Hefei Institutes of Physical Science (HFIPS), Chinese Academy of Sciences, Hefei 230031, Anhui, People's Republic of China.
University of Science and Technology of China (USTC), Hefei 230026, Anhui, People's Republic of China.
J Neural Eng. 2024 Nov 8;21(6). doi: 10.1088/1741-2552/ad8b6e.
Customized human-machine interfaces for controlling assistive devices are vital in improving the self-help ability of upper limb amputees and tetraplegic patients. Given that most of them possess residual shoulder mobility, using it to generate commands to operate assistive devices can serve as a complementary approach to brain-computer interfaces.We propose a hybrid body-machine interface prototype that integrates soft sensors and an inertial measurement unit. This study introduces both a rule-based data decoding method and a user intent inference-based decoding method to map human shoulder movements into continuous commands. Additionally, by incorporating prior knowledge of the user's operational performance into a shared autonomy framework, we implement an adaptive switching command mapping approach. This approach enables seamless transitions between the two decoding methods, enhancing their adaptability across different tasks.The proposed method has been validated on individuals with cervical spinal cord injury, bilateral arm amputation, and healthy subjects through a series of center-out target reaching tasks and a virtual powered wheelchair driving task. The experimental results show that using both the soft sensors and the gyroscope exhibits the most well-rounded performance in intent inference. Additionally, the rule-based method demonstrates better dynamic performance for wheelchair operation, while the intent inference method is more accurate but has higher latency. Adaptive switching decoding methods offer the best adaptability by seamlessly transitioning between decoding methods for different tasks. Furthermore, we discussed the differences and characteristics among the various types of participants in the experiment.The proposed method has the potential to be integrated into clothing, enabling non-invasive interaction with assistive devices in daily life, and could serve as a tool for rehabilitation assessment in the future.
用于控制辅助设备的定制人机界面对于提高上肢截肢者和四肢瘫痪患者的自助能力至关重要。鉴于他们中的大多数人都有肩部活动的残留能力，因此可以使用它来生成操作辅助设备的命令，作为脑机接口的一种补充方法。我们提出了一种混合的人机界面原型，它集成了软传感器和惯性测量单元。本研究介绍了一种基于规则的数据解码方法和一种基于用户意图推断的解码方法，将人的肩部运动映射为连续命令。此外，通过将用户操作性能的先验知识纳入共享自主框架，我们实现了一种自适应切换命令映射方法。这种方法可以在两种解码方法之间进行无缝切换，提高它们在不同任务中的适应性。
通过一系列中心到目标的到达任务和虚拟动力轮椅驾驶任务，对颈脊髓损伤、双侧手臂截肢和健康受试者进行了验证。实验结果表明，使用软传感器和陀螺仪进行意图推断的性能最为全面。此外，基于规则的方法在轮椅操作方面表现出更好的动态性能，而意图推断方法更准确但延迟更高。自适应切换解码方法通过在不同任务之间无缝切换解码方法，提供了最佳的适应性。此外，我们讨论了实验中各种类型参与者之间的差异和特点。
该方法有望集成到服装中，实现日常生活中与辅助设备的非侵入性交互，并在未来成为康复评估的工具。
