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Sensors (Basel). 2017 Sep 27;17(10). pii: E2219. doi: 10.3390/s17102219.

Analyzing Sensor-Based Time Series Data to Track Changes in Physical Activity during Inpatient Rehabilitation.

Author information

1
Department of Computer Science, Gonzaga University, Spokane, WA 99202, USA. sprint@gonzaga.edu.
2
School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99163, USA. cook@eecs.wsu.edu.
3
St. Luke's Rehabilitation Institute, Spokane, WA 99202, USA. weeksdl@st-lukes.org.
4
School of Biological Sciences, Washington State University, Pullman, WA 99163, USA. jordana.dahmen@wsu.edu.
5
Department of Mathematics and Computer Science, Whitworth University, Spokane, WA 99251, USA. alafleur19@my.whitworth.edu.

Abstract

Time series data collected from sensors can be analyzed to monitor changes in physical activity as an individual makes a substantial lifestyle change, such as recovering from an injury or illness. In an inpatient rehabilitation setting, approaches to detect and explain changes in longitudinal physical activity data collected from wearable sensors can provide value as a monitoring, research, and motivating tool. We adapt and expand our Physical Activity Change Detection (PACD) approach to analyze changes in patient activity in such a setting. We use Fitbit Charge Heart Rate devices with two separate populations to continuously record data to evaluate PACD, nine participants in a hospitalized inpatient rehabilitation group and eight in a healthy control group. We apply PACD to minute-by-minute Fitbit data to quantify changes within and between the groups. The inpatient rehabilitation group exhibited greater variability in change throughout inpatient rehabilitation for both step count and heart rate, with the greatest change occurring at the end of the inpatient hospital stay, which exceeded day-to-day changes of the control group. Our additions to PACD support effective change analysis of wearable sensor data collected in an inpatient rehabilitation setting and provide insight to patients, clinicians, and researchers.

KEYWORDS:

Fitbit; change detection; inpatient rehabilitation; pervasive computing; physical activity monitoring; wearable sensors

PMID:
28953257
PMCID:
PMC5677114
DOI:
10.3390/s17102219
[Indexed for MEDLINE]
Free PMC Article

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