Intelligent Sensory Modality Selection for Electronic Supportive Devices
© 2017 ACM. Humans operating in stressful environments, such as in military or emergency first-responder roles, are subject to high sensory input loads and must often switch their attention between different modalities. Conventional supportive devices that assist users in such situations typically p...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Association for Computing Machinery (ACM)
2018
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/116007 https://orcid.org/0000-0002-9957-7111 https://orcid.org/0000-0003-1338-8107 |
| _version_ | 1811082767683813376 |
|---|---|
| author | Kotowick, Kyle Jordan Shah, Julie A |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Kotowick, Kyle Jordan Shah, Julie A |
| author_sort | Kotowick, Kyle Jordan |
| collection | MIT |
| description | © 2017 ACM. Humans operating in stressful environments, such as in military or emergency first-responder roles, are subject to high sensory input loads and must often switch their attention between different modalities. Conventional supportive devices that assist users in such situations typically provide information using a single, static sensory modality; however, this carries the risk of overload when the modalities for the primary task and the supportive device overlap. Effective feedback modality selection is essential in order to avoid such a risk. One potential method for accomplishing this is to intelligently select the supportive device's feedback modality based on the user's environment and given task; however, this may result in delayed or lost information due to the performance cost resulting from switching attention from one modality to another. This paper describes the design and results of a human-participant study designed to evaluate the benefits and risks of various intelligent modality-selection strategies. Our findings suggest complex interactions between strategies, sensory input load levels and feedback modalities, with numerous significant effects across many different performance metrics. |
| first_indexed | 2024-09-23T12:08:41Z |
| format | Article |
| id | mit-1721.1/116007 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T12:08:41Z |
| publishDate | 2018 |
| publisher | Association for Computing Machinery (ACM) |
| record_format | dspace |
| spelling | mit-1721.1/1160072022-09-28T00:28:48Z Intelligent Sensory Modality Selection for Electronic Supportive Devices Kotowick, Kyle Jordan Shah, Julie A Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Kotowick, Kyle Jordan Shah, Julie A Multimodal; sensory modality; sensory overload; switching cost; intelligent selection © 2017 ACM. Humans operating in stressful environments, such as in military or emergency first-responder roles, are subject to high sensory input loads and must often switch their attention between different modalities. Conventional supportive devices that assist users in such situations typically provide information using a single, static sensory modality; however, this carries the risk of overload when the modalities for the primary task and the supportive device overlap. Effective feedback modality selection is essential in order to avoid such a risk. One potential method for accomplishing this is to intelligently select the supportive device's feedback modality based on the user's environment and given task; however, this may result in delayed or lost information due to the performance cost resulting from switching attention from one modality to another. This paper describes the design and results of a human-participant study designed to evaluate the benefits and risks of various intelligent modality-selection strategies. Our findings suggest complex interactions between strategies, sensory input load levels and feedback modalities, with numerous significant effects across many different performance metrics. Lincoln Laboratory Natural Sciences and Engineering Research Council of Canada 2018-05-31T12:37:34Z 2018-05-31T12:37:34Z 2017-03 2018-04-10T16:40:51Z Article http://purl.org/eprint/type/ConferencePaper 9781450343480 http://hdl.handle.net/1721.1/116007 Kotowick, Kyle, and Julie Shah. “Intelligent Sensory Modality Selection for Electronic Supportive Devices.” Proceedings of the 22nd International Conference on Intelligent User Interfaces - IUI ’17 (2017). https://orcid.org/0000-0002-9957-7111 https://orcid.org/0000-0003-1338-8107 http://dx.doi.org/10.1145/3025171.3025228 Proceedings of the 22nd International Conference on Intelligent User Interfaces - IUI '17 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Association for Computing Machinery (ACM) MIT Web Domain |
| spellingShingle | Multimodal; sensory modality; sensory overload; switching cost; intelligent selection Kotowick, Kyle Jordan Shah, Julie A Intelligent Sensory Modality Selection for Electronic Supportive Devices |
| title | Intelligent Sensory Modality Selection for Electronic Supportive Devices |
| title_full | Intelligent Sensory Modality Selection for Electronic Supportive Devices |
| title_fullStr | Intelligent Sensory Modality Selection for Electronic Supportive Devices |
| title_full_unstemmed | Intelligent Sensory Modality Selection for Electronic Supportive Devices |
| title_short | Intelligent Sensory Modality Selection for Electronic Supportive Devices |
| title_sort | intelligent sensory modality selection for electronic supportive devices |
| topic | Multimodal; sensory modality; sensory overload; switching cost; intelligent selection |
| url | http://hdl.handle.net/1721.1/116007 https://orcid.org/0000-0002-9957-7111 https://orcid.org/0000-0003-1338-8107 |
| work_keys_str_mv | AT kotowickkylejordan intelligentsensorymodalityselectionforelectronicsupportivedevices AT shahjuliea intelligentsensorymodalityselectionforelectronicsupportivedevices |