Objective

The purpose of this study was to develop an approach to predict hand posture (pinch versus grip) and grasp force using forearm surface electromyography (sEMG) and artificial neural networks (ANNs) during tasks that varied repetition rate and duty cycle.

Background

Prior studies have used electromyography with machine learning models to predict grip force but relatively few studies have assessed whether both hand posture and force can be predicted, particularly at varying levels of duty cycle and repetition rate.

Method

Fourteen individuals participated in this experiment. sEMG data for five forearm muscles and force output data were collected. Calibration data (25, 50, 75, 100% of maximum voluntary contraction (MVC)) were used to train ANN models to predict hand posture (pinch versus grip) and force magnitude while performing tasks that varied load, repetition rate, and duty cycle.

Results

Across all participants, overall hand posture prediction accuracy was 79% (0.79 ± .08), whereas overall hand force prediction accuracy was 73% (0.73 ± .09). Accuracy ranged between 0.65 and 0.93 based on varying repetition rate and duty cycle.

Conclusion

Hand posture and force prediction were possible using sEMG and ANNs, though there were important differences in the accuracy of predictions based on task characteristics including duty cycle and repetition rate.

Application

The results of this study could be applied to the development of a dosimeter used for distal upper extremity biomechanical exposure measurement, risk assessment, job (re)design, and return to work programs.

中文翻译：

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使用表面肌电图和人工神经网络进行手部姿势和力估计

客观的

本研究的目的是开发一种方法，在不同重复率和占空比的任务中使用前臂表面肌电图 (sEMG) 和人工神经网络 (ANN) 预测手部姿势（捏合与抓握）和抓握力。

背景

先前的研究使用肌电图和机器学习模型来预测握力，但相对较少的研究评估了是否可以预测手部姿势和力，特别是在不同水平的占空比和重复率下。

方法

十四个人参加了这个实验。收集了五个前臂肌肉的 sEMG 数据和力输出数据。校准数据（最大自主收缩 (MVC) 的 25%、50%、75%、100%）用于训练 ANN 模型以预测手部姿势（捏合与抓握）和力量大小，同时执行负载、重复率和占空比不同的任务.

结果

在所有参与者中，整体手部姿势预测准确度为 79% (0.79 ± .08)，而整体手部力量预测准确度为 73% (0.73 ± .09)。根据不同的重复率和占空比，精度介于 0.65 和 0.93 之间。

结论

使用 sEMG 和 ANN 可以预测手部姿势和力量，但基于任务特征（包括占空比和重复率）的预测准确性存在重要差异。

应用

本研究的结果可应用于开发用于远端上肢生物力学暴露测量、风险评估、工作（重新）设计和重返工作计划的剂量计。