Next Article in Journal
Design and Experimental Tests of a Buoyancy Change Module for Autonomous Underwater Vehicles
Next Article in Special Issue
Design of Guided Bending Bellows Actuators for Soft Hand Function Rehabilitation Gloves
Previous Article in Journal
Design and Testing of Accurate Dicing Control System for Fruits and Vegetables
Previous Article in Special Issue
Effect of Wearing Running Shoes on Lower Limb Kinematics by Using OpenSim Simulation Software
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Robotic Knee Prosthesis with Cycloidal Gear and Four-Bar Mechanism Optimized Using Particle Swarm Algorithm

1
Center for Applied Biomechanics, Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
2
The Chancellery, Universiti Tenaga Nasional, Kajang 43000, Malaysia
*
Author to whom correspondence should be addressed.
Actuators 2022, 11(9), 253; https://doi.org/10.3390/act11090253
Submission received: 22 July 2022 / Revised: 28 August 2022 / Accepted: 29 August 2022 / Published: 1 September 2022
(This article belongs to the Special Issue Soft Exoskeleton and Supernumerary Limbs for Human Augmentation)

Abstract

:
A powered transfemoral prosthesis is needed as people with transfemoral amputation show 60 percent extra metabolic cost when compared to people with no amputation. Recently, as illustrated in the literature, the most high-torque robotic knee prosthesis utilize harmonic reducers. Despite the advantage of high reduction ratio and efficiency, the harmonic drive cannot be back-driven. Therefore, the harmonic drive is not an optimal solution for prosthetic systems with direct and indirect contact with the environment. In this paper, we outline an initial design of robotic knee prosthesis. The proposed robotic knee prosthesis consists of BLDC motor, cycloidal gear with reduction ratio 13:1, four-bar mechanism, and timing belt transmission with 4:1 reduction ratio. To optimize the torque transmission and range of motion (RoM), a multiobjective optimization problem must be undertaken. The end-effector motion depends on each bar length in the four-bar mechanism. The four-bar mechanism was optimized using particle swarm optimization (PSO). To complete the optimization, a set of 50 steps was collected using wearable sensors. Then, the data of sagittal plan were processed to identify the target profile for PSO. The prototype’s computer-aided manufacturing (CAM) was completed using a MarkTwo 3D printer with carbon fiber composite. The overall design can achieve a maximum torque of 84 N.m. However, the current design lacks the elastic component (no spring is added on the actuator output), which is necessary for a functional prosthesis; this limitation will be addressed in future study.

1. Introduction

The knee complex is a limited condyloid joint. It consists of two joints with three degrees of freedom (DoF) (tibiofemoral and patellofemoral joints). Tibiofemoral joint functions mostly as a hinge joint with a slight rotation (i.e., adduction/abduction and internal/external), whereas patellofemoral joint main function is as a knee extension with the mechanical advantage of increasing the leverage of patellar tendon, which maximizes the knee torque [1]. Knee full-range of flexion is between 130° and 160°. However, in daily activities, the range drops to a value between 60° and 70° when walking, 80° while ascending stairs, and 90° from sitting to standing. Moreover, knee full-range of extension is 5° [1]. The knee joint’s peak moment occurs at early stance [2]; the knee complex generates from 0.3 to 0.7 and 1.2 to 1.7 N.m/kg when walking and running, respectively. By establishing the requirements of torque and RoM, we can discuss the weight limitation for a functional robotic knee prosthesis.
Subatmospheric suspension can provide a vacuum range between 0 and −8 inHg. During the swing phase, the fictitious force acts on the socket [3]. Therefore, the momentum should be minimized by decreasing the prosthesis mass. The knee joint prosthesis weight would be between 0.5 and 1.6 kg. However, the size of the knee prosthesis is not critical for the design configuration.
In summary, a functional design of a single DoF robotic knee prosthesis must mimic the complementary function of the knee joint. The knee joint stiffness is high during stance phase and low during swing phase. Moreover, the knee net mechanical power increases dramatically with ambulation speed and during terrain changes. A functional robotic knee prosthesis should not only be able to provide the nonlinear stiffness, but also should be able to produce the necessary amount of torque for all types of daily activities.
All robotic prostheses with an electrical actuator are adopting permanent magnet motors as they are more efficient and have high-torque density [4]. The DC motor was selected because it has a linear relationship between torque and current, which makes the control system simple and inherently stable [5]. From Table 1, the actuators with BLDC motors can produce higher torque. However, the BLDC motor is not optimal for the application as the motor is not in a continuous high-speed operating mode [6].
As shown in Table 1 and Table 2, most of the prototypes used a motor with 200 Watt nominal power. In recent years, more designs are using high-torque outrunner BLDC [7,8]. The new generation of powered prostheses can allow users to ambulate with a wider speed range and enhanced RoM, because the high-power motor can support the body during dorsiflexion.
Nonetheless, the utilization of BLDC motor is justifiable for the high torque-to-weight ratio. Finally, the PMSM can be potentially the best machine to develop actuators for robotic prostheses based on the motor characteristic [6].
As shown in Table 1 and Table 2, the permanent feature of all prostheses is the elastic element(s), which can help in optimizing the device power production and consumption [46]. Furthermore, the elastic element of the design reduces the shocking load in every heel-strike event. Therefore, the elastic actuators are featured consistently in the field of robotic prostheses. The actuator output is connected to the end-effector via a mechanism that amplifies the torque and generates limits on the RoM.
In Table 1 and Table 2, the consistency of the motor selection is noticeable. However, the elastic element of the actuators varies between 38 and 1200 kN/m [12,35]. The variation of the elastic elements can be attributed to the difference in working mechanisms and actuator structures. The ankle–foot stiffness is an important factor in ambulation metabolic cost [47]. In another study, it was found that the self-selected stiffness tends to increase the kinematics correlation between the biological limb and prosthetic limb with no noticeable effect on the metabolic cost [48]. Moreover, the increase in the stiffness above the self-selected stiffness led to reduction in metabolic cost and reduction in kinematic correlation [48]. This finding can answer the issue raised in [49], where the powered prosthesis’ positive mechanical work is not the only source of metabolic cost.
The vast majority of powered prostheses use linkage mechanisms, and the stability of the linkage mechanisms has been well established through analysis [50,51]. The most common linkage mechanisms are slider-crank [52] and four-bar mechanism [8]. Other designs applied multistage transmission [41,53]. The nonlinear profile and variable transmission ratio are important factors on the mechanism selection.
Table 3 shows the commonly used mechanisms in robotic prostheses design; some systems were developed with the direct drive method. In order to achieve the design requirements, high-torque outrunner motors were used with harmonic gear, which can achieve a high transmission ratio within the weight limit [8,41,42].
The most critical challenge for robotic prostheses’ mechanical systems is the high torque-to-weight ratio required to support the body weight. Therefore, a high efficiency speed reducer with compact design and high reduction ratio is needed. Some recent designs [8] use harmonic gear as the system reducer. However, the selection of the series elastic element is extremely critical to avoid damaging the gear box as a result of repetitive shock loads. Despite the success in utilizing cycloidal gear boxes in machining robots (high torque with direct contact with working environment) [62], a review of the literature illustrates the gap in robotic prostheses design [63]. Therefore, the main objective of this paper is to present a distinguished design of robotic knee prosthesis; in other words, the design is the first robotic prosthesis with a cycloidal gear drive and optimized four-bar mechanism. The optimized four-bar mechanism restricts the robotic knee prosthesis’ RoM, which provides structural safety in both passive and active operational modes.
The rest of this paper is arranged as follows: the design methodology is identified in Section 2. Robotic knee prosthesis mechanical design is provided in Section 3. Finally, the conclusion and future work are outlined in Section 4.

