摘要
由一位新闻记者兼机器人与机器学习的工作人员新闻编辑每日新闻-关于机器人的新研究结果已经公布。据《中华人民共和国连云港消息》编辑报道，该研究称：“机器人技术在康复医学，特别是手部康复领域显示出广阔的应用前景。手部功能在日常生活中起着不可忽视的重要作用，其关键特性体现在多个层面。”新闻编辑从南京医科大学的研究中得到一句话:“从基本生活技能到职业需求,健康的手功能是不可缺少的。手功能是日常生活技能的基础,包括自我照顾、饮食、穿衣、饮食等活动。”本文提出了一种基于Ycbcr颜色空间和卷积神经网络的手势识别算法,该算法首先提取手势图像并通过转换后的图像进行识别,然后设计了一种基于Ycbcr和CNN的功能康复训练机器人。实验证实，当数据集大小为500时，YOLOV3、YOLOV3-SPP、YOLOV4和混合算法的信噪比分别为27.5dB、3 2.7dB、34.8db和41.2db。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on robotics have bee n published. According to news originating from Lianyungang, People’s Republic o f China, by NewsRx editors, the research stated, “Robot technology shows broad a pplication prospects in rehabilitation medicine, especially in hand rehabilitati on. Hand function plays an important role that cannot be ignored in daily life, and its key properties are reflected in multiple levels.” The news editors obtained a quote from the research from Nanjing Medical Univers ity: “From basic life skills to occupational needs, healthy hand function is ind ispensable. Hand function is the foundation for performing daily life skills, in cluding activities such as self-care, eating, dressing, and grooming. The abilit y to freely use hand functions is directly related to an individual’s quality of life and independence. This study proposed a gesture recognition algorithm by f using Ycbcr color space and convolutional neural network. The method first conve rted gesture images and recognizes them through the converted images. Then, a ha nd function rehabilitation training robot based on Ycbcr and CNN was designed, w hich provided rehabilitation treatment for patients with impaired hand function. These experiments confirmed that when the data set size was 500, the signal-to- noise ratios of YOLOV3, YOLOV3-SPP, YOLOV4, and hybrid algorithms were 27.5dB, 3 2.7dB, 34.8dB, and 41.2dB, respectively.”
NETL
NSTL