[1] 明东,蒋晟龙,王忠鹏,等. 基于人机信息交互的助行外骨骼机器人技术进展[J]. 自动化学报,2017,43(7):1089-1100. MING Dong,JIANG Shenglong,WANG Zhongpeng,et al. Advances in technology of walking exoskeleton robot based on human-machine information interaction[J]. Journal of Automation,2017,43(7):1089-1100. [2] REN F,BAO Y. A review on human-computer interaction and intelligent robots[J]. International Journal of Information Technology & Decision Making,2020,19(1):5-47. [3] 路知远. 穿戴式健康监护及人机交互应用中若干关键技术研究[D]. 合肥:中国科学技术大学,2014. LU Zhiyuan. Research on key technologies in wearable health monitoring and human-computer interaction applications[D]. Hefei:University of Science and Technology of China,2014. [4] XU P. A real-time hand gesture recognition and human-computer interaction system[J]. arXiv preprint arXiv:1704.07296,2017. [5] VULETIC T,DUFFY A,HAY L,et al. Systematic literature review of hand gestures used in human computer interaction interfaces[J]. International Journal of Human-Computer Studies,2019,129:74-94. [6] PIRONDINI E,COSCIA M,MARCHESCHI S,et al. Evaluation of a new exoskeleton for upper limb post-stroke neuro-rehabilitation:Preliminary results[C]//2nd International Conference on NeuroRehabilitation (ICNR),June 24-26,2014,Aalborg,Denmark:Springer,2014:637-645. [7] LI Guanglin,SCHULTZ A E,KUIKEN T A. Quantifying pattern recognition-Based myoelectric control of multifunctional transradial prostheses[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering,2010,18(2):185-192. [8] BHAGWAT S,MUKHERJI P. Electromyogram (EMG) based fingers movement recognition using sparse filtering of wavelet packet coefficients[J]. Sādhanā,2020,45(1):1-11. [9] 熊安斌,丁其川,赵新刚,等. 基于单通道sEMG分解的手部动作识别方法[J]. 机械工程学报,2016,52(7):6-13. XIONG Anbin,DING Qichuan,ZHAO Xingang,et al. Classification of hand gestures based on single-channel sEMG decomposition[J]. Journal of Mechanical Engineering,2016,52(7):6-13. [10] ZHAI Xiaolong,JELFS B,CHAN RHM,et al. Self-recalibrating surface EMG pattern recognition for neuroprosthesis control based on convolutional neural network[J]. Frontiers in Neuroscience,2017,11:379. [11] RAHIMIAN E,ZABIHI S,ATASZAR S F,et al. Surface EMG-based hand gesture recognition via hybrid and dilated deep neural network architectures for neurorobotic prostheses[J]. Journal of Medical Robotics Research,2020,5:1. [12] REHMAN MZU,WARIS A,GILANI S O,et al. Multiday EMG-based classification of hand motions with deep learning techniques[J]. Sensors,2018,18(8):1. [13] BUONGIORNO D,BARSOTTI M,BARONE F,et al. A linear approach to optimize an EMG-driven neuromusculoskeletal model for movement intention detection in myo-control:A case study on shoulder and elbow joints[J]. Frontiers in Neurorobotics,2018,12:1. [14] SARASOLA-SANZ A,IRASTORZA-LANDA N,LOPEZ-LARRAZ E,et al. A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients[C]//Institute of Electrical and Electronics Engineers. International Conference on Rehabilitation Robotics (ICORR),July 17-20,2017,London,IEEE,2017:895-900. [15] FANG Bin. A novel interface device developed based on mrt for prosthetic hand[C]//Institute of Electrical and Electronics Engineers. Chinese Automation Congress (CAC),November 06-08,2020,Shanghai:IEEE,2020,2163-2168. [16] PAN S J,TSANG I W,KWOK J T,et al. Domain adaptation via transfer component analysis[J]. IEEE Transactions on Neural Networks,2011,22(2):199-210. |