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This systematic review aims to evaluate the application of machine learning (ML) and artificial intelligence (AI) methods in analyzing data from wearable inertial measurement units (IMUs) in clinical, biomechanical, and healthcare settings. Specifically, we seek to identify: 1) the types of ML/AI models used, 2) the motions and sensor-derived features analyzed, 3) the applications and outcomes assessed, and 4) the performance metrics reported. We aim to highlight the most promising analytical strategies and guide future research toward the development of objective sensor-based functional assessment tools for clinical use. First, we ask: How are machine learning and artificial intelligence methods applied to assess human movement impairments associated with musculoskeletal and neurological conditions using wearable inertial measurement unit (IMU) data in clinical, biomechanical, or healthcare applications? Secondly, we also ask: How do machine learning and artificial intelligence methods complement or extend traditional biomechanical and statistical analysis methods in assessing human movement?

1Jack Miner and the Birds and Some Things I Know about Nature

By Jack Miner

Recollections of personal experiences and observations by a noted Canadian conservationist and influential figure in the practice of banding birds. - Summary by Jonathon Kirk