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Open Access Research

Monitoring motor capacity changes of children during rehabilitation using body-worn sensors

Christina Strohrmann1*, Rob Labruyère2, Corinna N Gerber2, Hubertus J van Hedel2, Bert Arnrich1 and Gerhard Tröster1

Author Affiliations

1 Wearable Computing Lab.,, ETH Zurich, Gloriastrasse 35, Zurich, Switzerland

2 Pediatric Rehab Research Group, Rehabilitation Center, University Children’s Hospital Zurich, Mühlebergstrasse 104, Affoltern am Albis, Switzerland

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Journal of NeuroEngineering and Rehabilitation 2013, 10:83  doi:10.1186/1743-0003-10-83

Published: 30 July 2013

Abstract

Background

Rehabilitation services use outcome measures to track motor performance of their patients over time. State-of-the-art approaches use mainly patients’ feedback and experts’ observations for this purpose. We aim at continuously monitoring children in daily life and assessing normal activities to close the gap between movements done as instructed by caregivers and natural movements during daily life. To investigate the applicability of body-worn sensors for motor assessment in children, we investigated changes in movement capacity during defined motor tasks longitudinally.

Methods

We performed a longitudinal study over four weeks with 4 children (2 girls; 2 diagnosed with Cerebral Palsy and 2 with stroke, on average 10.5 years old) undergoing rehabilitation. Every week, the children performed 10 predefined motor tasks. Capacity in terms of quality and quantity was assessed by experts and movement was monitored using 10 ETH Orientation Sensors (ETHOS), a small and unobtrusive inertial measurement unit. Features such as smoothness of movement were calculated from the sensor data and a regression was used to estimate the capacity from the features and their relation to clinical data. Therefore, the target and features were normalized to range from 0 to 1.

Results

We achieved a mean RMS-error of 0.15 and a mean correlation value of 0.86 (p<0.05 for all tasks) between our regression estimate of motor task capacity and experts’ ratings across all tasks. We identified the most important features and were able to reduce the sensor setup from 10 to 3 sensors. We investigated features that provided a good estimate of the motor capacity independently of the task performed, e.g. smoothness of the movement.

Conclusions

We found that children’s task capacity can be assessed from wearable sensors and that some of the calculated features provide a good estimate of movement capacity over different tasks. This indicates the potential of using the sensors in daily life, when little or no information on the task performed is available. For the assessment, the use of three sensors on both wrists and the hip suffices. With the developed algorithms, we plan to assess children’s motor performance in daily life with a follow-up study.

Keywords:
Experiments; Signal processing; Rehabilitation; Children; Wearable sensors; Motor performance