Abstract
This paper investigates hand gesture recognition from acoustic measurements at wrist for the development of a low-cost wearable human-computer interaction (HCI) device. A prototype with 5 microphone sensors on human wrist is benchmarked in hand gesture recognition performance by identifying 36 gestures in American Sign Language (ASL). Three subjects were recruited to perform over 20 trials for each set of hand gestures, including 26 ASL alphabets and 10 ASL numbers. Ten features were extracted from the signal recorded by each sensor. Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbors (kNN), and Linear Discriminant Analysis (LDA) were compared in classification performance. Among which, LDA offered the highest average classification accuracy above 80%. Based on these preliminary results, our proposed technique has exhibited a promising means for developing a low-cost HCI.
| Original language | English |
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| Title of host publication | Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS) |
| Publisher | IEEE |
| Pages | 4443-4446 |
| ISBN (Print) | 9781509028092 |
| DOIs | |
| Publication status | Published - Jul 2017 |
| Event | 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - International Convention Center, Jeju Island, Korea, Republic of Duration: 11 Jul 2017 → 15 Jul 2017 https://embc.embs.org/2017/ https://embs.papercept.net/conferences/conferences/EMBC17/program/EMBC17_ContentListWeb_3.html |
Publication series
| Name | |
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| ISSN (Print) | 1557-170X |
Conference
| Conference | 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 |
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| Abbreviated title | EMBC' 17 |
| Place | Korea, Republic of |
| City | Jeju Island |
| Period | 11/07/17 → 15/07/17 |
| Internet address |