ABSTRACT
Wearable technologies play a central role in human-centered Internet-of-Things applications. Wearables leverage computational and machine learning algorithms to detect events of interest such as physical activities and medical complications. A major obstacle in large-scale utilization of current wearables is that their computational algorithms need to be re-built from scratch upon any changes in the configuration of the network. Retraining of these algorithms requires significant amount of labeled training data, a process that is labor-intensive, time-consuming, and infeasible. We propose an approach for automatic retraining of the machine learning algorithms in real-time without need for any labeled training data. We measure the inherent correlation between observations made by an old sensor view for which trained algorithms exist and the new sensor view for which an algorithm needs to be developed. By applying our real-time multi-view autonomous learning approach, we achieve an accuracy of 80.66% in activity recognition, which is an improvement of 15.96% in the accuracy due to the automatic labeling of the data in the new sensor node. This performance is only 7.96% lower than the experimental upper bound where labeled training data are collected with the new sensor.
- U. Anliker, J. Ward, Lukowicz et al., "Amon: a wearable multiparameter medical monitoring and alert system," Information Technology in Biomedicine, IEEE Transactions on, vol. 8, no. 4, pp. 415--427, Dec 2004. Google ScholarDigital Library
- K. D. Feuz and D. J. Cook, "Transfer learning across feature-rich heterogeneous feature spaces via feature-space remapping (fsr)," ACM Transactions on Intelligent Systems and Technology (TIST), vol. 6, no. 1, p. 3, 2015. Google ScholarDigital Library
- J. Stankovic, "Research directions for the internet of things," Internet of Things Journal, IEEE, vol. 1, no. 1, pp. 3--9, Feb 2014.Google ScholarCross Ref
- H. Ghasemzadeh, N. Amini, R. Saeedi, and M. Sarrafzadeh, "Power-aware computing in wearable sensor networks: An optimal feature selection," Mobile Computing, IEEE Transactions on, vol. 14, no. 4, pp. 800--812, April 2015.Google ScholarDigital Library
- C. M. Bishop et al., Pattern recognition and machine learning. springer New York, 2006, vol. 4, no. 4. Google ScholarDigital Library
- D. Zakim and M. Schwab, "Data collection as a barrier to personalized medicine," Trends in pharmacological sciences, vol. 36, no. 2, pp. 68--71, 2015.Google ScholarCross Ref
- L. Bao and S. S. Intille, "Activity recognition from user-annotated acceleration data," in Pervasive computing. Springer, 2004, pp. 1--17.Google Scholar
- S. J. Pan and Q. Yang, "A survey on transfer learning," Knowledge and Data Engineering, IEEE Transactions on, vol. 22, no. 10, pp. 1345--1359, Oct 2010. Google ScholarDigital Library
- W. Dai, Q. Yang, G.-R. Xue, and Y. Yu, "Boosting for transfer learning," in Proceedings of the 24th international conference on Machine learning. ACM, 2007, pp. 193--200. Google ScholarDigital Library
- Y. Freund and R. E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," Journal of Computer and System Sciences, vol. 55, no. 1, pp. 119--139, 1997. Google ScholarDigital Library
- D. Roggen, K. FÃűrster, A. Calatroni, and G. TrÃűster, "The adarc pattern analysis architecture for adaptive human activity recognition systems," Journal of Ambient Intelligence and Humanized Computing, vol. 4, no. 2, pp. 169--186, 2013.Google ScholarCross Ref
- M. Kurz, G. Holzl, A. Ferscha, A. Calatroni, D. Roggen, G. Troster, Sagha et al., "The opportunity framework and data processing ecosystem for opportunistic activity and context recognition," International Journal of Sensors Wireless Communications and Control, vol. 1, no. 2, pp. 102--125, 2011.Google ScholarCross Ref
- A. Calatroni, D. Roggen, and G. Troster, "Automatic transfer of activity recognition capabilities between body-worn motion sensors: Training newcomers to recognize locomotion," Eighth international conference on networked sensing systems (INSS'11), Penghu, Taiwan, vol. 6, 2011.Google Scholar
- T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein et al., Introduction to algorithms. MIT press Cambridge, 2001, vol. 2. Google ScholarDigital Library
- R. Jonker and T. Volgenant, "Improving the hungarian assignment algorithm," Operations Research Letters, vol. 5, no. 4, pp. 171--175, 1986. Google ScholarDigital Library
- Plug-n-learn: automatic learning of computational algorithms in human-centered internet-of-things applications
Recommendations
Share-n-Learn: A Framework for Sharing Activity Recognition Models in Wearable Systems With Context-Varying Sensors
Wearable sensors utilize machine learning algorithms to infer important events such as the behavioral routine and health status of their end users from time-series sensor data. A major obstacle in large-scale utilization of these systems is that the ...
Learn from Yesterday: a semi-supervised continual learning method for supervision-limited text-to-SQL task streams
AAAI'23/IAAI'23/EAAI'23: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial IntelligenceConventional text-to-SQL studies are limited to a single task with a fixed-size training and test set. When confronted with a stream of tasks common in real-world applications, existing methods struggle with the problems of insufficient supervised data ...
Learning to Learn in a Semi-supervised Fashion
Computer Vision – ECCV 2020AbstractTo address semi-supervised learning from both labeled and unlabeled data, we present a novel meta-learning scheme. We particularly consider that labeled and unlabeled data share disjoint ground truth label sets, which can be seen tasks like in ...
Comments