浏览全部资源
扫码关注微信
1.宁夏大学电子与电气工程学院,宁夏 银川 750021
2.宁夏大学信息工程学院,宁夏 银川 750021
3.宁夏大学“东数西算”人工智能与信息安全重点实验室,宁夏 银川 750021
[ "罗志成(1996‒ ),男,宁夏大学硕士生,主要研究方向为穿戴式医学电子设备、信号处理等。" ]
[ "田宁(1999‒ ),男,宁夏大学硕士生,主要研究方向为穿戴式医学电子设备、智能控制算法、功能性电刺激等。" ]
[ "杨光(1997‒ ),男,宁夏大学硕士生,主要研究方向为穿戴式医学电子设备、深度学习、模式识别等。" ]
[ "秦飞舟(1972‒ ),女,宁夏大学教授,主要研究方向为智能自动化应用、数据库技术、计算机应用开发等。" ]
[ "鲍学亮(1986‒ ),男,博士,宁夏大学副教授,主要研究方向为智能康复医学电子仪器、神经工程、人工智能、物联网应用等。" ]
纸质出版日期:2024-06-10,
收稿日期:2024-03-26,
修回日期:2024-06-15,
移动端阅览
罗志成,田宁,杨光等.面向瘫痪肢体功能康复的无线多通道混合数据采集系统[J].物联网学报,2024,08(02):103-115.
LUO Zhicheng,TIAN Ning,YANG Guang,et al.Wireless multichannel hybrid data acquisition system for functional rehabilitation of paralyzed limbs[J].Chinese Journal on Internet of Things,2024,08(02):103-115.
罗志成,田宁,杨光等.面向瘫痪肢体功能康复的无线多通道混合数据采集系统[J].物联网学报,2024,08(02):103-115. DOI: 10.11959/j.issn.2096-3750.2024.00399.
LUO Zhicheng,TIAN Ning,YANG Guang,et al.Wireless multichannel hybrid data acquisition system for functional rehabilitation of paralyzed limbs[J].Chinese Journal on Internet of Things,2024,08(02):103-115. DOI: 10.11959/j.issn.2096-3750.2024.00399.
设计了一款无线多通道混合数据采集系统,包含硬件系统和PC数据采集及分析处理软件系统两部分。硬件系统根据目标需求,通过按键设置体表肌电(sEMG
surface electromyography)信号采集通道数和惯性测量单元(IMU
inertial measurement unit)数量,基于FreeRTOS操作系统创建sEMG信号传输任务和与IMU传感器数量对应的运动学数据采集任务,采用互斥信号量技术确保各任务获取的数据通过Wi-Fi传输给PC软件系统;PC软件系统实现对获取的混合信号实时显示、存储、处理分析等。相比于当下商用Trigno无线基础混合信号采集系统和其他自研系统,所设计的混合数据采集系统在瘫痪肢体运动功能康复领域更实用、具有更强的扩展性。
A wireless multi-channel hybrid data acquisition system was presented
consisting of two distinct components: a hardware system and a PC data acquisition analysis and processing software system. The hardware system was set with the number of surface electromyography (sEMG) signal acquisition channels and the number of inertial measurement unit (IMU) according to the target requirements by pressing a key
and sEMG signal transmission tasks and kinematics data acquisition tasks corresponding to the number of IMUs were created based on the FreeRTOS operating system
and the mutually exclusive signal volume technique was used to ensure that the data acquired by each task were transmitted to the PC software system via Wi-Fi. The data acquisition tasks were created based on the FreeRTOS operating system
and the kinematics tasks corresponding to the number of IMU sensors were created
and the mutually exclusive signal volume technique was used to ensure that the data acquired by each task was transmitted to the PC software system via Wi-Fi. The designed hybrid data acquisition system is more practical and scalable in the field of motor function rehabilitation for paralyzed limbs compared to the current commercial Trigno wireless based mixed signal acquisition system and other self-developed systems.
