Abstract
Analytical tools for the personalized assessment of natural behaviors are in great demand today, particularly among the community of performing artists. New wearables offer a variety of physiological signals that require proper integration in order to achieve this. Advances in this area of research would provide the artist and trainers with outcome measures of performance to help develop a standardized statistical language to facilitate communication across fields. In this work we present new visualization tools and analytics that enable the automatic identification and tracking of noise-to-signal transitions. The frequency of such transitions differentiate periods of spontaneous random noise from periods of well-structured noise in human motion. The latter are conducive of a predictive code denoting volition. The analyses are tailored to personalized tracking but also amenable to track the performance of an ensemble. We use our example to discuss new possibilities that these research tools may open for the community of performing artists.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 3rd International Symposium on Movement and Computing, MOCO 2016 |
| Publisher | Association for Computing Machinery |
| ISBN (Electronic) | 9781450343077 |
| DOIs | |
| State | Published - Jul 5 2016 |
| Event | 3rd International Symposium on Movement and Computing, MOCO 2016 - Thessaloniki, Greece Duration: Jul 5 2016 → Jul 6 2016 |
Publication series
| Name | ACM International Conference Proceeding Series |
|---|---|
| Volume | 05-06-July-2016 |
Other
| Other | 3rd International Symposium on Movement and Computing, MOCO 2016 |
|---|---|
| Country | Greece |
| City | Thessaloniki |
| Period | 7/5/16 → 7/6/16 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Software
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Computer Networks and Communications
Keywords
- Deliberate motions
- Personalized statistics
- Sleep and exercise
- Spontaneous motions
- Wearable sensors
Cite this
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Outcome measures of deliberate and spontaneous motions. / Kalampratsidou, Vilelmini; Torres, Elizabeth.
Proceedings of the 3rd International Symposium on Movement and Computing, MOCO 2016. Association for Computing Machinery, 2016. 9 (ACM International Conference Proceeding Series; Vol. 05-06-July-2016).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Outcome measures of deliberate and spontaneous motions
AU - Kalampratsidou, Vilelmini
AU - Torres, Elizabeth
PY - 2016/7/5
Y1 - 2016/7/5
N2 - Analytical tools for the personalized assessment of natural behaviors are in great demand today, particularly among the community of performing artists. New wearables offer a variety of physiological signals that require proper integration in order to achieve this. Advances in this area of research would provide the artist and trainers with outcome measures of performance to help develop a standardized statistical language to facilitate communication across fields. In this work we present new visualization tools and analytics that enable the automatic identification and tracking of noise-to-signal transitions. The frequency of such transitions differentiate periods of spontaneous random noise from periods of well-structured noise in human motion. The latter are conducive of a predictive code denoting volition. The analyses are tailored to personalized tracking but also amenable to track the performance of an ensemble. We use our example to discuss new possibilities that these research tools may open for the community of performing artists.
AB - Analytical tools for the personalized assessment of natural behaviors are in great demand today, particularly among the community of performing artists. New wearables offer a variety of physiological signals that require proper integration in order to achieve this. Advances in this area of research would provide the artist and trainers with outcome measures of performance to help develop a standardized statistical language to facilitate communication across fields. In this work we present new visualization tools and analytics that enable the automatic identification and tracking of noise-to-signal transitions. The frequency of such transitions differentiate periods of spontaneous random noise from periods of well-structured noise in human motion. The latter are conducive of a predictive code denoting volition. The analyses are tailored to personalized tracking but also amenable to track the performance of an ensemble. We use our example to discuss new possibilities that these research tools may open for the community of performing artists.
KW - Deliberate motions
KW - Personalized statistics
KW - Sleep and exercise
KW - Spontaneous motions
KW - Wearable sensors
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U2 - 10.1145/2948910.2948930
DO - 10.1145/2948910.2948930
M3 - Conference contribution
AN - SCOPUS:84979730183
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 3rd International Symposium on Movement and Computing, MOCO 2016
PB - Association for Computing Machinery
ER -