Manual physical balance assistance of therapists during gait training of stroke survivors: characteristics and predicting the timing
| Autor: | Jaap H. Buurke, Juliet Albertina Maria Haarman, Johan S. Rietman, Erik Maartens, Jasper Reenalda, Herman van der Kooij |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Male
Stroke rehabilitation Sacrum 030506 rehabilitation medicine.medical_specialty Acceleration Health Informatics Walking Body weight Balance-assisting characteristics lcsh:RC321-571 03 medical and health sciences 0302 clinical medicine Gait (human) Physical medicine and rehabilitation Gait training Predictive Value of Tests Postural Balance Humans Medicine Survivors Stroke survivor Gait lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Gait Disorders Neurologic Aged Balance (ability) Hip Behavior prediction business.industry Research Rehabilitation Algorithm development Reproducibility of Results Middle Aged Predictive value Exercise Therapy Physical Therapists Technical requirements Correction forces Female 0305 other medical science business Straight line walking human activities Algorithms 030217 neurology & neurosurgery |
| Zdroj: | Journal of NeuroEngineering and Rehabilitation, Vol 14, Iss 1, Pp 1-11 (2017) Journal of neuroengineering and rehabilitation, 14:125. BioMed Central Journal of NeuroEngineering and Rehabilitation |
| ISSN: | 1743-0003 |
| DOI: | 10.1186/s12984-017-0337-8 |
| Popis: | Background During gait training, physical therapists continuously supervise stroke survivors and provide physical support to their pelvis when they judge that the patient is unable to keep his balance. This paper is the first in providing quantitative data about the corrective forces that therapists use during gait training. It is assumed that changes in the acceleration of a patient’s COM are a good predictor for therapeutic balance assistance during the training sessions Therefore, this paper provides a method that predicts the timing of therapeutic balance assistance, based on acceleration data of the sacrum. Methods Eight sub-acute stroke survivors and seven therapists were included in this study. Patients were asked to perform straight line walking as well as slalom walking in a conventional training setting. Acceleration of the sacrum was captured by an Inertial Magnetic Measurement Unit. Balance-assisting corrective forces applied by the therapist were collected from two force sensors positioned on both sides of the patient’s hips. Measures to characterize the therapeutic balance assistance were the amount of force, duration, impulse and the anatomical plane in which the assistance took place. Based on the acceleration data of the sacrum, an algorithm was developed to predict therapeutic balance assistance. To validate the developed algorithm, the predicted events of balance assistance by the algorithm were compared with the actual provided therapeutic assistance. Results The algorithm was able to predict the actual therapeutic assistance with a Positive Predictive Value of 87% and a True Positive Rate of 81%. Assistance mainly took place over the medio-lateral axis and corrective forces of about 2% of the patient’s body weight (15.9 N (11), median (IQR)) were provided by therapists in this plane. Median duration of balance assistance was 1.1 s (0.6) (median (IQR)) and median impulse was 9.4Ns (8.2) (median (IQR)). Although therapists were specifically instructed to aim for the force sensors on the iliac crest, a different contact location was reported in 22% of the corrections. Conclusions This paper presents insights into the behavior of therapists regarding their manual physical assistance during gait training. A quantitative dataset was presented, representing therapeutic balance-assisting force characteristics. Furthermore, an algorithm was developed that predicts events at which therapeutic balance assistance was provided. Prediction scores remain high when different therapists and patients were analyzed with the same algorithm settings. Both the quantitative dataset and the developed algorithm can serve as technical input in the development of (robot-controlled) balance supportive devices. |
| Databáze: | OpenAIRE |
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