Background: A waist-mounted sensor is an attractive option for detecting initial and end of foot contacts during gait in a clinical setting without disturbing the subject's natural gait. Research question: To examine the current state of the field regarding waist-mounted sensor algorithms for gait event detection during locomotion in adults. Methods: A scoping review design was used to search peer-reviewed literature or conference proceedings published through October 2018 for algorithms for gait event detection. We analyzed data from the studies in a descriptive manner. Results: In total, 588 potentially relevant articles were selected, of which 14 (171 participants, mean age: 44.0 years) met the inclusion criteria. We identified 15 algorithms developed using biomechanical theories including the inverted pendulum model that represents gait during level walking. Most algorithms estimated gait events using triaxial acceleration data with an absolute error of approximately 50–100 ms in healthy adults. However, there was a large amount of inter-trial heterogeneity, and only a few algorithms were validated in patients with neurological diseases. Lower gait speed reduced the accuracy of gait event estimation. Significance: There was no algorithm that showed outstanding performance in the estimation of gait events during level walking using the waist-mounted sensor. More comparisons of all available algorithms with an established reference standard for one data-set are needed to identify the best algorithms. As patients with pathological conditions display altered trunk acceleration and slower gait speeds, the development of an algorithm that does not rely on particular signal characteristics and is robust for a wide range of gait speeds is needed before a specific algorithm can be recommended as a valid strategy for clinical practice.
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