Conference Proceeding Article
We analyze the ability of mobile phone-generated accelerometer data to detect high-level (i.e., at the semantic level) indoor lifestyle activities, such as cooking at home and working at the workplace, in practical settings. We design a 2-T ier activity extraction framework (called SAMMPLE) for our purpose. Using this, we evaluate discriminatory power of activity structures along the dimension of statistical features and after a transformation to a sequence of individual locomotive micro-activities (e.g. sitting or standing). Our ﬁndings from 152 days of real-life behavioral traces reveal that locomotive signatures achieve an average accuracy of 77.14%, an improvement of 16.37% over directly using statistical features.
activity recognition, semantic activities, accelerometer, NCCR-MICS, NCCR-MICS/ESDM
International Symposium on Wearable Computers (ISWC)
City or Country
Yan, Zhixian, Dipanjan Chakraborty, Archan Misra, Hoyoung Jeung, and Karl Aberer. 2012. SAMMPLE: Detecting Semantic Indoor Activities in Practical Settings using Locomotive Signatures. Proceedings of 16th International Symposium on Wearable Computers, 37-40. New York: IEEE.
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