Preference-based assistance optimization for lifting and lowering with a soft back exosuit

Wearable robotic devices have become increasingly prevalent in both occupational and rehabilitative settings, yet their widespread adoption remains inhibited by usability barriers related to comfort, restriction, and noticeable functional benefits. Acknowledging the importance of user perception in this context, this study explores preference-based controller optimization for a back exosuit that assists lifting. Considering the high mental and metabolic effort discrete motor tasks impose, we used a forced-choice Bayesian Optimization approach that promotes sampling efficiency by leveraging domain knowledge about just noticeable differences between assistance settings. Optimizing over two control parameters, preferred settings were consistent within and uniquely different between participants. We discovered that overall, participants preferred asymmetric parameter configurations with more lifting than lowering assistance, and that preferences were sensitive to user anthropometrics. These..., , , # Preference-based assistance optimization for lifting and lowering with a soft back exosuit

[https://doi.org/10.5061/dryad.2z34tmpx5](https://doi.org/10.5061/dryad.2z34tmpx5)

## Description of the data and file structure

This dataset comprises 3 zip files. One file each for the experiment to estimate a lowering gain/lifting gain (LOG/LIG) just noticeable difference (JND), respectively and one for the data of the main preference-based controller optimization experiment.

The JND experiments contain a csv file with the data for all subjects. The csv file contains 8 columns (all parameters are unitless), namely:

1. Subject ID
2. F1: LOG or LIG Option 1
3. F2: LOG or LIG Option 2
4. FB: User Feedback. 0/1 = Option 1/2 perceived as larger
5. Block: Experiment Block
6. LOG/LIG: The other control parameter which was held constant within a block
7. gd: Gain difference, i.e., the difference between F1 and F2
8. correct: Did subject correctly perceive the larger option as larger

The prefer...,

# 基于偏好的软式背部外骨骼服（back exosuit）抬举与下放辅助优化
可穿戴机器人设备（wearable robotic devices）在职业场景与康复场景中的应用愈发普及，但其大规模部署仍受限于与舒适度、活动受限及功能收益感知相关的可用性（usability）障碍。鉴于此场景中用户感知的重要性，本研究针对用于辅助抬举动作的背部外骨骼服（back exosuit）开展了基于偏好的控制器优化研究。

考虑到离散运动任务（discrete motor tasks）会带来较高的心理与代谢负荷（metabolic effort），本研究采用了强制选择贝叶斯优化（forced-choice Bayesian Optimization）方法，通过利用辅助参数间最小可觉差（just noticeable difference, JND）的领域知识来提升采样效率。本研究针对两项控制参数进行优化，结果显示受试者内部的偏好参数设置具有一致性，而不同受试者之间的偏好则存在显著差异。整体而言，受试者更偏好不对称的参数配置，即抬举辅助强度高于下放辅助强度，且偏好结果与受试者的人体测量学指标（anthropometrics）密切相关。

[https://doi.org/10.5061/dryad.2z34tmpx5](https://doi.org/10.5061/dryad.2z34tmpx5)

## 数据集与文件结构说明
本数据集包含3个压缩文件：分别用于估计下放增益（lowering gain, LOG）与抬举增益（lifting gain, LIG）最小可觉差（JND）的实验各1个，以及主实验（基于偏好的控制器优化实验）的数据1个。

最小可觉差实验包含一份涵盖所有受试者数据的CSV（Comma-Separated Values）文件，该文件共包含8列（所有参数均无单位），具体如下：
1. 受试者编号（Subject ID）
2. F1：抬举增益（LIG）或下放增益（LOG）方案1
3. F2：抬举增益（LIG）或下放增益（LOG）方案2
4. FB：用户反馈。0/1分别代表受试者感知方案1/2的增益更大
5. 实验区块（Block）
6. LOG/LIG：当前实验区块内保持恒定的另一项控制参数
7. gd：增益差值，即F1与F2的参数差值
8. correct：受试者是否正确识别出增益更大的方案

本偏好优化实验相关数据……