2. Method

Cycloid drives are compact high-ratio speed reducers with a reduction ratio around 10:1, which can have an efficiency up to 70 percent [64]. This is due to the friction at the contact points, which is the main source of losses in the cycloid drive [65]. The cycloidal gear efficiency can increase up to 90 percent if roller bearings are placed at the contact points [66]. Utilizing cycloidal gear in robotic prostheses is recommended because of its ability to be back-driven and due to the high torque-to-weight ratio required in the application [67]. A six-step guide line for performance testing is given in [68]; the method can be used to evaluate cycloid drive in high-performance robots and can be edited to assess the robotic knee prosthesis.
The basic method to generate the cycloidal profile is given in [69,70]. The cycloidal profiles (i.e., epitrochoidal and hypotrochoidal) were analyzed extensively in the literature [71]. In this work, the epitrochoidal profile was selected because the overall gear design can give good performance for different performance targets [72].
The PSO algorithm was introduced in [73], which was inspired by bird flocks searching for corn. PSO is widely used in unstructured continuous/discrete, multivariable, constrained and unconstrained optimization problems [74]. Figure 1 illustrates the original PSO algorithm, where i is the number of iterations.
The PSO was advanced and hybridized with different algorithms to enhance the converging speed and generalization ability [75,76,77]. In the past decades, the PSO algorithm was implemented to solve multivariable optimization problems and it exhibits good performance [78]. In [79], the authors used PSO to optimize four-bar linkage joint clearance. Several successful prototypes presented in the literature utilized linkage mechanisms to mimic the knee joint [80,81,82,83]. Linkage mechanisms were adapted for their ability to produce a nonlinear profile and amplify the input force [84]. To evaluate the mechanism in the proposed robotic knee prosthesis, the four-bar mechanism was evaluated based on the method presented in [85]. The robotic knee prosthesis scheme is outlined in the next section.

3. Mechanical Design for a Robotic Knee Prosthesis

In this section, an outline of the robotic knee prosthesis design and hardware assembly is presented. This section is divided into three subsections: design of cycloidal gear, kinematics analysis of four-bar mechanism, and system assembly. The system utilizes an outrunner BLDC motor because of the compact size and high torque-to-weight ratio; the motor maximum output torque is 0.94 N.m.

3.1. Cycloidal Gear for Robotic Knee Prosthesis

The cycloidal gear is based on an epicycloid, in which the original profile of the cycloid disk is given in [86]. The profile can be given in the Cartesian coordinate system, as shown in Equations (1) and (2), where d is the base circle diameter, ϕ is a free variable that takes value from 0 to 2π, e is the disc eccentricity, n is number of lobes (see Figure 2), ε is the radius of roller, and Γ is the contact angle between lobe and roller. To reduce cycloidal gear vibration, a cycloidal disc pair is designed with a 180 degree shift [87,88]. Figure 2 shows the profile of the cycloid disc with two reference circles.
x = d 2 · cos ( ϕ ) + e · cos ( ϕ · ( n + 1 ) ) ε · cos ( ϕ + Γ )
y = d 2 · sin ( ϕ ) + e · sin ( ϕ · ( n + 1 ) ) ε · sin ( ϕ + Γ )
Γ = a r c t a n ( sin ( n · ϕ ) d 2 n e + cos ( n · ϕ ) )
To assure that the system is working at its highest efficiency, the reduction ratio (i) is bound to be smaller than 20 while taking into consideration that the design’s torque requirement is around 55 N.m. The first transmission is selected to be i = 12. The maximum actuator output at this stage is approximately 10 N.m, where the cycloidal drive and motor efficiencies are 0.89 and 0.98, respectively. Figure 3 illustrates the assembled system of the BLDC motor and gear.
In Table 4, a complete list of the cycloidal gear parameters and description are provided for reference.
The cycloidal disks were reinforced by continuous carbon fiber to enhance the wear and tear at the contact point by increasing the disk stiffness. The reinforcement is highlighted in blue and illustrated in Figure 4.
To link the output shaft to the four-bar linkage input point, a timing belt with a 4:1 reduction ratio is used; the maximum input torque to the four-bar mechanism should be slightly more than 39 N.m.

