This document describes a study that aimed to use smartphone technology to measure community mobility and clinical predictors of mobility recovery in stroke patients. The study involved collecting smartphone sensor data including GPS coordinates over 3 months from 4 stroke patients. Two measures of mobility were calculated from the GPS data - percentage of time spent at home and average daily distance traveled. The data was analyzed to investigate correlations between these mobility measures and clinical outcomes like walking tests and depression scores. Preliminary trends identified percentage of time spent at home as relating to some clinical outcomes like slower walking speeds and lower balance confidence.

1. Methods
Conclusions Acknowledgements
References
Introduction
Figure 3. GPS coordinates with calculated k-means centroids
and 100m radii.
Study Design:
 Initial Visit: Clinical evaluations, in-lab activity labeling and
instructions to use smartphone.
 3-Month monitoring: Data collection via smartphone app (CIMON)
with at least 2 at-home labeling sessions [2].
 Final Visit: Clinical evaluations, in-lab activity labeling.
AR Data:
 Labeled in-lab and at-home sensor data was used to train the
algorithm in classifying each activity [2].
 Unlabeled sensor data was used to test the recall of the algorithm.  No significant correlations were found, however preliminary trends
were identified.
 %TSH seems to be a related to some clinical outcomes
(10MWT-SSV, ABC and PHQ-9). More time spent at home
correlates with slower walking speeds, lower balance
confidence and higher depression ratings.
 ADDT cannot be predicted based on clinical outcomes with
possible exception to 10MWT-SSV.
 Future work will incorporate additional GPS measures such as
location variance, entropy, circadian movement [4].
I would like to thank Arun Jayaraman and his team in the Max Nader Lab
for Rehabilitation Technologies and Outcomes Research for their guidance
and friendship. This work was supported by the National Institute on
Disability, Independent Living, and Rehabilitation Research
(NIDILRR90RE5014-02-00).
1. S. E. Lord, K. McPherson, H. K. McNaughton, L. Rochester, and M. Weatherall, “Community ambulation after stroke: how important and obtainable is it and what
measures appear predictive?,” Archives of Physical Medicine and Rehabilitation, vol. 85, no. 2, pp. 234–239, Feb. 2004.
2. M. K. O’Brien et al., “Activity Recognition for Persons With Stroke Using Mobile Phone Technology: Toward Improved Performance in a Home Setting,” Journal of
Medical Internet Research, vol. 19, no. 5, p. e184, May 2017.
3. D. Arthur and S. Vassilvitskii, “k-means++: The advantages of careful seeding,” in Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms,
2007, pp. 1027–1035.
4. S. Saeb et al., “Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study,” Journal of Medical Internet Research,
vol. 17, no. 7, p. e175, Jul. 2015.
Merging smartphone technology with physical rehabilitation may
lead to improved clinical interventions and outcome assessments [1].
We aim to use mobile technology to measure community mobility
and determine clinical predictors of mobility and recovery in persons
with stroke.
Figure 2. Representative accelerometer data during lying and walking.
GPS Mobility measures:
 Percent time spent at home (%TSH): Frequently visited locations
were identified using the k-means clustering method (k=10). GPS
data within a 100m radius of each location were used for time
calculations, including the participant’s home location [3].
 Average Daily Distance Travelled (ADDT): Speeds of 0.8 m/s or
higher were considered to be in a translational state, while others
being stationary locations. Distances travelled within the
translational state were summed for ADDT.
Participants:
 Three months of GPS data, from each of 4 participants, were
included in this analysis from an ongoing study (n=17).
Results
Continued: Methods
Mobility = Discrete Physical Activities + Community Movement
Mobility Monitoring = AR Algorithm + GPS
Activity Recognition (AR) Algorithm:
 Classifies six physical activities: standing, walking, sitting, lying,
stairs ascent and descent.
Global Positioning System (GPS):
 Quantifies movement and participation in the community.
Clinical outcomes
 6MWT, 10MWT, ABC Scale, Mini-BEST and PHQ-9.
Figure 1. Overview of smartphone sensing platform and mobility measures.
Figure 5. Percent change in clinical outcomes as a predictor of percent time spent at home. The coefficient of correlation and
p-value are indicated along with the trend line.
Figure 6. Percent change in clinical outcomes as a predictor of average daily distance travelled. The coefficient of correlation
and p-value are indicated along with the trend line.
Percent of Time Spent at Home Average Daily Distance Traveled
Figure 4. GPS coordinates in stationary and translational
states.
(m)(m)
Monitoring Community Mobility of Persons with Stroke Using Smartphone Technology
Jairo Maldonado-Contreras1,4, Megan K. O’Brien, PhD1,2, Chaithanya K. Mummidisetty, MS1, Xiao Bo, MS3,
Christian Poellabauer, PhD3, Arun Jayaraman, PT, PhD1,2
1Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, 2Department of Physical Medicine and Rehabilitation,
Northwestern University, 3Department of Computer Science and Engineering, University of Notre Dame, and 4Department of Mechanical and Aerospace Engineering,
California State University Long Beach