SIA OpenIR  > 工业控制网络与系统研究室
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)
Department工业控制网络与系统研究室
Conference Name8th International Conference on Body Area Networks
Conference DateSeptember 30 - October 2, 2013
Conference PlaceBoston, MA, USA
Source PublicationProceedings of the 8th International Conference on Body Area Networks
PublisherACM
Publication PlaceBrussels, Belgium
2013
Pages193-196
Indexed ByEI
EI Accession number20144900293421
Contribution Rank1
ISBN978-1-936968-89-3
KeywordPrincipal Component Analysis Hidden Markov Model Arm Gesture Recognition Inertial Measurement Unit
AbstractThis 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.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/14581
Collection工业控制网络与系统研究室
Recommended Citation
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.
Files in This Item: Download All
File Name/Size DocType Version Access License
PCA & HMM Based Arm (407KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhang YL(张吟龙)]'s Articles
[Liang W(梁炜)]'s Articles
[Tan JD(谈金东)]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhang YL(张吟龙)]'s Articles
[Liang W(梁炜)]'s Articles
[Tan JD(谈金东)]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhang YL(张吟龙)]'s Articles
[Liang W(梁炜)]'s Articles
[Tan JD(谈金东)]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: PCA & HMM Based Arm Gesture Recognition Using Inertial Measurement Unit.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.