Name
Title | ||
|---|---|---|
Effective Assessment of Cognitive Load in Real-World Scenarios using Wrist-worn Sensor Data |
Abstract | ||
|---|---|---|
ABSTRACTThe ability to assess cognitive load of an user in real time, is an integral part of effective human-computer interactions. Several approaches are used in literature for assessing cognitive load of an user in labs, however, assessment in real world scenarios is still an issue. This study proposes an approach for classification of cognitive load based on physiological signals collected through wearable devices. We have applied the proposed approach on publicly available CogLoad dataset containing galvanic skin conductance, skin temperature and heart rate information of the participants while performing a task. A set of signal property based features are proposed and an optimal subset of features are selected. These features are finally used to train a machine learning model for classification of cognitive load. Proposed approach with selected features yields the classification accuracy of 69% for binary classification which is better than the current state-of-the-art. Proposed model can be applied in scenarios like online tutorials, meetings, interviews, call center support etc. to understan the cognitive load of the user and administer changes to make the interaction more amicable and fruitful. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3469260.3469666 | Mobile Systems, Applications, and Services |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dibyanshu Jaiswal | 1 | 0 | 2.37 |
| Debatri Chatterjee | 2 | 28 | 11.52 |
| Rahul Gavas | 3 | 0 | 0.34 |
| Ramesh Kumar Ramakrishnan | 4 | 0 | 2.70 |
| Arpan Pal | 5 | 195 | 51.41 |