Data Fusion of Human Skeleton Joint Tracking Using Two Kinect Sensors and Extended Set Membership Filter
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摘要: 以Kinect为代表的深度图像传感器在肢体康复系统中得到广泛应用.单一深度图像传感器采集人体关节点数据时由于肢体遮挡、传感器数据错误和丢失等原因降低系统可靠性.本文研究了利用两台Kinect深度图像传感器进行数据融合从而达到消除遮挡、数据错误和丢失的目的，提高康复系统中数据的稳定性和可靠性.首先，利用两台Kinect采集患者健康侧手臂运动数据；其次，对两组数据做时间对准、Bursa线性模型下的坐标变换和基于集员滤波的数据融合；再次，将融合后的健康侧手臂运动数据经过“镜像运动”作为患侧手臂运动指令；最后，将患侧运动指令下发给可穿戴式镜像康复外骨骼带动患者患侧手臂完成三维动画提示的康复动作，达到患者主动可控康复的目的.本文通过Kinect与VICON系统联合实验以及7自由度机械臂控制实验验证了数据融合方法的有效性，以及两台Kinect可有效解决上述问题.
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关键词:
- 镜像康复 /
- Kinect深度图像传感器 /
- Bursa线性模型 /
- 集员滤波
Abstract: Kinect-like depth sensors are widely used in rehabilitation systems. However, using a single depth sensor is less reliable due to limb blocking, data-loss or error. This paper uses two Kinect sensors and a data fusion algorithm to solve these problems. Firstly, two Kinect sensors are used to capture the motion data of the healthy side arm of the hemiplegic patient. Secondly, the data is processed with time alignment, then coordinates are transformed with Bursa transform and data fusion is done using extended set membership filter (ESMF) successively. Then, the motion data is mirrored by the middle plane, namely "mirror motion". In the end, the mirrored motion data controls the wearable robotic arm to drive the patient's paralytic side arm so as to interactively and initiatively complete a variety of recovery actions prompted by computer with 3D animation games. The effectiveness of the proposed approach is validated by both experiments on Kinect sensors & VICON and a 7 DOF manipulator. Also, two Kinect sensors can solve those problems effectively.1) 本文责任编委 程龙 -
图 2 镜像运动康复系统的Kinect布置示意图及各坐标系
Fig. 2 Coordinate system and the layout of Kinect sensors of the mirror movements rehabilitation system
图 5 副Kinet#2的坐标系与主Kinect#1沿其$Y$轴旋转90$^{\circ}$之后的坐标系之间满足小角度旋转假设
Fig. 5 The coordinate of Kinect#2 should meet the small angle rotation condition with the coordinate of Kinect#1 after it rotates 90$^{\circ}$ according to its $Y$ axis
图 6 Kinect传感器追踪的三个手臂关节点及其三个关节角
Fig. 6 3 joints and the 3 joint angles tracked by Kinect sensors
图 10 实验现场对两台Kinect做空间对准的场景
Fig. 10 Experiment scene of registration of the two Kinect sensors
图 11 VICON和Kinect同时追踪肢体动作
Fig. 11 VICON and Kinect sensors tracking the limb joints synchronously
图 13 VICON和Kinect采集的手掌点运动数据对比
Fig. 13 The movement data of hand from VICON system and Kinect sensor
图 14 系统误差补偿后的Kinect数据与VICON数据
Fig. 14 The data from Kinect sensor and data from VICON system using system error compensation
表 1 Kinect人体关节点数据误差
Table 1 The error of Kinect sensor for joints tracking
坐标 均值(mm) 方差(mm) 区间(mm) 直角系x -15.4 45.6 [-200 200] 直角系y -36.1 38.7 [-150 150] 直角系z 16 54.1 [-200 200] 球面系r 14.3 54.5 [-200 200] 球面系φ 0.01 0.03 [-0.1 0.1] 球面系θ 0.19 0.6 [-1 1] 
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表 2 数据融合后Kinect人体关节点数据误差
Table 2 The error of Kinect sensor after data fusion
坐标 均值(mm) 方差(mm) 直角系x 0.0 36.7 直角系y 0.0 34.9 直角系z 0.0 40.6 球面系r 0.8 38.4 球面系φ 0.00 0.02 球面系θ 0.02 0.60
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