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A Statistical Parametric Mapping Analysis Approach for the Evaluation of a Passive Back Support Exoskeleton on Mechanical Loading During a Simulated Patient Transfer Task.

Unai Latorre ErezumaMaialen Zelaia AmilibiaAnder EspinCamilo CortésJon IrazustaAna Rodriguez Larrad
Published in: Journal of applied biomechanics (2023)
This study assessed the effectiveness of a passive back support exoskeleton during a mechanical loading task. Fifteen healthy participants performed a simulated patient transfer task while wearing the Laevo (version 2.5) passive back support exoskeleton. Collected metrics encompassed L5-S1 joint moments, back and abdominal muscle activity, lower body and back kinematics, center of mass displacement, and movement smoothness. A statistical parametric mapping analysis approach was used to overcome limitations from discretization of continuous data. The exoskeleton reduced L5-S1 joint moments during trunk flexion, but wearing the device restricted L5-S1 joint flexion when flexing the trunk as well as hip and knee extension, preventing participants from standing fully upright. Moreover, wearing the device limited center of mass motion in the caudal direction and increased its motion in the anterior direction. Therefore, wearing the exoskeleton partly reduced lower back moments during the lowering phase of the patient transfer task, but there were some undesired effects such as altered joint kinematics and center of mass displacement. Statistical parametric mapping analysis was useful in determining the benefits and hindrances produced by wearing the exoskeleton while performing the simulated patient transfer task and should be utilized in further studies to inform design and appropriate usage.
Keyphrases
  • case report
  • high resolution
  • randomized controlled trial
  • skeletal muscle
  • big data
  • machine learning
  • psychometric properties
  • deep learning
  • lower limb