2014 Volume 22 Issue 4 Pages 651-659
Wearable computing technologies are attracting a great deal of attention on context-aware systems. They recognize user context by using wearable sensors. Though conventional context-aware systems use accelerometers or microphones, the former requires wearing many sensors and a storage such as PC for data storing, and the latter cannot recognize complex user motions. In this paper, we propose an activity and context recognition method where the user carries a neck-worn receiver comprising a microphone, and small speakers on his/her 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 latter indicates the speed of motions. We combine the gesture recognition by using ultrasound and conventional MFCC-based environmental-context recognition to recognize complex contexts from the recorded sound. 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. Contexts are embedded in the recorded sound all together, and this recorded sound creates a sound-based life log with context information. We evaluate the approach 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 86.6% on average. When there was environmental sound generated from other people, we compare an approach that selects used feature values depending on a situation against standard approach, which uses feature value of ultrasound and environmental sound. Results for the proposed approach are 64.3%, for the standard approach are 57.3%.
