Accuracy of sensor-based classification of clinically relevant motor activities in daily life of children with mobility impairments

Abstract

Background: Wearable inertial sensors enable objective, long-term monitoring of motor activities in the children's habitual environment after rehabilitation. However, sophisticated algorithms are needed to derive clinically relevant outcome measures. Therefore, we developed three independent algorithms based on the needs of pediatric rehabilitation. The first algorithm estimates the duration of lying, sitting, and standing positions and the number of sit-to- stand transitions with data of a trunk and a thigh sensor. The second algorithm detects active wheeling periods and distinguishes it from passive wheeling with data of a wrist and a wheelchair sensor. The third algorithm detects walking periods, discriminates between free and assisted walking, and estimates the covered altitude change during stair climbing with data of a single ankle sensor and a sensor placed on walking aids. Research question: This study aimed to determine the accuracy of each algorithm in children undergoing rehabilitation. Methods: Thirty-one children and adolescents with various medical diagnoses and levels of mobility impairments performed a semi-structured activity circuit. They wore inertial sensors on both wrists, the sternum, and the thigh and shank of the less-affected side. Video recordings, which were labeled by two independent researchers, served as reference criteria to determine the algorithms' performance. Results: The activity classification accuracy was 97% for the posture detection algorithm, 96% for the wheeling detection algorithm, and 93% for the walking detection algorithm. Significance: This study presents three novel algorithms that provide a comprehensive and clinically relevant view of the children's motor activities. These algorithms are described reproducibly and can be applied to other inertial sensor technologies. Moreover, they were validated in children with mobility impairments and can be used in clinical practice and clinical trials to determine the children's motor performance in their habitual environment. To enable the evaluation of future algorithms, we published the labeled dataset.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study was supported by the Walter Muggli Fund of the ACCENTUS Foundation, Zurich, Switzerland, the Children's Research Center of the University Children's Hospital of Zurich, Switzerland, and the Anna Mueller Grocholski Foundation, Zurich, Switzerland. The funding sources were not involved in designing the study; in collecting, analyzing, and interpreting data; and in writing the report.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The ethics committee of the Canton Zurich, Switzerland (Kantonale Ethikkommission Zuerich) gave ethical approval for this work.

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Data Availability

All data produced in the present study are available upon reasonable request to the authors.

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