混合数据采集肌电信号IMU传感器无线多通道肌肉疲劳监测
hybrid data acquisitionsEMG signalIMU sensorwireless multichannelmuscle fatigue monitoring
PARK H K, JUNG J, LEE D W, et al. A wearable electromyography-controlled functional electrical stimulation system improves balance, gait function, and symmetry in older adults[J]. Technology and Health Care: Official Journal of the European Society for Engineering and Medicine, 2022, 30(2): 423-435.
XU R, ZHANG H C, ZHAO X Y, et al. Symmetrical contralaterally controlled functional electrical stimulation enhanced cortical activity and synchronization of stroke survivors[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering: a Publication of the IEEE Engineering in Medicine and Biology Society, 2023, 31: 2287-2295.
GHASEMZADEH H, JAFARI R, PRABHAKARAN B. A body sensor network with electromyogram and inertial sensors: multimodal interpretation of muscular activities[J]. IEEE Transactions on Information Technology in Biomedicine: a Publication of the IEEE Engineering in Medicine and Biology Society, 2010, 14(2): 198-206.
JUNG J, LEE D W, SON Y, et al. Volitional EMG controlled wearable FES system for lower limb rehabilitation[C]//Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Piscataway: IEEE Press, 2021: 7099-7102.
JIANG Y J, SONG L, ZHANG J M, et al. Multi-category gesture recognition modeling based on sEMG and IMU signals[J]. Sensors, 2022, 22(15): 5855.
TEPE C, ERDIM M. Classification of surface electromyography and gyroscopic signals of finger gestures acquired by Myo armband using machine learning methods[J]. Biomedical Signal Processing and Control, 2022, 75: 103588.
ARANTES A P B B, BRESSAN N. Classification of muscle inertial motion and electromyographic activity integration to improve accuracy in pattern recognition[J]. JPO Journal of Prosthetics and Orthotics, 2021, 35(2): 83-91.
TAO W, YINGNIAN W, RUI Y, et al. Research on real-time gesture classification algorithm based on IMU and sEMG mixed signals[J]. Journal of System Simulation, 2023, 35(2): 359-371.
XU R, ZHAO X Y, WANG Z Y, et al. A co-driven functional electrical stimulation control strategy by dynamic surface electromyography and joint angle[J]. Frontiers in Neuroscience, 2022, 16: 909602.
鲍学亮. 基于肌电桥的瘫痪肢体运动功能重建实验研究和下肢可穿戴式肌电桥系统设计[D]. 南京: 东南大学, 2018.
BAO X L. EMGB-Based experimental study of motor function rebuilding for paralyzed limbs and system design of wearable EMGB for lower limbs[D]. Nanjing: Southeast University, 2018.
YEOM H, CHANG Y H. Autogenic EMG-controlled functional electrical stimulation for ankle dorsiflexion control[J]. Journal of Neuroscience Methods, 2010, 193(1): 118-125.
KINUGASA R, KUBO S. Development of consumer-friendly surface electromyography system for muscle fatigue detection[J]. IEEE Access, 2023(1): 6394-6403.
BENALCÁZAR M E, JARAMILLO A G, JONATHAN, et al. Hand gesture recognition using machine learning and the Myo armband[C]//Proceedings of the 2017 25th European Signal Processing Conference (EUSIPCO). Piscataway: IEEE Press, 2017: 1040-1044.
LAVADO D M, VELA E A. A wearable device based on IMU and EMG sensors for remote monitoring of elbow rehabilitation[C]//Proceedings of the 2022 E-Health and Bioengineering Conference (EHB). Piscataway: IEEE Press, 2022: 1-4.
BIAGETTI G, CRIPPA P, FALASCHETTI L, et al. A portable wireless sEMG and inertial acquisition system for human activity monitoring[C]//ROJAS I, ORTUÑO F. International Conference on Bioinformatics and Biomedical Engineering. Cham: Springer, 2017: 608-620.
BIAGETTI G, CRIPPA P, FALASCHETTI L, et al. A multi-channel electromyography, electrocardiography and inertial wireless sensor module using bluetooth low-energy[J]. Electronics, 2020, 9(6): 934.
