Name
Abstract | ||
|---|---|---|
Recent improvements in ultrasound imaging enable new opportunities for hand pose detection using wearable devices. Ultrasound imaging has remained under-explored in the HCI community despite being non-invasive, harmless and capable of imaging internal body parts, with applications including smart-watch interaction, prosthesis control and instrument tuition. In this paper, we compare the performance of different forearm mounting positions for a wearable ultrasonographic device. Location plays a fundamental role in ergonomics and performance since the anatomical features differ among positions. We also investigate the performance decrease due to cross-session position shifts and develop a technique to compensate for this misalignment. Our gesture recognition algorithm combines image processing and neural networks to classify the flexion and extension of 10 discrete hand gestures with an accuracy above 98%. Furthermore, this approach can continuously track individual digit flexion with less than 5% NRMSE, and also differentiate between digit flexion at different joints. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3025453.3025807 | CHI |
Keywords | Field | DocType |
Gesture recognition, interactive ultrasound imaging, machine learning, computer vision | Computer vision,Computer science,Wearable computer,Gesture,Numerical digit,Ultrasound imaging,Image processing,Gesture recognition,Artificial intelligence,Wearable technology,Artificial neural network | Conference |
Citations | PageRank | References |
10 | 0.63 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jess McIntosh | 1 | 48 | 6.56 |
| Asier Marzo | 2 | 53 | 12.59 |
| Mike Fraser | 3 | 957 | 102.89 |
| Carol Phillips | 4 | 10 | 0.63 |