A Motion Recognition Method for a Wearable Dancing Musical Instrument
ISWC '09 Proceedings of the 2009 International Symposium on Wearable Computers
Discriminative temporal smoothing for activity recognition from wearable sensors
UCS'07 Proceedings of the 4th international conference on Ubiquitous computing systems
Toward High-Level Activity Recognition from Accelerometers on Mobile Phones
ITHINGSCPSCOM '11 Proceedings of the 2011 International Conference on Internet of Things and 4th International Conference on Cyber, Physical and Social Computing
A practical approach to recognizing physical activities
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Development of a single 3-axis accelerometer sensor based wearable gesture recognition band
UIC'07 Proceedings of the 4th international conference on Ubiquitous Intelligence and Computing
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In activity recognition techniques, existing wearable sensors have a problem performing the recognition process. Because existing wearable sensors perform recognition process by dividing sensor data into partial sequences, there is lag between the changes in action and the output of the recognition result. Therefore, we focused on the point activities change and have proposed a method to reduce the response time of the activity recognition technique. However, parameters such as window size immediately after the activity changing point and the activity changing point detection in the proposed method have not been studied well. Thus, in this paper, we conducted experiments using the HASC Corpus, which contains large-scale data of human activity. We report results of examining various parameters in the proposed method and features of the proposed method revealed by comparison with a conventional method. To give a concrete example, for IIR band-pass filter bank to be used for activity changing point detection, we clarified the frequency and the appropriate number of filters. In addition, we clarified the relationship between identification accuracy and the size of a special window that is set after activity changing point detection. The proposed method reduced the response time to the 2035ms on average from 2773ms, the of average of the conventional method. In addition, the proposed method can reduce the amount of calculation, achieve both high recognition accuracy and short response time, and output the recognition results in consistent times to reduce the jitter of response time.