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Research

PRECISION AND PATIENT ACCEPTANCE OF A BELT-WORN WEARABLE (ACTIBELT) IN PATIENTS WITH OSTEOPOROSIS AND/OR AFTER TRAUMA SURGERY

The European Orthopaedic Research Society (EORS) 25th Annual and Anniversary Meeting, Munich, Germany, September 2017. Part 2 of 2.



Abstract

Mobility plays an important role, in particular for patients with osteoporosis and after trauma surgery, both as an outcome and as treatment. Mobility is closely linked to the patient”s quality of life and exercise is a powerful additional treatment option. In order to be able to generate an evidence base to evaluate various surgical and non-surgical treatment options, objective measurements of patient mobility and exercise over a certain time period are needed. Wearables are a promising candidate, with obvious advantages compared to questionnaires and/or PROs. However, when extracting parameters with wearables, one often faces the problem of algorithms not performing well enough for special cases like slow gait speeds or impaired gait, as they typically appear in this patient group. We plan to further extend the applicability of the actibelt system (3D accelerometer, 100Hz), in particular to improve the measurement precision of real-world walking speed in slow and impaired walking. We are using a special measurement wheel including a rotating 3D accelerometer that allows to capture high quality real-world walking speed and distance measurements, and a mobile high resolution camera system. In a first block 20 patients with osteoporosis were included in the study at the Ludwigs-Maximilians-University”s Department of General, Trauma and Reconstructive Surgery in Munich, Germany and equipped with an actibelt. Patients were asked to walk as “normal” as possible, while wearing their usual apparel, in the building and outside the building. They climbed stairs and had to deal with all unexpected “stop and go” events that appear in real-world walking. Various gait parameters will be extracted from the recorded data and compared to the gold standard. We will then tune the existing algorithms as well as new algorithms (e.g. step detection based on continuous wavelet transformation) to explore potential improvements of both step detection and speed estimation algorithms. Further refinement and validation using real world data is warranted.


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