In recent years, the number of stroke patients has increased, which has led to an increased need for motor rehabilitation exercises. The application of automation devices in the rehabilitation of patients is becoming increasingly developed. These devices help reduce the patient training time and workload. Each type of equipment uses different actuators such as electric motors, pneumatic cylinders, and hydraulic cylinders. Different from previous studies, in this paper, a research model of lower extremity rehabilitation devices using a simple actuator which is a linear actuator is presented. The main contribution of this study is that a dynamic model of the linear actuator associated with the action dynamics of the lower extremities of the studied model with three degrees of freedom was established. A mathematical model for this actuator is also presented, simulating the response of the mechanism using the MATLAB/Simulink software. A lower extremity rehabilitation device using a linear actuator was manufactured and tested. The test results for 10 volunteers show that the actuator works and causes miniature angle errors. This experiment demonstrates that the linear actuator can be used in stroke patient rehabilitation equipment and contributes to the diversification of the mechanism in automation equipment.
LanghornePRamachandraS. Organised inpatient (stroke unit) care for stroke: network meta-analysis. Cochrane Database Syst Rev2020; 4: 1–119.
2.
GeedSFeitPEdwardsDF, et al.Why are stroke rehabilitation trial recruitment rates in single digits?Front Neurol2021; 12: 1–9.
3.
KārkliņaAChenEBērziņaG, et al.Patients’ physical activity in stroke units in Latvia and Sweden. Brain Behav2021; 11: 1–8.
4.
PinterMMBraininM. Rehabilitation after stroke in older people. Maturitas2011; 71: 104–108.
5.
WinsteinCJSteinJArenaR, et al.Guidelines for adult stroke rehabilitation and recovery. Stroke2016; 47: 1–73.
6.
CramerSCWolfSLAdamsHP, et al.Stroke recovery and rehabilitation research. Stroke2017; 48: 813–819.
7.
LeeaKBLimbSHKimcKH, et al.Six-month functional recovery of stroke patients. Int J Rehabil Res2015; 38: 173–180.
8.
PuigJBlascoGSchlaugG, et al.Diffusion tensor imaging as a prognostic biomarker for motor recovery and rehabilitation after stroke. Neuroradiology2021; 59: 343–351.
9.
ColemanERMoudgalRLangK, et al.Early rehabilitation after stroke: a narrative review. Curr Atheroscler Rep2017; 19: 1–12.
WeberLMSteinJ. The use of robots in stroke rehabilitation: a narrative review. NeuroRehabilitation2018; 43: 99–110.
12.
ChangWHKimaY-H. Robot-assisted therapy in stroke rehabilitation. J Stroke2013; 15: 174–181.
13.
KeelingABPiitzMSemrauJA, et al.Robot enhanced stroke therapy optimizes rehabilitation (RESTORE): a pilot study. J NeuroEng Rehabil2021; 18: 1–16.
14.
PoliPMoroneGRosatiG, et al. Robotic technologies and rehabilitation: new tools for stroke patients’ therapy. BioMed Res Int2013; 2013: 1–8.
15.
JinXCuiXAgrawalSK. Design of a cable-driven active leg exoskeleton (C-ALEX) and gait training experiments with human subjects. In: International conference on robotics and automation, Washington State Convention Center Seattle, Washington, 26–30 May 2015, pp. 5578–5583. New York: IEEE.
16.
YoonJNovandyBYoonC-H, et al.A 6-DOF gait rehabilitation robot with upper and lower limb connections that allows walking velocity updates on various terrains. IEEE/ASME Trans Mechatron2010; 15: 201–215.
17.
EiammanussakulTSangveraphunsiriV. Lower limb rehabilitation robot in sitting position for various therapeutic exercises. In: International conference on bioinformatics and biomedical technology – ICBBT’17, Lisbon, Portugal, May 14–16, 2017, pp. 112–116. New York: ACM.
18.
