Battraw Marcus A, Fitzgerald Justin, Winslow Eden J, James Michelle A, Bagley Anita M, Joiner Wilsaan M, Schofield Jonathon S
Department of Mechanical and Aerospace Engineering, University of California, Davis, CA, USA.
Department of Biomedical Engineering, University of California, Davis, CA, USA.
Sci Rep. 2024 Dec 30;14(1):31741. doi: 10.1038/s41598-024-82519-z.
Children born with congenital upper limb absence exhibit consistent and distinguishable levels of biological control over their affected muscles, assessed through surface electromyography (sEMG). This represents a significant advancement in determining how these children might utilize sEMG-controlled dexterous prostheses. Despite this potential, the efficacy of employing conventional sEMG classification techniques for children born with upper limb absence is uncertain, as these techniques have been optimized for adults with acquired amputations. Tuning sEMG classification algorithms for this population is crucial for facilitating the successful translation of dexterous prostheses. To support this effort, we collected sEMG data from a cohort of N = 9 children with unilateral congenital below-elbow deficiency as they attempted 11 hand movements, including rest. Five classification algorithms were used to decode motor intent, tuned with features from the time, frequency, and time-frequency domains. We derived the congenital feature set (CFS) from the participant-specific tuned feature sets, which exhibited generalizability across our cohort. The CFS offline classification accuracy across participants was 73.8% ± 13.8% for the 11 hand movements and increased to 96.5% ± 6.6% when focusing on a reduced set of five movements. These results highlight the potential efficacy of individuals born with upper limb absence to control dexterous prostheses through sEMG interfaces.
通过表面肌电图（sEMG）评估，先天性上肢缺失患儿对其受影响肌肉表现出一致且可区分的生物控制水平。这在确定这些儿童如何利用sEMG控制的灵巧假肢方面是一项重大进展。尽管有这种潜力，但对于先天性上肢缺失患儿采用传统sEMG分类技术的效果尚不确定，因为这些技术是针对后天截肢的成年人进行优化的。为该人群调整sEMG分类算法对于促进灵巧假肢的成功应用至关重要。为支持这一工作，我们收集了N = 9名单侧先天性肘下缺失患儿在尝试11种手部动作（包括休息）时的sEMG数据。使用了五种分类算法来解码运动意图，并根据时间、频率和时频域的特征进行调整。我们从参与者特定的调整特征集中导出了先天性特征集（CFS），该特征集在我们的队列中具有通用性。对于11种手部动作，参与者间CFS离线分类准确率为73.8%±13.8%，当聚焦于一组简化的五种动作时，准确率提高到96.5%±6.6%。这些结果突出了先天性上肢缺失个体通过sEMG接口控制灵巧假肢的潜在效果。
