Data_Sheet_1_3D Scanning of the Forearm for Orthosis and HMI Applications.ZIP

The rise of rehabilitation robotics has ignited a global investigation into the human machine interface (HMI) between device and user. Previous research on wearable robotics has primarily focused on robotic kinematics and controls but rarely on the actual design of the physical HMI (pHMI). This paper presents a data-driven statistical forearm surface model for designing a forearm orthosis in exoskeleton applications. The forearms of 6 subjects were 3D scanned in a custom-built jig to capture data in extreme pronation and supination poses, creating 3D point clouds of the forearm surface. Resulting data was characterized into a series of ellipses from 20 to 100% of the forearm length. Key ellipse parameters in the model include: normalized major and minor axis length, normalized center point location, tilt angle, and circularity ratio. Single-subject (SS) ellipse parameters were normalized with respect to forearm radiale-stylion (RS) length and circumference and then averaged over the 6 subjects. Averaged parameter profiles were fit with 3rd-order polynomials to create combined-subjects (CS) elliptical models of the forearm. CS models were created in the jig as-is (CS1) and after alignment to ellipse centers at 20 and 100% of the forearm length (CS2). Normalized curve fits of ellipse major and minor axes in model CS2 achieve R2 values ranging from 0.898 to 0.980 indicating a high degree of correlation between cross-sectional size and position along the forearm. Most other parameters showed poor correlation with forearm position (0.005 < R2 < 0.391) with the exception of tilt angle in pronation (0.877) and circularity in supination (0.657). Normalized RMSE of the CS2 ellipse-fit model ranged from 0.21 to 0.64% of forearm circumference and 0.22 to 0.46% of forearm length. The average and peak surface deviation between the scaled CS2 model and individual scans along the forearm varied from 0.56 to 2.86 mm (subject averages) and 3.86 to 7.16 (subject maximums), with the peak deviation occurring between 45 and 50% RS length. The developed equations allow reconstruction of a scalable 3D model that can be sized based on two user measures, RS length and forearm circumference, or based on generic arm measurements taken from existing anthropometric databases.

康复机器人技术的兴起，引发了全球范围内针对设备与用户之间人机交互界面（Human Machine Interface, HMI）的研究热潮。过往针对可穿戴机器人的研究多聚焦于机器人运动学与控制技术，却极少关注物理人机交互界面（physical HMI, pHMI）的实际设计。本文提出了一种数据驱动的统计前臂表面模型，用于外骨骼应用场景下前臂矫形器的设计。本研究借助定制夹具，对6名受试者的前臂在极端旋前与旋后姿态下进行三维扫描，获取前臂表面的三维点云数据。所得数据被表征为沿前臂长度20%至100%区间的一系列椭圆截面。该模型中的关键椭圆参数包括：归一化长、短半轴长度，归一化中心点位置，倾斜角以及圆度比。单受试者（Single-subject, SS）椭圆参数以前臂桡骨茎突-尺骨茎突（radiale-stylion, RS）长度与前臂围度为基准进行归一化，随后在6名受试者中取平均。将平均后的参数曲线以三阶多项式进行拟合，从而构建合并受试者（Combined-subjects, CS）前臂椭圆模型。本研究构建了两种合并受试者椭圆模型：保持夹具原始状态的CS1模型，以及将椭圆中心对齐至前臂长度20%与100%位置的CS2模型。CS2模型中椭圆长、短半轴的归一化拟合曲线的决定系数（R²）介于0.898至0.980之间，表明前臂截面尺寸与沿前臂的位置之间存在高度相关性。其余多数参数与前臂位置的相关性较弱（0.005 < R² < 0.391），仅旋前状态下的倾斜角（R²=0.877）与旋后状态下的圆度（R²=0.657）例外。CS2椭圆拟合模型的归一化均方根误差（Root Mean Square Error, RMSE）为前臂围度的0.21%至0.64%，以及前臂长度的0.22%至0.46%。缩放后的CS2模型与单受试者扫描数据沿前臂的平均表面偏差与最大表面偏差分别为0.56至2.86毫米（受试者平均值）与3.86至7.16毫米（受试者最大值），峰值偏差出现在RS长度的45%至50%区间内。本研究所提出的方程可用于重建可缩放的三维模型，该模型可基于两项用户测量参数（RS长度与前臂围度）进行尺寸调整，也可依托现有人体测量数据库中的通用手臂测量数据完成尺寸适配。