[1] |
崔迪. 虚拟环境下远程自适应康复训练系统与评估模型研究[D]. 宁波: 中国科学院大学(中国科学院宁波材料技术与工程研究所), 2017.
|
|
CUI Di. Research on adaptability and evaluation model of remote rehabilitation training system in virtual environment[D]. Ningbo: Ningbo Institute of Material Technology, Chinese Academy of Sciences, 2017.
|
[2] |
LUNENBURGER L, WELLNER M, BANZ R, et al. Virtual Performance-Enhancing Reality (ViPER) for robot-assisted gait training[C]//2006 International Workshop on Virtual Rehabilitation. New York, USA: IEEE, 2006: 174-177.
|
[3] |
耿松松. 中国残疾人康复现状与问题研究[D]. 兰州: 兰州大学, 2013.
|
|
GENG Songsong. Rehabilitation and problems on disabled in China[D]. Lanzhou: Lanzhou University, 2013.
|
[4] |
KOMATIREDDY R, CHOKSHI A, BASNETT J, et al. Quality and quantity of rehabilitation exercises delivered by a 3-D motion controlled camera: A pilot study[J]. International Journal of Physical Medicine & Rehabilitation, 2014, 2(4): 214.
|
[5] |
JOHANSSON B B. Current trends in stroke rehabilitation. A review with focus on brain plasticity[J]. Acta Neurologica Scandinavica, 2011, 123(3): 147-159.
doi: 10.1111/j.1600-0404.2010.01417.x
pmid: 20726844
|
[6] |
TEYHEN D S, SHAFFER S W, LORENSON C L, et al. The functional movement screen: A reliability study[J]. The Journal of Orthopaedic & Sports Physical Therapy, 2012, 42(6): 530-540.
|
[7] |
DEAKIN A, HILL H, POMEROY V M. Rough guide to the Fugl-Meyer assessment: Upper limb section[J]. Physiotherapy, 2003, 89(12): 751-763.
doi: 10.1016/S0031-9406(05)60502-0
URL
|
[8] |
ZHANG Z, FANG Q, GU X D. Objective assessment of upper-limb mobility for poststroke rehabilitation[J]. IEEE Transactions on Bio-medical Engineering, 2016, 63(4): 859-868.
doi: 10.1109/TBME.2015.2477095
pmid: 26357394
|
[9] |
汤翾, 黄襄念, 周杉. 基于Kinect的肩周炎康复训练动作识别系统研究[J]. 现代计算机(专业版), 2014(23): 53-55.
|
|
TANG Xuan, HUANG Xiangnian, ZHOU Shan. Research on the frozen rehabilitation training action recognition system based on kinect[J]. Modern Computer, 2014(23): 53-55.
|
[10] |
杨文璐, 王杰, 夏斌, 等. 基于Kinect的下肢体康复动作评估系统[J]. 传感器与微系统, 2017, 36(1): 91-94.
|
|
YANG Wenlu, WANG Jie, XIA Bin, et al. Assessment system of lower limb rehabilitation action based on Kinect[J]. Transducer & Microsystem Technologies, 2017, 36(1): 91-94.
|
[11] |
吴齐云, 战荫伟, 邵阳. 基于DTW和K-means的动作匹配和评估[J]. 电子技术应用, 2016, 42(8): 141-143.
|
|
WU Qiyun, ZHAN Yinwei, SHAO Yang. Human motion matching and evaluation based on STDTW and K-means[J]. Application of Electronic Technique, 2016, 42(8): 141-143.
|
[12] |
HOUMANFAR R, KARG M, KULIĆ D. Movement analysis of rehabilitation exercises: Distance metrics for measuring patient progress[J]. IEEE Systems Journal, 2016, 10(3): 1014-1025.
doi: 10.1109/JSYST.2014.2327792
URL
|
[13] |
SU C J, CHIANG C Y, HUANG J Y. Kinect-enabled home-based rehabilitation system using Dynamic Time Warping and fuzzy logic[J]. Applied Soft Computing, 2014, 22: 652-666.
doi: 10.1016/j.asoc.2014.04.020
URL
|
[14] |
CAPECCI M, CERAVOLO M G, FERRACUTI F, et al. A Hidden Semi-Markov Model based approach for rehabilitation exercise assessment[J]. Journal of Biomedical Informatics, 2018, 78: 1-11.
doi: S1532-0464(17)30282-4
pmid: 29277330
|
[15] |
LIAO Y L, VAKANSKI A, XIAN M. A deep learning framework for assessing physical rehabilitation exercises[J]. IEEE Transactions on Neural Systems & Rehabilitation Engineering, 2020, 28(2): 468-477.
|
[16] |
AR I, AKGUL Y S. A computerized recognition system for the home-based physiotherapy exercises using an RGBD camera[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering: A Publication of the IEEE Engineering in Medicine & Biology Society, 2014, 22(6): 1160-1171.
|
[17] |
闫航, 陈刚, 崔莉亚, 等. 基于单目视觉的在线人体康复动作识别[J]. 计算机应用与软件, 2021, 38(2): 171-178.
|
|
YAN Hang, CHEN Gang, CUI Liya, et al. Online human rehabilitation action recognition based on monocular vision[J]. Computer Applications & Software, 2021, 38(2): 171-178.
|
[18] |
LIN J F S, KULIĆ D. Online segmentation of human motion for automated rehabilitation exercise analysis[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering: A Publication of the IEEE Engineering in Medicine & Biology Society, 2014, 22(1): 168-180.
|
[19] |
李航. 统计学习方法[M]. 第2版. 北京: 清华大学出版社, 2019.
|
|
LI Hang. Statistical learning methods[M]. 2nd ed. Beijing: Tsinghua University Press, 2019.
|
[20] |
HODA M, HODA Y, HAGE A, et al. Cloud-based rehabilitation and recovery prediction system for stroke patients[J]. Cluster Computing, 2015, 18(2): 803-815.
doi: 10.1007/s10586-015-0448-6
URL
|
[21] |
VAKANSKI A, JUN H P, PAUL D, et al. A data set of human body movements for physical rehabilitation exercises[J]. Data, 2018, 3(1): 2.
doi: 10.3390/data3010002
URL
|