3.2. Four-Bar Linkage Design and Kinematic Analysis

In this subsection, the design and optimization of the four-bar mechanism is discussed, beginning with the kinematic analysis to the optimization of the linkages, and at last, the output torque calculation.
There are many methods to study the four-bar linkage. The vector representation in complex domain is one of the most effective methods for kinematic analysis. Figure 5 shows a generalized structure of the four-bar mechanism.
r 1 + r 2 = r 3 + r 4 .  
r 1 × e j θ s + r 2 × e j θ 2 = r 3 × e j θ 3 + r 4 × e j θ 0
r 1 × e j θ s + r 2 × e j θ 2 = r 3 × e j θ 3 + r 4 × e j θ 0
By isolating the vector r 3 and multiplying Equations (5) and (6), we can find the relation between θ1 and θ2:
r 1 2 + r 2 2 r 3 2 + r b 2 + 2 × r 1 × r 2 × ( C θ s × C θ 2 S θ s × S θ 2 ) 2 × r 2 × r b × ( C θ 2 × C θ 0 S θ 2 × S θ 0 ) 2 × r 1 × r b × ( C θ s × C θ 0 S θ s × S θ 0 ) = 0  
A 1 * = r 1 2 + r 2 2 r 3 2 + r b 2  
A 2 * = 2 × r 1 × r b × ( C θ s × C θ 0 S θ s × S θ 0 )  
B 1 * = 2 × r 1 × r 2 × C θ s 2 × r 2 × r b × C θ 0  
B 2 * = 2 × r 1 × r 2 × S θ s 2 × r 2 × r b × S θ 0  
Using half-angle rules, Equation (7) is transformed into a quadratic equation. With the use of the combined variables given in Equations (8)–(11), the final equation can be written, as shown in Equation (12). The parameters of Equation (12) can be rearranged to simplify the equation by combining Equations (12)–(16).
( A 1 * + A 2 * B 1 * ) × T θ 2 / 2 2 + 2 × B 2 * × T θ 2 / 2 + A 1 * + A 2 * + B 1 * = 0  
A = A 1 * + A 2 * B 1 *  
B = 2 × B 2 *  
C = A 1 * + A 2 * + B 1 *  
x = T θ 2 / 2  
A × x 2 + B × x + C = 0  
Equation (17) can be solved to find θ2 (output angle) in terms of θs.
θ 2 = 2 × a t a n 2 ( B B 2 4 × A × C , 2 A )  
Finally, the maximum output torque is given in Equation (19). A full kinetic analysis was derived in [88]. In the early stance-phase, θ2 = 0 and θ3 = −π/2. Therefore, the maximum torque and the robotic knee prosthesis RoM can be determined by optimizing the bars’ lengths.
τ o u t , m a x = r 3 r 1 · τ i n , m a x · c o s ( θ 2 , s t a n c e θ 3 , s t a n c e π 2 )
To find the value of the four bars, an objective function is defined by Equation (18) and the target value (Equation (20)). PSO algorithm with restriction is developed to minimize the objective function. The optimization technique was run using MATLAB (2019a). Figure 6 shows the average knee joint RoM for normal walking gait.
f o b j ( r 1 , r 2 , r 3 , r b , θ s ) = K n e e r o m θ 2
Using the parameters given in Table 5, Equation (19) yields a maximum torque of 82 N.m (considering bearing efficiency is 0.95). Overall design assembly is illustrated in Figure 7.

4. Conclusions

In this paper, an overview of a robotic knee prosthesis design utilizing cycloidal gear drive and four-bar linkage mechanism is provided. The four-bar linkage mechanism is optimized using the PSO algorithm. The prosthesis developed can provide up to 82 N.m, which can support a 120 kg person; the prototype overall weight is 0.9 Kg, including two pyramid adaptors and a battery.
The current design limitation is the lack of an additional elastic element that can mimic the knee stiffness. A combination of a series of springs is a viable option to fulfill the design requirements of high stiffness during the stance phase and minimum stiffness during the swing phase. The series spiral spring(s) will be designed as we discussed in [88], and will be attached to the actuator. Furthermore, a control system should be established to have the capability to control the robotic knee prosthesis under two modes (i.e., passive and active foot).

Author Contributions

M.A.K.: conceptualization, data collection, data analysis, visualization, computer-aided design, computer-aided manufacturing, software, and original draft writing; H.A.K.: conceptualization, data collection, data analysis, computer-aided manufacturing, and manuscript review and editing; J.L.: data collection, data analysis, computer-aided design, and manuscript review and editing; T.K.: data analysis, visualization, software, and manuscript review and editing; Z.A.-H.: data analysis, visualization, software, and manuscript review and editing; H.N.S.: data collection, visualization, group administration, and manuscript review and editing; N.A.: supervision, data collection, investigation, and manuscript review and editing; N.A.A.O.: supervision, conceptualization, group administration, investigation, and manuscript review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This work is supported by the Ministry of Science, Technology, and Innovation, Malaysia, under the grant NTIS-Sandbox (grant number: NTIS 098773).

Institutional Review Board Statement

This study was granted approval from the Medical Research and Ethics Committee’s (MREC) (reference number: KKM/NIHSEC/P19-2206(11)).

Informed Consent Statement

All subjects agreed to participate in this study by signing a consent form. All subjects gave permission to publish the data collected during the trials by signing a consent form.

Data Availability Statement

All data generated during this work are available in the manuscript; additional data are available upon reasonable request from the corresponding author.

Acknowledgments

The authors acknowledge Ministry of Science, Technology, and Innovation, Malaysia (MTDC) funds for this work, under the grant NTIS-Sandbox: (grant number: NTIS 098773).

Conflicts of Interest

The authors have no conflict of interest.

