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Should Hands Be Restricted When Measuring Able-Bodied Participants to Evaluate Machine Learning Controlled Prosthetic Hands?
IEEE Transactions on Neural Systems and Rehabilitation Engineering ( IF 4.8 ) Pub Date : 2020-07-07 , DOI: 10.1109/tnsre.2020.3007803
Morten B. Kristoffersen , Andreas W. Franzke , Corry K. van der Sluis , Raoul M. Bongers , Alessio Murgia

Objective: When evaluating methods for machine-learning controlled prosthetic hands, able-bodied participants are often recruited, for practical reasons, instead of participants with upper limb absence (ULA). However, able-bodied participants have been shown to often perform myoelectric control tasks better than participants with ULA. It has been suggested that this performance difference can be reduced by restricting the wrist and hand movements of able-bodied participants. However, the effect of such restrictions on the consistency and separability of the electromyogram’s (EMG) features remains unknown. The present work investigates whether the EMG separability and consistency between unaffected and affected arms differ and whether they change after restricting the unaffected limb in persons with ULA. Methods: Both arms of participants with unilateral ULA were compared in two conditions: with the unaffected hand and wrist restricted or not. Furthermore, it was tested if the effect of arm and restriction is influenced by arm posture (arm down, arm in front, or arm up). Results: Fourteen participants (two women, age = 53.4±4.05) with acquired transradial limb loss were recruited. We found that the unaffected limb generated more separated EMG than the affected limb. Furthermore, restricting the unaffected hand and wrist lowered the separability of the EMG when the arm was held down. Conclusion: Limb restriction is a viable method to make the EMG of able-bodied participants more similar to that of participants with ULA. Significance: Future research that evaluates methods for machine learning controlled hands in able-bodied participants should restrict the participants’ hand and wrist.

中文翻译:

测量有能力的参与者以评估机器学习控制的假手时,应该限制双手吗?

目的:在评估机器学习控制的假手方法时,出于实际原因,通常会招募身体健全的参与者,而不是上肢缺失(ULA)的参与者。但是,身体强壮的参与者表现出比执行ULA的参与者通常执行更好的肌电控制任务。已经提出,可以通过限制健全参与者的手腕和手部运动来减小这种性能差异。但是,这种限制对肌电图(EMG)特征的一致性和可分离性的影响仍然未知。目前的工作调查未受影响和受影响的手臂之间的肌电图可分离性和一致性是否不同,以及在限制ULA患者的未受影响肢体后它们是否发生了变化。方法:在两种情况下比较了单侧ULA参与者的双臂:未受影响的手和手腕是否受限。此外,还测试了手臂和约束的效果是否受手臂姿势(手臂向下,手臂在前或手臂向上)的影响。结果:招募了14名获得性trans骨下肢缺失的参与者(两名女性,年龄= 53.4±4.05)。我们发现未受影响的肢体比受影响的肢体产生了更多的分离的EMG。此外,限制未受影响的手和手腕会降低当按住手臂时EMG的可分离性。结论:肢体限制是使健壮参与者的肌电图与ULA参与者的肌电图更相似的可行方法。意义:
更新日期:2020-09-08
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