ISWC '00 Proceedings of the 4th IEEE International Symposium on Wearable Computers
Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Using Ultrasonic Hand Tracking to Augment Motion Analysis Based Recognition of Manipulative Gestures
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
Gait analyzer based on a cell phone with a single three-axis accelerometer
Proceedings of the 8th conference on Human-computer interaction with mobile devices and services
Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Upper Body Postures using Textile Strain Sensors
ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
One-handed gesture recognition using ultrasonic Doppler sonar
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
ISWC '08 Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers
Evaluating Gesture Recognition by Multiple-Sensor-Containing Mobile Devices
ISWC '11 Proceedings of the 2011 15th Annual International Symposium on Wearable Computers
Particle filters for position sensing with asynchronous ultrasonic beacons
LoCA'06 Proceedings of the Second international conference on Location- and Context-Awareness
SoundWave: using the doppler effect to sense gestures
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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We propose an activity and context recognition method where the user carries a neck-worn receiver comprising a microphone, and small speakers on his wrists that generate ultrasounds. The system recognizes gestures on the basis of the volume of the received sound and the Doppler effect. The former indicates the distance between the neck and wrists, and the later indicates the speed of motions. Thus, our approach substitutes the wired or wireless communication typically required in body area motion sensing networks by ultrasounds. Our system also recognizes the place where the user is in and the people who are near the user by ID signals generated from speakers placed in rooms and on people. The strength of the approach is that, for offline recognition, a simple audio recorder can be used for the receiver. We evaluate the approach in one scenario on nine gestures/activities with 10 users. Evaluation results confirmed that when there was no environmental sound generated from other people, the recognition rate was 87% on average. When there was environmental sound generated from other people, we compare approach ultrasound-based recognition which uses only the feature value of ultrasound against standard approach, which uses feature value of ultrasound and environmental sound. Results for the proposed approach are 65%, for the standard approach are 57%.