References

  1. Levangie, P.K.; Norkin, C.C. Joint Structure and Function: A Comprehensive Analysis; F.A. Davis: Philadelphia, PA, USA, 2011. [Google Scholar]
  2. Hamill, J.; Knutzen, K.M. Biomechanical Basis of Human Movement; Lippincott Williams & Wilkins: Philadelphia, PA, USA, 2006. [Google Scholar]
  3. Muller, M.D. Transfemoral Amputation: Prosthetic Management. Atlas of Amputations and Limb Deficiencies, 4th ed.; American Academy of Orthopaedic Surgeons: Rosemont, IL, USA, 2016; pp. 537–554. [Google Scholar]
  4. Melfi, M.J.; Evon, S.; McElveen, R. Induction versus permanent magnet motors. IEEE Ind. Appl. Mag. 2009, 15, 28–35. [Google Scholar] [CrossRef]
  5. Hameyer, K.; Belmans, R.J. Permanent magnet excited brushed DC motors. IEEE Trans. Ind. Electron. 1996, 43, 247–255. [Google Scholar] [CrossRef]
  6. Derammelaere, S.; Haemers, M.; De Viaene, J.; Verbelen, F.; Stockman, K. A quantitative comparison between BLDC, PMSM, brushed DC and stepping motor technologies. In Proceedings of the 2016 19th International Conference on Electrical Machines and Systems (ICEMS), Chiba, Japan, 13–16 November 2016; pp. 1–5. [Google Scholar]
  7. Carney, M.E.; Shu, T.; Stolyarov, R.; Duval, J.-F.; Herr, H.M. Design and Preliminary Results of a Reaction Force Series Elastic Actuator for Bionic Knee and Ankle Prostheses. IEEE Trans. Med Robot. Bionics 2021, 3, 542–553. [Google Scholar] [CrossRef]
  8. Azocar, A.F.; Mooney, L.M.; Duval, J.-F.; Simon, A.M.; Hargrove, L.J.; Rouse, E.J. Design and clinical implementation of an open-source bionic leg. Nat. Biomed. Eng. 2020, 4, 941–953. [Google Scholar] [CrossRef]
  9. Au, S.K.; Dilworth, P.; Herr, H. An ankle-foot emulation system for the study of human walking biomechanics. In Proceedings of the 2006 IEEE International Conference on 2006, Orlando, FL, USA, 15–19 May 2006; pp. 2939–2945. [Google Scholar]
  10. Au, S.K.; Herr, H.; Weber, J.; Martinez-Villalpando, E.C. Powered ankle-foot prosthesis for the improvement of amputee ambulation. In Proceedings of the 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, 22–26 August 2007; pp. 3020–3026. [Google Scholar]
  11. Au, S.K.; Herr, H.M. Powered ankle-foot prosthesis. IEEE Robot. Autom. Mag. 2008, 15, 52–59. [Google Scholar] [CrossRef]
  12. Eilenberg, M.F.; Geyer, H.; Herr, H. Control of a Powered Ankle–Foot Prosthesis Based on a Neuromuscular Model. IEEE Trans. Neural Syst. Rehabil. Eng. 2010, 18, 164–173. [Google Scholar] [CrossRef] [PubMed]
  13. Hsieh, T.-H. Design and Control of a Two-Degree-of-Freedom Powered Ankle-Foot Prosthesis. Master’s Thesis, Massachusetts Institute of Technology, Massachusetts Ave, Cambridge, MA, USA, 2019. [Google Scholar]
  14. Hitt, J.K.; Bellman, R.; Holgate, M.; Sugar, T.G.; Hollander, K.W. The sparky (spring ankle with regenerative kinetics) project: Design and analysis of a robotic transtibial prosthesis with regenerative kinetics. In Proceedings of the International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Las Vegas, NV, USA, 4–7 September 2007; pp. 1587–1596. [Google Scholar]
  15. Holgate, M.A.; Hitt, J.K.; Bellman, R.D.; Sugar, T.G.; Hollander, K.W. The SPARKy (Spring Ankle with Regenerative kinetics) project: Choosing a DC mo-tor based actuation method. In Proceedings of the 2008 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, Scottsdale, AZ, USA, 19–22 October 2008; pp. 163–168. [Google Scholar]
  16. Zhu, J.; Wang, Q.; Wang, L. PANTOE 1: Biomechanical design of powered ankle-foot prosthesis with compliant joints and segmented foot. In Proceedings of the Advanced Intelligent Mechatronics (AIM), 2010 IEEE/ASME International Conference on 2010, Montreal, QC, Canada, 6–9 July 2010; pp. 31–36. [Google Scholar]
  17. Wang, Q.; Yuan, K.; Zhu, J.; Wang, L. Finite-state control of a robotic transtibial prosthesis with motor-driven nonlinear damping behaviors for level ground walking. In Proceedings of the Advanced Motion Control (AMC), 2014 IEEE 13th International Workshop on 2014, Yokohama, Japan, 14–16 March 2014; pp. 155–160. [Google Scholar]
  18. Wang, Q.; Yuan, K.; Zhu, J.; Wang, L. Walk the Walk: A Lightweight Active Transtibial Prosthesis. IEEE Robot. Autom. Mag. 2015, 22, 80–89. [Google Scholar] [CrossRef]
  19. Feng, Y.; Zhu, J.; Wang, Q. Metabolic cost of level-ground walking with a robotic transtibial prosthesis combining push-off power and nonlinear damping behaviors: Preliminary results. In Proceedings of the Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the 2016, Orlando, FL, USA, 16–20 August 2016; pp. 5063–5066. [Google Scholar]
  20. Shultz, A.H.; Mitchell, J.E.; Truex, D.; Lawson, B.E.; Goldfarb, M. Preliminary evaluation of a walking controller for a powered ankle prosthesis. In Proceedings of the Robotics and Automation (ICRA), 2013 IEEE International Conference on 2013, Montreal, QC, Canada, 4 October 2013; pp. 4838–4843. [Google Scholar]
  21. Shultz, A.H.; Lawson, B.E.; Goldfarb, M. Variable Cadence Walking and Ground Adaptive Standing With a Powered Ankle Prosthesis. IEEE Trans. Neural Syst. Rehabil. Eng. 2015, 24, 495–505. [Google Scholar] [CrossRef]
  22. Sun, J.; Voglewede, P.A. Controller implementation of a powered transtibial prosthetic device. In Proceedings of the ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Washington, DC, USA, 28–31 August 2011; pp. 597–603. [Google Scholar]
