The Mobile Sensing Platform: An Embedded Activity Recognition System Export

@article{choudhury:msp, abstract = {The Mobile Sensing Platform (MSP) is a small-form-factor wearable device designed for embedded activity recognition. The MSP aims broadly to support context-aware ubiquitous computing applications. It incorporates multimodal sensing, data processing and inference, storage, all-day battery life, and wireless connectivity into a single 4 oz (115 g) wearable unit. Several design iterations and real-world deployments over the last four years have identified a set of core hardware and software requirements for a mobile inference system. This article presents findings and lessons learned in the course of designing, improving and using this system. This article is part of a special issue on activity-based computing.}, address = {Los Alamitos, CA, USA}, author = {Choudhury, Tanzeem and Borriello, Gaetano and Consolvo, Sunny and Haehnel, Dirk and Harrison, Beverly and Hemingway, Bruce and Hightower, Jeffrey and Klasnja, Predrag ". and Koscher, Karl and Lamarca, Anthony and Landay, James A. and Legrand, Louis and Lester, Jonathan and Rahimi, Ali and Rea, Adam and Wyatt, Danny}, citeulike-article-id = {4218358}, citeulike-linkout-0 = {http://doi.ieeecomputersociety.org/10.1109/MPRV.2008.39}, citeulike-linkout-1 = {http://dx.doi.org/10.1109/MPRV.2008.39}, doi = {10.1109/MPRV.2008.39}, issn = {1536-1268}, journal = {IEEE Pervasive Computing}, keywords = {activity-inferencing, as626, sensing}, number = {2}, pages = {32--41}, posted-at = {2009-03-25 12:47:58}, priority = {2}, publisher = {IEEE Computer Society}, title = {The Mobile Sensing Platform: An Embedded Activity Recognition System}, url = {http://dx.doi.org/10.1109/MPRV.2008.39}, volume = {7}, year = {2008} }

TY - JOUR ID - choudhury:msp L3 - citeulike-article-id:4218358 N2 - The Mobile Sensing Platform (MSP) is a small-form-factor wearable device designed for embedded activity recognition. The MSP aims broadly to support context-aware ubiquitous computing applications. It incorporates multimodal sensing, data processing and inference, storage, all-day battery life, and wireless connectivity into a single 4 oz (115 g) wearable unit. Several design iterations and real-world deployments over the last four years have identified a set of core hardware and software requirements for a mobile inference system. This article presents findings and lessons learned in the course of designing, improving and using this system. This article is part of a special issue on activity-based computing. IS - 2 JF - IEEE Pervasive Computing SN - 1536-1268 AD - Los Alamitos, CA, USA EP - 41 TI - The Mobile Sensing Platform: An Embedded Activity Recognition System PB - IEEE Computer Society VL - 7 SP - 32 KW - activity-inferencing KW - as626 KW - sensing AU - Choudhury, Tanzeem AU - Borriello, Gaetano AU - Consolvo, Sunny AU - Haehnel, Dirk AU - Harrison, Beverly AU - Hemingway, Bruce AU - Hightower, Jeffrey AU - Klasnja, Predrag AU - Koscher, Karl AU - Lamarca, Anthony AU - Landay, James AU - Legrand, Louis AU - Lester, Jonathan AU - Rahimi, Ali AU - Rea, Adam AU - Wyatt, Danny PY - 2008/// UR - http://dx.doi.org/10.1109/MPRV.2008.39 ER -