TY - GEN
T1 - Optimization of Brain Mobile Interface Applications Using IoT
AU - Sadeghi, Koosha
AU - Banerjee, Ayan
AU - Sohankar, Javad
AU - Gupta, Sandeep
N1 - Funding Information:
This work has been partly funded by CNS grant #1218505, IIS grant #1116385, and NIH grant #EB019202. Also, for human subject experiment, we have an ASU IRB in place (STUDY 00000445).
Publisher Copyright:
© 2016 IEEE.
PY - 2017/2/1
Y1 - 2017/2/1
N2 - Pervasive Brain Mobile Interfaces (BMoI) can be made more accurate and time efficient when knowledge from other sensors and computation power from available devices in the Internet of Things (IoT) infrastructure are utilized. This paper takes the example of Neuro-Movie (nMovie), an interactive movie application that blurs movie scenes based on mental state, to illustrate and analyze optimization opportunities when BMoI is interfaced with IoT. The three way trade-off between accuracy, real-time operation, and energy efficiency can be optimized through usage of physiological responses from IoT sensors and prediction algorithms. Latency and power models of BMoI are developed for thorough analysis of the trade-offs. Experiments on 10 volunteers show that: a) utilizing electrocardiogram responses to psychological stimulus increases the accuracy of mental state recognition by almost 10%, b) predictive models cover computation and communication latencies in the system to satisfy real-time requirements, and c) use of predictive models allows duty cycling of smartphone WiFi that potentially saves upto 71.6% communication energy.
AB - Pervasive Brain Mobile Interfaces (BMoI) can be made more accurate and time efficient when knowledge from other sensors and computation power from available devices in the Internet of Things (IoT) infrastructure are utilized. This paper takes the example of Neuro-Movie (nMovie), an interactive movie application that blurs movie scenes based on mental state, to illustrate and analyze optimization opportunities when BMoI is interfaced with IoT. The three way trade-off between accuracy, real-time operation, and energy efficiency can be optimized through usage of physiological responses from IoT sensors and prediction algorithms. Latency and power models of BMoI are developed for thorough analysis of the trade-offs. Experiments on 10 volunteers show that: a) utilizing electrocardiogram responses to psychological stimulus increases the accuracy of mental state recognition by almost 10%, b) predictive models cover computation and communication latencies in the system to satisfy real-time requirements, and c) use of predictive models allows duty cycling of smartphone WiFi that potentially saves upto 71.6% communication energy.
KW - Internet-of-things
KW - brain-mobile interface
KW - interactive movies
KW - multi-modal sensing
KW - pervasive systems
UR - http://www.scopus.com/inward/record.url?scp=85015245570&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015245570&partnerID=8YFLogxK
U2 - 10.1109/HiPC.2016.014
DO - 10.1109/HiPC.2016.014
M3 - Conference contribution
AN - SCOPUS:85015245570
T3 - Proceedings - 23rd IEEE International Conference on High Performance Computing, HiPC 2016
SP - 32
EP - 41
BT - Proceedings - 23rd IEEE International Conference on High Performance Computing, HiPC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd IEEE International Conference on High Performance Computing, HiPC 2016
Y2 - 19 December 2016 through 22 December 2016
ER -