  23. LaPre, A.K.; Umberger, B.R.; Sup, F.C. A Robotic Ankle–Foot Prosthesis With Active Alignment. J. Med. Devices 2016, 10, 025001. [Google Scholar] [CrossRef]
  24. Cherelle, P.; Matthys, A.; Grosu, V.; Vanderborght, B.; Lefeber, D. The AMP-Foot 2.0: Mimicking intact ankle behavior with a powered transtibial prosthe-sis. In Proceedings of the Biomedical Robotics and Biomechatronics (BioRob), 2012 4th IEEE RAS & EMBS International Conference on 2012, Rome, Italy, 24–27 June 2012; pp. 544–549. [Google Scholar]
  25. Cherelle, P.; Grosu, V.; Matthys, A.; Vanderborght, B.; Lefeber, D. Design and validation of the ankle mimicking prosthetic (AMP-) foot 2.0. IEEE Trans. Neural Syst. Rehabil. Eng. 2014, 22, 138–148. [Google Scholar] [CrossRef]
  26. Cherelle, P.; Grosu, V.; Cestari, M.; Vanderborght, B.; Lefeber, D. The AMP-Foot 3, new generation propulsive prosthetic feet with explosive motion char-acteristics: Design and validation. Biomed. Eng. Online 2016, 15, 21. [Google Scholar] [CrossRef] [PubMed]
  27. Jimenez-Fabian, R.; Flynn, L.; Geeroms, J.; Vitiello, N.; VanderBorght, B.; Lefeber, D. Sliding-Bar MACCEPA for a Powered Ankle Prosthesis. J. Mech. Robot. 2015, 7, 041011. [Google Scholar] [CrossRef]
  28. Jimenez-Fabian, R.; Geeroms, J.; Flynn, L.; Vanderborght, B.; Lefeber, D. Reduction of the torque requirements of an active ankle prosthesis using a parallel spring. Robot. Auton. Syst. 2017, 92, 187–196. [Google Scholar] [CrossRef]
  29. Gao, F.; Liu, Y.; Liao, W.-H. A new powered ankle-foot prosthesis with compact parallel spring mechanism. In Proceedings of the Robotics and Biomimetics (ROBIO), 2016 IEEE International Conference on 2016, Liege, Belgium, 13–14 December 2016; pp. 473–478. [Google Scholar]
  30. Gao, F.; Liu, Y.; Liao, W.-H. Implementation and Testing of Ankle-Foot Prosthesis With a New Compensated Controller. IEEE/ASME Trans. Mechatron. 2019, 24, 1775–1784. [Google Scholar] [CrossRef]
  31. Grimmer, M.; Holgate, M.; Holgate, R.; Boehler, A.; Ward, J.; Hollander, K.; Sugar, T.; Seyfarth, A. A powered prosthetic ankle joint for walking and run-ning. Biomed. Eng. Online 2016, 15, 37–52. [Google Scholar] [CrossRef]
  32. Alleva, S.; Antonelli, M.G.; Zobel, P.B.; Durante, F. Biomechanical Design and Prototyping of a Powered Ankle-Foot Prosthesis. Materials 2020, 13, 5806. [Google Scholar] [CrossRef]
  33. Dong, D.; Ge, W.; Convens, B.; Sun, Y.; Verstraten, T.; Vanderborght, B. Design, Optimization and Energetic Evaluation of an Efficient Fully Powered An-kle-Foot Prosthesis With a Series Elastic Actuator. IEEE Access 2020, 8, 61491–61503. [Google Scholar] [CrossRef]
  34. She, H.; Zhu, J.; Tian, Y.; Wang, Y.; Huang, Q. Design of a powered ankle-foot prosthesis with an adjustable stiffness toe joint. Adv. Robot. 2020, 34, 689–697. [Google Scholar] [CrossRef]
  35. Sup, F.; Varol, H.A.; Mitchell, J.; Withrow, T.J.; Goldfarb, M. Preliminary Evaluations of a Self-Contained Anthropomorphic Transfemoral Prosthesis. IEEE/ASME Trans. Mechatron. 2009, 14, 667–676. [Google Scholar] [CrossRef]
  36. Martinez-Villalpando, E.C.; Herr, H. Agonist-antagonist active knee prosthesis: A preliminary study in level-ground walking. J. Rehabil. Res. Dev. 2009, 46, 46. [Google Scholar] [CrossRef]
  37. Rouse, E.J.; Mooney, L.M.; Martinez-Villalpando, E.C.; Herr, H.M. Clutchable series-elastic actuator: Design of a robotic knee prosthesis for minimum energy consumption. In Proceedings of the 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR), Beijing, China, 15–18 July 2013; pp. 1–6. [Google Scholar]
  38. Shultz, A.H.; Lawson, B.E.; Goldfarb, M. Running with a powered knee and ankle prosthesis. IEEE Trans. Neural Syst. Rehabil. Eng. 2014, 23, 403–412. [Google Scholar] [CrossRef] [PubMed]
  39. Rouse, E.J.; Mooney, L.M.; Herr, H.M. Clutchable series-elastic actuator: Implications for prosthetic knee design. Int. J. Robot. Res. 2014, 33, 1611–1625. [Google Scholar] [CrossRef]
  40. Zhao, H.; Reher, J.; Horn, J.; Paredes, V.; Ames, A.D. Realization of nonlinear real-time optimization based controllers on self-contained transfemoral prosthesis. In Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems, Seattle, DC, USA, 14–16 April 2015; pp. 130–138. [Google Scholar]
  41. Zhao, H.; Ambrose, E.; Ames, A.D. Preliminary results on energy efficient 3D prosthetic walking with a powered compliant transfemoral prosthesis. In Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Brussels, Belgium, 11–12 December 2017; pp. 1140–1147. [Google Scholar]
  42. Elery, T.; Rezazadeh, S.; Nesler, C.; Gregg, R.D. Design and validation of a powered knee–ankle prosthesis with high-torque, low-impedance actuators. IEEE Trans. Robot. 2020, 36, 1649–1668. [Google Scholar] [CrossRef] [PubMed]
  43. Flynn, L.; Geeroms, J.; Jimenez-Fabian, R.; Heins, S.; Vanderborght, B.; Munih, M.; Molino Lova, R.; Vitiello, N.; Lefeber, D. The challenges and achieve-ments of experimental implementation of an active transfemoral prosthesis based on biological quasi-stiffness: The CYBERLEGs beta-prosthesis. Front. Neurorobotics 2018, 12, 80. [Google Scholar] [CrossRef]
