Name
Abstract | ||
|---|---|---|
Wearables have emerged as a revolutionary technology in many application domains including healthcare and fitness. Machine learning algorithms, which form the core intelligence of wearables, traditionally deduce a computational model from a set of training examples to detect events of interest (e.g. activity type). However, in the dynamic environment in which wearables typically operate in, the accuracy of a computational model drops whenever changes in configuration of the system (such as device type and sensor orientation) occur. Therefore, there is a need to develop systems which can adapt to the new configuration autonomously. In this paper, using transfer learning as an organizing principle, we develop several algorithms for data mapping. The data mapping algorithms employ effective signal similarity methods and are used to adapt the system to the new configuration. We demonstrate the efficacy of the data mapping algorithms using a publicly available dataset on human activity recognition. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/BigData.2016.7840648 | 2016 IEEE International Conference on Big Data (Big Data) |
Keywords | Field | DocType |
transfer learning,autonomous wearable system reconfiguration,machine learning,wearables intelligence,computational model,sensor orientation,data mapping,signal similarity methods,human activity recognition,healthcare,fitness | Data mining,Time series,Organizing principle,Data mapping,Computer science,Transfer of learning,Artificial intelligence,Control reconfiguration,Algorithm design,Activity recognition,Wearable computer,Algorithm,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4673-9006-4 | 3 | 0.39 |
References | Authors | |
16 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ramyar Saeedi | 1 | 81 | 8.00 |
| Hassan Ghasemzadeh | 2 | 45 | 4.66 |
| Assefaw Gebremedhin | 3 | 12 | 0.94 |