SAMMPLE: Detecting Semantic Indoor Activities in Practical Settings using Locomotive Signatures

• Main Authors: YAN, Zhixian, CHAKRABORTY, Dipanjan, MISRA, Archan, JEUNG, Hoyoung, ABERER, Karl
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2012
• Subjects: activity recognition; semantic activities; accelerometer; NCCR-MICS; NCCR-MICS/ESDM; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/1521; https://ink.library.smu.edu.sg/context/sis_research/article/2520/viewcontent/MisraAiswc_2tier.pdf
• Institution: Singapore Management University

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.