SoftBoMI: a non-invasive wearable body-machine interface for mapping movement of shoulder to commands.

Rongkai Liu, Quanjun Song, Tingting Ma, Hongqing Pan, Hao Li, Xinyan Zhao
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Abstract

Objective.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.Approach.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.Main results.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.Significance.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.

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SoftBoMI:用于将肩部运动映射到指令的非侵入式可穿戴体机接口。
目的:用于控制辅助设备的定制化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拥有残余的肩部活动能力,利用它来生成操作辅助设备的指令可以作为脑机接口的一种补充方法:我们提出了一种混合型体机接口原型,它集成了软传感器和惯性测量单元。这项研究引入了基于规则的数据解码方法和基于用户意图推理的解码方法,将人体肩部动作映射为连续指令。此外,通过将用户操作性能的先验知识纳入共享自主框架,我们实施了一种自适应切换指令映射方法。这种方法实现了两种解码方法之间的无缝转换,增强了它们在不同任务中的适应性:通过一系列中心向外目标伸手任务和虚拟电动轮椅驾驶任务,在颈椎损伤者、双臂截肢者和健康人身上验证了所提出的方法。实验结果表明,使用软传感器和陀螺仪在意图推断方面表现最为全面。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,而意图推理方法更准确,但延迟更高。自适应切换解码方法通过在不同任务的解码方法之间无缝切换,提供了最佳的适应性。此外,我们还讨论了实验中各类参与者的差异和特点 意义:所提出的方法有望集成到服装中,实现与日常生活中的辅助设备的无创互动,并可作为未来康复评估的工具。
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