Abstract
With the proliferation of wearable sensors, we have access to rich information regarding human movement that gives us insights into our daily activities like never before. In a sensor rich environment, it is desirable to build systems that are aware of human interactions by studying contextual information. In this paper, we attempt to quantify one such contextual cue - the connectedness of physical movement. Inspired by the Semblance of Typology Entrainments, we estimate the connectedness of trained dancers as observed from inertial sensors, using a diverse set of techniques such as quaternion correlation, approximate entropy, Fourier temporal pyramids, and discrete cosine transform. Preliminary experiments show that it is possible to robustly estimate connectedness that is invariant to frequency, amplitude, noise or time lag.
| Original language | English (US) |
|---|---|
| Title of host publication | ACM International Conference Proceeding Series |
| Publisher | Association for Computing Machinery |
| Pages | 120-123 |
| Number of pages | 4 |
| Volume | 14-15-August-2015 |
| ISBN (Print) | 9781450334570 |
| DOIs | |
| State | Published - Aug 14 2015 |
| Event | 2nd International Workshop on Movement and Computing, MOCO 2015 - Vancouver, Canada Duration: Aug 14 2015 → Aug 15 2015 |
Other
| Other | 2nd International Workshop on Movement and Computing, MOCO 2015 |
|---|---|
| Country | Canada |
| City | Vancouver |
| Period | 8/14/15 → 8/15/15 |
Fingerprint
Keywords
- Automated society
- Automation
- CHI
- Connectedness
- Correlation
- Cross approximate entropy
- Discrete cosine transform
- Fourier temporal pyramids
- Group intention
- Group movement
- HCI
- Human movement
- Social signal processing
- Time series analysis
- Wearable sensing
ASJC Scopus subject areas
- Human-Computer Interaction
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Software
Cite this
Towards realtime measurement of connectedness in human movement. / Krzyzaniak, Michael; Anirudh, Rushil; Venkataraman, Vinay; Turaga, Pavan; Wei, Sha Xin.
ACM International Conference Proceeding Series. Vol. 14-15-August-2015 Association for Computing Machinery, 2015. p. 120-123.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Towards realtime measurement of connectedness in human movement
AU - Krzyzaniak, Michael
AU - Anirudh, Rushil
AU - Venkataraman, Vinay
AU - Turaga, Pavan
AU - Wei, Sha Xin
PY - 2015/8/14
Y1 - 2015/8/14
N2 - With the proliferation of wearable sensors, we have access to rich information regarding human movement that gives us insights into our daily activities like never before. In a sensor rich environment, it is desirable to build systems that are aware of human interactions by studying contextual information. In this paper, we attempt to quantify one such contextual cue - the connectedness of physical movement. Inspired by the Semblance of Typology Entrainments, we estimate the connectedness of trained dancers as observed from inertial sensors, using a diverse set of techniques such as quaternion correlation, approximate entropy, Fourier temporal pyramids, and discrete cosine transform. Preliminary experiments show that it is possible to robustly estimate connectedness that is invariant to frequency, amplitude, noise or time lag.
AB - With the proliferation of wearable sensors, we have access to rich information regarding human movement that gives us insights into our daily activities like never before. In a sensor rich environment, it is desirable to build systems that are aware of human interactions by studying contextual information. In this paper, we attempt to quantify one such contextual cue - the connectedness of physical movement. Inspired by the Semblance of Typology Entrainments, we estimate the connectedness of trained dancers as observed from inertial sensors, using a diverse set of techniques such as quaternion correlation, approximate entropy, Fourier temporal pyramids, and discrete cosine transform. Preliminary experiments show that it is possible to robustly estimate connectedness that is invariant to frequency, amplitude, noise or time lag.
KW - Automated society
KW - Automation
KW - CHI
KW - Connectedness
KW - Correlation
KW - Cross approximate entropy
KW - Discrete cosine transform
KW - Fourier temporal pyramids
KW - Group intention
KW - Group movement
KW - HCI
KW - Human movement
KW - Social signal processing
KW - Time series analysis
KW - Wearable sensing
UR - http://www.scopus.com/inward/record.url?scp=84958553875&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84958553875&partnerID=8YFLogxK
U2 - 10.1145/2790994.2791012
DO - 10.1145/2790994.2791012
M3 - Conference contribution
SN - 9781450334570
VL - 14-15-August-2015
SP - 120
EP - 123
BT - ACM International Conference Proceeding Series
PB - Association for Computing Machinery
ER -