TY - GEN
T1 - Quantifying community mobility after Stroke using mobile phone technology
AU - O'Brien, Megan K.
AU - Mummidisetty, Chaithanya K.
AU - Bo, Xiao
AU - Jayaraman, Arun
N1 - Funding Information:
This work is funded by the National Institute on Disability, Independent Living, and Rehabilitation Research (RERC-TEAMM grant H133E130020).
PY - 2017/9/11
Y1 - 2017/9/11
N2 - Stroke is a common neurological disorder that can drastically impact mobility - that is, the ability to move in and through one's surroundings. Although rehabilitation programs focus on restoring mobility after stroke, there is no present method for reliably and fully quantifying mobility outside of a clinical setting. Smartphones are ubiquitous wearable sensing systems that can measure community mobility using movement and location tracking. We are currently investigating the ability of smartphone data to characterize post-stroke recovery in a long-term monitoring study (3-6 months). We will evaluate the potential of community mobility features to predict recovery, as determined by traditional clinical assessments. Preliminary results will be available in August 2017. Findings will provide a more objective, complete picture of recovery following stroke as well as the real-world impact of rehabilitation.
AB - Stroke is a common neurological disorder that can drastically impact mobility - that is, the ability to move in and through one's surroundings. Although rehabilitation programs focus on restoring mobility after stroke, there is no present method for reliably and fully quantifying mobility outside of a clinical setting. Smartphones are ubiquitous wearable sensing systems that can measure community mobility using movement and location tracking. We are currently investigating the ability of smartphone data to characterize post-stroke recovery in a long-term monitoring study (3-6 months). We will evaluate the potential of community mobility features to predict recovery, as determined by traditional clinical assessments. Preliminary results will be available in August 2017. Findings will provide a more objective, complete picture of recovery following stroke as well as the real-world impact of rehabilitation.
KW - Activity recognition
KW - GPS
KW - Machine learning
KW - Smartphone
KW - Stroke rehabilitation
KW - Wearable technology
UR - http://www.scopus.com/inward/record.url?scp=85030836441&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85030836441&partnerID=8YFLogxK
U2 - 10.1145/3123024.3123085
DO - 10.1145/3123024.3123085
M3 - Conference contribution
AN - SCOPUS:85030836441
T3 - UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers
SP - 161
EP - 164
BT - UbiComp/ISWC 2017 - Adjunct Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers
PB - Association for Computing Machinery, Inc
T2 - 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and ACM International Symposium on Wearable Computers, UbiComp/ISWC 2017
Y2 - 11 September 2017 through 15 September 2017
ER -