Measurement Unit
作者: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:Proceedings of the 8th International Conference on Body Area Networks
会议录出版者:ACM
会议录出版地:Brussels, Belgium
出版日期: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
内容类型:会议论文
URI标识:http://ir.sia.cn/handle/173321/14581
专题:工业控制网络与系统研究室_会议论文
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PCA & HMM Based Arm Gesture Recognition Using Inertial Measurement Unit.pdf（407KB）会议论文--开放获取CC BY-NC-SA
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推荐引用方式：
Zhang YL,Liang W,Tan JD,et al. PCA & HMM Based Arm Gesture Recognition Using Inertial Measurement Unit[C]. 8th International Conference on Body Area Networks. Boston, MA, USA. September 30 - October 2, 2013.PCA & HMM Based Arm
Gesture Recognition Using Inertial Measurement Unit.
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