WangHLinMJinZ, et al.Virtual reality training system based on lower limb rehabilitation robot. Int J Eng Technol (UAE)2018; 7: 119–122.
19.
AliffMDaniealFMohamedMF, et al. Development of flexible pneumatic rehabilitation actuator for knee injury. Test Eng Manag2020; 83: 12849–12855.
20.
PetreIDeaconescuARogozeaL, et al. Orthopaedic rehabilitation device actuated with pneumatic muscles. Int J Adv Robot Syst2011; 11: 1–8.
21.
GoergenRValdieroACRasiaLA, et al. Development of a pneumatic exoskeleton robot for lower limb rehabilitation. In: 16th international conference on rehabilitation robotics (ICORR), Toronto, Canada, June 24–28, 2019, pp. 7–11. New York: IEEE.
22.
GlowinskiSObstMMajdanikS, et al. Dynamic model of a humanoid exoskeleton of a lower limb with hydraulic actuators. Sensors2021; 21: 1–20.
23.
YangYMaLHuangD, et al. Sampled-data repetitive learning control of electro-hydraulic actuator used in lower limb exoskeleton for rehabilitation based on Euler approximation. In: Data driven control and learning systems conference (DDCLS), Chongqing, China, 26–27 May, 2017, pp. 231–236. New York: IEEE.
24.
YuYLiangW, Design optimization for lower limb assistive mechanism based on manipulability inclusive principle. In: International conference on robotics and biomimetics (ROBIO), Guangzhou, China, 11–14 December, 2012, pp. 174–180. New York: IEEE.
25.
TodaHSugiharaS.Ankle joint stretching device using tension rod for self rehabilitation. J Robot Mechatron2021; 33: 843–850.
26.
Mao W-LSupraptoHungC-W.Adaptive neural network–based synchronized control of dual-axis linear actuators. Adv Mech Eng2016; 8: 1–17.
27.
StopforthR.Customizable rehabilitation lower limb exoskeleton system. Int J Adv Rob Syst2012; 9: 1–7.
28.
ZhangLLiJDongM, et al. Design and workspace analysis of a parallel ankle rehabilitation robot (PARR). J Healthc Eng 2019; 2019: 1–10.
29.
JazarRN.Theory of applied robotics, 2nd ed. New York: Springer, 2010, p. 50.
30.
TaghvaeiSTavasoliAFeiziN, et al. A control-oriented dynamic model for sit-to-stand motion with fixed support. Proc IMechE Part K: J Multi-Body Dyn2017; 232: 1–9.
31.
HashemiEKhajepourA.Kinematic and threedimensional dynamic modeling of a biped robot. Proc IMechE Part K: J Multi-Body Dyn2016; 231: 1–17.
32.
XuJLiYXuL, et al. A multi-mode rehabilitation robot with magnetorheological actuators based on human motion intention estimation. IEEE Trans Neural Syst Rehabil Eng2019; 27: 2216–2228.
33.
LinC-YLeeC-H.Remote servo tuning system for multi-axis CNC machine tools using a virtual machine tool approach. Appl Sci2017; 7: 1–21.
34.
LeeHLeeJRyuJ-H, et al. Relaxing the conservatism of passivity condition for impedance controlled series elastic actuators. In: International conference on intelligent robots and systems (IROS), Macau, China, November 4–8, 2019, pp. 7610–7615. New York: IEEE.
35.
LeeL-WLiI-HLuL-Y, et al. Hardware development and safety control strategy design for a mobile rehabilitation robot. Appl Sci2022; 12: 1–25.
36.
RoyGSajiHJDasS, et al. Modeling and control of electric linear actuator for driving knee joint of a lower body exoskeleton. Adv Instrum Control Appl Syst Appl2020; 4: 1–18.
37.
TianJZhouYYangL, et al. Analysis of stick-slip reduction for a new torsional vibration tool based on PID control. Proc IMechE Part K: J Multi-Body Dyn2021; 235: 164–175.