  44. Hong, W.; Paredes, V.; Chao, K.; Patrick, S.; Hur, P. Consolidated control framework to control a powered transfemoral prosthesis over inclined terrain conditions. In Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 20–24 May 2019; pp. 2838–2844. [Google Scholar]
  45. Lenzi, T.; Cempini, M.; Hargrove, L.; Kuiken, T. Design, development, and testing of a lightweight hybrid robotic knee prosthesis. Int. J. Robot. Res. 2018, 37, 953–976. [Google Scholar] [CrossRef]
  46. Liu, J.; Osman, N.A.A.; Kouzbary, M.A.; Kouzbary, H.A.; Razak, N.A.A.; Shasmin, H.N.; Arifin, N. Optimization and comparison of typical elastic actuators in powered ankle-foot prosthesis. Int. J. Control. Autom. Syst. 2022, 20, 232–242. [Google Scholar] [CrossRef]
  47. Hedrick, E.A.; Malcolm, P.; Wilken, J.M.; Takahashi, K.Z. The effects of ankle stiffness on mechanics and energetics of walking with added loads: A pros-thetic emulator study. J. Neuroeng. Rehabil. 2019, 16, 1–15. [Google Scholar] [CrossRef]
  48. Clites, T.R.; Shepherd, M.K.; Ingraham, K.A.; Wontorcik, L.; Rouse, E.J. Understanding patient preference in prosthetic ankle stiffness. J. Neuroeng. Rehabil. 2021, 18, 1–16. [Google Scholar] [CrossRef]
  49. Quesada, R.E.; Caputo, J.M.; Collins, S.H. Increasing ankle push-off work with a powered prosthesis does not necessarily reduce metabolic rate for tran-stibial amputees. J. Biomech. 2016, 49, 3452–3459. [Google Scholar] [CrossRef]
  50. Burgess, S.C. A review of linkage mechanisms in animal joints and related bio-inspired designs. Bioinspiration Biomim 2021, 16, 041001. [Google Scholar] [CrossRef]
  51. Kim, S.I.; Kim, Y.Y. Topology optimization of planar linkage mechanisms. Int. J. Numer. Methods Eng. 2014, 98, 265–286. [Google Scholar] [CrossRef]
  52. Sup, F.; Bohara, A.; Goldfarb, M. Design and Control of a Powered Transfemoral Prosthesis. Int. J. Robot. Res. 2008, 27, 263–273. [Google Scholar] [CrossRef] [PubMed]
  53. Sun, J.; Voglewede, P.A. Powered transtibial prosthetic device control system design, implementation, and bench testing. J. Med. Devices 2014, 8, 011004. [Google Scholar] [CrossRef]
  54. Sun, J.; Fritz, J.M.; Del Toro, D.R.; Voglewede, P.A. Amputee Subject Testing Protocol, Results, and Analysis of a Powered Transtibial Prosthetic Device. J. Med Devices 2014, 8, 041007–410076. [Google Scholar] [CrossRef] [PubMed]
  55. Zhao, H.; Reher, J.; Horn, J.; Paredes, V.; Ames, A.D. Realization of stair ascent and motion transitions on prostheses utilizing optimization-based control and intent recognition. In Proceedings of 2015 IEEE International Conference on Rehabilitation Robotics (ICORR), Singapore, 11–14 August 2015; pp. 265–270. [Google Scholar]
  56. Au, S.K.; Weber, J.; Herr, H. Biomechanical design of a powered ankle-foot prosthesis. In Proceedings of the 2007 IEEE 10th International Conference on Rehabilitation robotics, Noordwijk, The Netherlands, 13–15 June 2007; pp. 298–303. [Google Scholar]
  57. Ficanha, E.M.; Rastgaar, M.; Kaufman, K.R. A two-axis cable-driven ankle-foot mechanism. Robot. Biomim. 2014, 1, 379. [Google Scholar] [CrossRef]
  58. Caputo, J.M.; Collins, S.H. An experimental robotic testbed for accelerated development of ankle prostheses. In Proceedings of the 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, 6–10 May 2013; pp. 2645–2650. [Google Scholar]
  59. Caputo, J.M.; Collins, S.H. A Universal Ankle–Foot Prosthesis Emulator for Human Locomotion Experiments. J. Biomech. Eng. 2014, 136, 035002. [Google Scholar] [CrossRef]
  60. Pham, A.-D.; Ahn, H.-J. Rigid precision reducers for machining industrial robots. Int. J. Precis. Eng. Manuf. 2021, 22, 1469–1486. [Google Scholar] [CrossRef]
  61. Lara-Barrios, C.M.; Blanco-Ortega, A.; Guzmán-Valdivia, C.H.; Valles, K.D.B. Literature review and current trends on transfemoral powered prosthetics. Adv. Robot. 2017, 32, 51–62. [Google Scholar] [CrossRef]
  62. Sensinger, J.W. Efficiency of high-sensitivity gear trains, such as cycloid drives. J. Mech. Des. 2013, 135, 071006. [Google Scholar] [CrossRef]
  63. Blagojevic, M.; Kocic, M.; Marjanovic, N.; Stojanovic, B.; Dordevic, Z.; Ivanovic, L.; Marjanovic, V. Influence of the friction on the cycloidal speed reducer efficiency. J. Balk. Tribol. Assoc. 2012, 18, 217–227. [Google Scholar]
  64. Lee, K.; Hong, S.; Oh, J.-H. Development of a Lightweight and High-efficiency Compact Cycloidal Reducer for Legged Robots. Int. J. Precis. Eng. Manuf. 2019, 21, 415–425. [Google Scholar] [CrossRef]
  65. Sensinger, J.W.; Lipsey, J.H. Cycloid vs. harmonic drives for use in high ratio, single stage robotic transmissions. In Proceedings of the 2012 IEEE International Conference on Robotics and Automation, St Paul, MN, USA, 14–18 May 2012; pp. 4130–4135. [Google Scholar]
  66. Qiu, Z.; Xue, J. Review of Performance Testing of High Precision Reducers for Industrial Robots. Measurement 2021, 183, 109794. [Google Scholar] [CrossRef]
  67. Botsiber, D.; Kingston, L. Design and performance of the cycloid speed reducer. Mach. Des. 1956, 28, 65–69. [Google Scholar]
  68. Malhotra, S.; Parameswaran, M. Analysis of a cycloid speed reducer. Mech. Mach. Theory 1983, 18, 491–499. [Google Scholar] [CrossRef]
  69. Hsieh, C.-F. Study on Geometry Design of Rotors Using Trochoidal Curve. Ph.D. Dissertation, Department of Mechanical Engineering, National Chung Cheng University, Chiayi, Taiwan, 2006. [Google Scholar]
