TY - GEN
T1 - Autonomous sensor-context learning in dynamic human-centered internet-of-things environments
AU - Rokni, Seyed Ali
AU - Ghasemzadeh, Hassan
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/11/7
Y1 - 2016/11/7
N2 - Human-centered Internet-of-Things (IoT) applications utilize computational algorithms such as machine learning and signal processing techniques to infer knowledge about important events such as physical activities and medical complications. The inference is typically based on data collected with wearable sensors or those embedded in the environment. A major obstacle in large-scale utilization of these systems is that the computational algorithms cannot be shared between users or reused in contexts different than the setting in which the training data are collected. For example, an activity recognition algorithm trained for a wrist-band sensor cannot be used on a smartphone worn on the waist. We propose an approach for automatic detection of physical sensor-contexts (e.g., on-body sensor location) without need for collecting new labeled training data. Our techniques enable system designers and end-users to share and reuse computational algorithms that are trained under different contexts and data collection settings. We develop a framework to autonomously identify sensor-context. We propose a gating function to automatically activate the most accurate computational algorithm among a set of shared expert models. Our analysis based on real data collected with human subjects while performing 12 physical activities demonstrate that the accuracy of our multi-view learning is only 7.9% less than the experimental upper bound for activity recognition using a dynamic sensor constantly migrating from one on-body location to another. We also compare our approach with several mixture-of-experts models and transfer learning techniques and demonstrate that our approach outperforms algorithms in both categories.
AB - Human-centered Internet-of-Things (IoT) applications utilize computational algorithms such as machine learning and signal processing techniques to infer knowledge about important events such as physical activities and medical complications. The inference is typically based on data collected with wearable sensors or those embedded in the environment. A major obstacle in large-scale utilization of these systems is that the computational algorithms cannot be shared between users or reused in contexts different than the setting in which the training data are collected. For example, an activity recognition algorithm trained for a wrist-band sensor cannot be used on a smartphone worn on the waist. We propose an approach for automatic detection of physical sensor-contexts (e.g., on-body sensor location) without need for collecting new labeled training data. Our techniques enable system designers and end-users to share and reuse computational algorithms that are trained under different contexts and data collection settings. We develop a framework to autonomously identify sensor-context. We propose a gating function to automatically activate the most accurate computational algorithm among a set of shared expert models. Our analysis based on real data collected with human subjects while performing 12 physical activities demonstrate that the accuracy of our multi-view learning is only 7.9% less than the experimental upper bound for activity recognition using a dynamic sensor constantly migrating from one on-body location to another. We also compare our approach with several mixture-of-experts models and transfer learning techniques and demonstrate that our approach outperforms algorithms in both categories.
UR - http://www.scopus.com/inward/record.url?scp=85000997420&partnerID=8YFLogxK
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U2 - 10.1145/2966986.2967008
DO - 10.1145/2966986.2967008
M3 - Conference contribution
AN - SCOPUS:85000997420
T3 - IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
BT - 2016 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 35th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2016
Y2 - 7 November 2016 through 10 November 2016
ER -