8th International Conference on Body Area Networks
會(huì)議日期:
September 30 - October 2, 2013
會(huì)議地點(diǎn):
Boston, MA, USA
會(huì)議錄:
Proceedings of the 8th International Conference on Body Area Networks
會(huì)議錄出版者:
ACM
會(huì)議錄出版地:
Brussels, Belgium
出版日期:
2013
頁碼:
193-196
收錄類別:
EI
EI收錄號(hào):
20144900293421
產(chǎn)權(quán)排序:
1
ISBN號(hào):
978-1-936968-89-3
關(guān)鍵詞:
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.
語種:
英語
內(nèi)容類型:
會(huì)議論文
URI標(biāo)識(shí):
http://ir.sia.cn/handle/173321/14581
專題:
工業(yè)控制網(wǎng)絡(luò)與系統(tǒng)研究室_會(huì)議論文
條目包含的文件
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文件名稱/大小 內(nèi)容類型 版本類型 開放類型 使用許可 ?
PCA & HMM Based Arm Gesture Recognition Using Inertial Measurement Unit.pdf（407KB）
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CC BY-NC-SA
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推薦引用方式：
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.