BASSANI G, FILIPPESCHI A, GRAZIANO A, et al. A wearable device to assist the evaluation of workers health based on inertial and sEMG signals[C]//Proceedings of the 2021 29th Mediterranean Conference on Control and Automation (MED). Piscataway: IEEE Press, 2021: 669-674.
LI Y R, ZHANG X, GONG Y N, et al. Motor function evaluation of hemiplegic upper-extremities using data fusion from wearable inertial and surface EMG sensors[J]. Sensors, 2017, 17(3): 582.
KHAN M A, BAYRAM B M, DAS R, et al. Electromyography and inertial motion sensors based wearable data acquisition system for stroke patients: a pilot study[C]//Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Piscataway: IEEE Press, 2021: 6953-6956.
BIAGETTI G, CRIPPA P, FALASCHETTI L, et al. Human activity monitoring system based on wearable sEMG and accelerometer wireless sensor nodes[J]. Biomedical Engineering Online, 2018, 17(S1): 132.
MI C, ZHOU T T, WEI B, et al. Design of high-accuracy eight-channel surface electromyography acquisition system and its application[J]. The European Physical Journal Special Topics, 2018, 227(7): 933-942.
YANG Y H, RUAN S J, CHEN P C, et al. A low-cost wireless multichannel surface EMG acquisition system[J]. IEEE Consumer Electronics Magazine, 2020, 9(5): 14-19.
TIAN N, CHEN G, ZHOU Y, et al. A type-2 fuzzy PID-based system of surface FES with pulse width modulation for stroke rehabilitation[C]//Proceedings of the 2023 4th International Conference on Computer Engineering and Intelligent Control (ICCEIC). Piscataway: IEEE Press, 2023: 63-68.
郭旭阳, 王洁, 罗玲, 等. 空气呼吸器对志愿消防员肌肉疲劳的影响研究[J]. 安全, 2024, 45(1): 81-85.
GUO X Y, WANG J, LUO L, et al. Study on the effect of carrying air respirators on muscle fatigue of volunteer firefighters[J]. Safety & Security, 2024, 45(1): 81-85.
MA’AS M D F, MASITOH, AZMI A Z U, et al. Real-time muscle fatigue monitoring based on Median frequency of electromyography signal[C]//Proceedings of the 2017 5th International Conference on Instrumentation, Control, and Automation (ICA). Piscataway: IEEE Press, 2017: 135-139.
CHESLER N C, DURFEE W K. Surface EMG as a fatigue indicator during FES-induced isometric muscle contractions[J]. Journal of Electromyography and Kinesiology: Official Journal of the International Society of Electrophysiological Kinesiology, 1997, 7(1): 27-37.
KIM Y, LEE S R, KIM S, et al. Toward sustainable and accessible mobility: a functional electrical stimulation-based robotic bike with a fatigue-compensation algorithm and mechanism for cybathlon 2020[J]. IEEE Robotics & Automation Magazine, 2021, 28(4): 32-42.
LUO S Y, XU H N, ZUO Y, et al. A review of functional electrical stimulation treatment in spinal cord injury[J]. Neuromolecular Medicine, 2020, 22(4): 447-463.
AHMAD M K I, SHAMSUDIN A U, SOOMRO Z A, et al. Closed-loop functional electrical stimulation (FES)-cycling rehabilitation with phase control Fuzzy Logic for fatigue reduction control strategies for stroke patients[J]. Jurnal Ilmiah SINERGI , 28(1): 63-74.
IBITOYE M O, HAMZAID N A, ABDUL WAHAB A K, et al. Quadriceps mechanomyography reflects muscle fatigue during electrical stimulus-sustained standing in adults with spinal cord injury - a proof of concept[J]. Biomedizinische Technik Biomedical Engineering, 2020, 65(2): 165-174.
NAEEM J, HAMZAID N A, AZMAN A W, et al. Electrical stimulator with mechanomyography-based real-time monitoring, muscle fatigue detection, and safety shut-off: a pilot study[J]. Biomedizinische Technik, 2020, 65(4): 461-468.
0
浏览量
2
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构