  70. Robison, A.J.; Vacca, A. Performance comparison of epitrochoidal, hypotrochoidal, and cycloidal gerotor gear profiles. Mech. Mach. Theory 2021, 158, 104228. [Google Scholar] [CrossRef]
  71. Kennedy, J.; Eberhart, R. Particle swarm optimization. In Proceedings of the ICNN′95-International Conference on Neural Networks, Perth, WA, Australia, 27 November–1 December 1995; pp. 1942–1948. [Google Scholar]
  72. Sengupta, S.; Basak, S.; Peters, R.A. Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives. Mach. Learn. Knowl. Extr. 2018, 1, 157–191. [Google Scholar] [CrossRef]
  73. Robinson, J.; Sinton, S.; Rahmat-Samii, Y. Particle swarm, genetic algorithm, and their hybrids: Optimization of a profiled corrugated horn antenna. In Proceedings of the IEEE Antennas and Propagation Society International Symposium (IEEE Cat. No. 02CH37313), San Antonio, TX, USA, 16–21 June 2002; pp. 314–317. [Google Scholar]
  74. Abdel-Kader, R.F. Genetically improved PSO algorithm for efficient data clustering. In Proceedings of the 2010 Second International Conference on Machine Learning and Computing, Bangalore, India, 9–11 February 2010; pp. 71–75. [Google Scholar]
  75. Das, S.; Abraham, A.; Konar, A. Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives. In Advances of Computational Intelligence in Industrial Systems; Springer: Berlin/Heidelberg, Germany, 2008; Volume 116. [Google Scholar]
  76. Wang, D.; Tan, D.; Liu, L. Particle swarm optimization algorithm: An overview. Soft Comput. 2018, 22, 387–408. [Google Scholar] [CrossRef]
  77. Sardashti, A.; Daniali, H.M.; Varedi, S.M. Optimal free-defect synthesis of four-bar linkage with joint clearance using PSO algorithm. Meccanica 2013, 48, 1681–1693. [Google Scholar] [CrossRef]
  78. Sun, Y.; Ge, W.; Zheng, J.; Dong, D. Design and Evaluation of a Prosthetic Knee Joint Using the Geared Five-Bar Mechanism. IEEE Trans. Neural Syst. Rehabil. Eng. 2015, 23, 1031–1038. [Google Scholar] [CrossRef]
  79. Steele, A.G.; Hunt, A.; Etoundi, A.C. Development of a bio-inspired knee joint mechanism for a bipedal robot. In Proceedings of the Conference on Biomimetic and Biohybrid Systems, Stanford, CA, USA, 26–28 July 2017; pp. 418–427. [Google Scholar]
  80. Jin, D.; Zhang, R.; O Dimo, H.; Wang, R.; Zhang, J. Kinematic and dynamic performance of prosthetic knee joint using six-bar mechanism. J. Rehabil. Res. Dev. 2003, 40, 39. [Google Scholar] [CrossRef] [PubMed]
  81. Radcliffe, C. Four-bar linkage prosthetic knee mechanisms: Kinematics, alignment and prescription criteria. Prosthet. Orthot. Int. 1994, 18, 159–173. [Google Scholar] [CrossRef]
  82. Patek, S.N.; Nowroozi, B.; Baio, J.; Caldwell, R.L.; Summers, A.P. Linkage mechanics and power amplification of the mantis shrimp’s strike. J. Exp. Biol. 2007, 210, 3677–3688. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  83. Lin, C.-C.; Chang, W.-T. The force transmissivity index of planar linkage mechanisms. Mech. Mach. Theory 2002, 37, 1465–1485. [Google Scholar] [CrossRef]
  84. Braren, R. Cycloidal Gears. US Patent US4050331A, 27 September 1977. [Google Scholar]
  85. Heyer, J.H. Design of Silent, Miniature, High Torque Actuators; Massachusetts Institute of Technology: Cambridge, MA, USA, 1999. [Google Scholar]
  86. Lin, W.-S.; Shih, Y.-P.; Lee, J.-J. Design of a two-stage cycloidal gear reducer with tooth modifications. Mech. Mach. Theory 2014, 79, 184–197. [Google Scholar] [CrossRef]
  87. Rothenhofer, G.; Walsh, C.; Slocum, A. Transmission ratio based analysis and robust design of mechanisms. Precis. Eng. 2010, 34, 790–797. [Google Scholar] [CrossRef]
  88. Liu, J.; Abu Osman, N.A.; Al Kouzbary, M.; Al Kouzbary, H.; Razak, N.A.A.; Shasmin, H.N.; Arifin, N. Stiffness estimation of planar spiral spring based on Gaussian process regression. Sci. Rep. 2022, 12, 1–15. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Original PSO algorithm presented in a simplified flowchart.
Figure 1. Original PSO algorithm presented in a simplified flowchart.
Actuators 11 00253 g001
Figure 2. Cycloid disc profile with the important metrics highlighted: e—eccentricity, dh—hole diameters, D—reference circle diameter of the fixed ring pins, d—base circle diameter, and ① indicates a lobe.
Figure 2. Cycloid disc profile with the important metrics highlighted: e—eccentricity, dh—hole diameters, D—reference circle diameter of the fixed ring pins, d—base circle diameter, and ① indicates a lobe.
Actuators 11 00253 g002
Figure 3. Initial assembly of the cycloidal gear drive for testing and load evaluation.
Figure 3. Initial assembly of the cycloidal gear drive for testing and load evaluation.
Actuators 11 00253 g003
Figure 4. One-layer illustration of carbon-fiber reinforcement of the cycloidal disks.
Figure 4. One-layer illustration of carbon-fiber reinforcement of the cycloidal disks.
Actuators 11 00253 g004
Figure 5. A vector representation of four-bar linkage mechanism. Where the red lines represent reference geometry.
Figure 5. A vector representation of four-bar linkage mechanism. Where the red lines represent reference geometry.
Actuators 11 00253 g005
Figure 6. Knee kinematics in the sagittal plane for 50 consecutive steps of a normal subject with a self-selected walking speed. (a) The average of knee angular speed is shown in solid black and standard deviation is in gray. (b) The average of knee angular position is shown in solid red and standard deviation is in pink.
Figure 6. Knee kinematics in the sagittal plane for 50 consecutive steps of a normal subject with a self-selected walking speed. (a) The average of knee angular speed is shown in solid black and standard deviation is in gray. (b) The average of knee angular position is shown in solid red and standard deviation is in pink.
Actuators 11 00253 g006
Figure 7. Robotic knee prosthesis design assembly.
Figure 7. Robotic knee prosthesis design assembly.
Actuators 11 00253 g007
Table 1. Powered ankle–foot prosthesis actuator design parameters.
Table 1. Powered ankle–foot prosthesis actuator design parameters.
MotorMotor’s Power (Watt)Elastic Element(s)Stiffness
DC motor 150Series springFlexion 300 kN/m
Extension 600 kN/m
[9]
DC motor150Series and parallel springs1200 kN/m (series)
770 kN/m (parallel)
[10]
BLDC motor200Series and parallel springs600 kN/m (series)
630 Nm/rad (parallel)
[11]
BLDC motor200Series and parallel springs1200 Nm/rad (series)
533 Nm/rad (parallel)
[12]
BLDC motor 1400N/A-[13]
BLDC motor 1600Series spring378 kN/m[7]
DC motor150Series and
Nonlinear keel springs
32 kN/m (series)[14]
DC motor150Series spring50 kN/m (series)[15]
DC motor83Series springs500 kN/m (series)
200 kN/m (toe-spring)
[16]
BLDC motor50N/A-[17]
DC motor150N/A-[18,19]
BLDC motor200Parallel spring43 Nm/rad[20]
BLDC motor200Parallel spring240 Nm/rad[21]
DC motor150Series spring26.6 Nm/rad[22]
BLDC motor200N/A-[23]
DC motor60Series springs120 kN/m (series)
300 kN/m (toe-spring)
[24]
DC motor60Series springs60 kN/m (series)
300 kN/m (toe-spring)
[25]
BLDC motor50Series springs180 kN/m(series)
300 kN/m (toe-spring)
[26]
DC motor60Series spring132 kN/m[27]
DC motor60Series and parallel springs130 kN/m (series)
270 Nm/rad (parallel)
[28]
DC motor90Nonlinear parallel springNo information given[29,30]
BLDC motor200Series spring445 kN/m[31]
BLDC motor283Shock-absorber No information given[32]
DC motor150Series spring208 kN/m[33]
DC motor150Series springs210 kN/m
42 Nm/rad (toe)
[34]
1 outrunner.
Table 2. Transfemoral robotic prosthesis actuators.
Table 2. Transfemoral robotic prosthesis actuators.
MotorMotor’s Power (Watt)Elastic Element(s)Stiffness
BLDC motor200Series spring for ankle actuator 38 kN/m[35]
DC motor150 (extension)
60 (flexion)
Series springs160 Nm/rad
137 Nm/rad
[36]
DC motor150Series spring200 Nm/rad[37]
BLDC motor200No information-[38]
BLDC motor200Series springs385 kN/m (extension)
338 kN/m (flexion)
[39]
BLDC motor600Series spring378 kN/m[7]
BLDC motor483N/A-[40]
BLDC motor206Torsion series springs1146 Nm/rad[41]
BLDC motor400Torsion series springs600 Nm/rad[8]
BLDC motor410--[42]
BLDC motor40Series springs17–974 Nm/rad[43]
BLDC motor240--[44]
BLDC motor90Shock absorberNo information given[45]
Table 3. Mechanisms commonly used in powered prostheses.
Table 3. Mechanisms commonly used in powered prostheses.
Common MechanismAdvantagesDrawbacks
slider-crank
(1)
simple mechanism
(2)
can utilize linear actuator
(1)
nonlinear kinematics require more sensors for measurement
[12,16,18,31,35]
four-bar linkage
(1)
more parameters can be optimized to achieve enhanced performance
(2)
more stable structure
(3)
can utilize linear actuator
[8,13,54,55,56]
direct drive
(1)
can provide the most stable structure
(2)
can reduce the device size
(1)
elastic element design is critical
(2)
high-torque motor is required
[21,38,41,57]
[8]
[42]
cable-driven
(1)
design can be upgraded to two DoFs system
(2)
can increase maximum torque generation
(1)
nonlinearity and unmodeled elasticity can affect the control behavior
(2)
sacrifices the design stability
[56,57,58,59,60,61]
Table 4. Cycloidal gear design parameters.
Table 4. Cycloidal gear design parameters.
DescriptionValue
dcircle diameter of base circle50.4 mm
Dreference circle diameter of the fixed ring pins54.6 mm
Nnumber of pins 13
nnumber of lobes 12
eeccentricity4 mm
ireduction ratio n N n = 12
eroller radius 3.175 mm
dhhole diameter d h = 2   ( ε + e ) = 14.35 mm
Table 5. Four-bar mechanism optimized parameters.
Table 5. Four-bar mechanism optimized parameters.
LinkParameters
r130 mm
r256.624 mm
r366 mm
rb66.193 mm
q00.43706 rad
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Al Kouzbary, M.; Al Kouzbary, H.; Liu, J.; Khamis, T.; Al-Hashimi, Z.; Shasmin, H.N.; Arifin, N.; Abu Osman, N.A. Robotic Knee Prosthesis with Cycloidal Gear and Four-Bar Mechanism Optimized Using Particle Swarm Algorithm. Actuators 2022, 11, 253. https://doi.org/10.3390/act11090253

AMA Style

Al Kouzbary M, Al Kouzbary H, Liu J, Khamis T, Al-Hashimi Z, Shasmin HN, Arifin N, Abu Osman NA. Robotic Knee Prosthesis with Cycloidal Gear and Four-Bar Mechanism Optimized Using Particle Swarm Algorithm. Actuators. 2022; 11(9):253. https://doi.org/10.3390/act11090253

Chicago/Turabian Style

Al Kouzbary, Mouaz, Hamza Al Kouzbary, Jingjing Liu, Taha Khamis, Zaina Al-Hashimi, Hanie Nadia Shasmin, Nooranida Arifin, and Noor Azuan Abu Osman. 2022. "Robotic Knee Prosthesis with Cycloidal Gear and Four-Bar Mechanism Optimized Using Particle Swarm Algorithm" Actuators 11, no. 9: 253. https://doi.org/10.3390/act11090253

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop